System

Abstract

A system is provided.SOLUTION: A system comprising: means for simulating a change in weather at a place designated by a user using artificial intelligence that has learned moving images of video devices all over the country and weather forecast data; means for receiving moving image data captured by the user and position information; means for generating a moving image by simulating a change in weather predicted based on the moving image data; and means for providing the generated simulation moving image to the 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 forecasts are shown using numbers and icons, but it can be difficult to imagine the actual scenery, making it difficult to take specific actions based on the forecast. For example, it is difficult to make accurate decisions based on the weather forecast, such as choosing a type of umbrella or changing plans. To solve this problem, a system is needed that can link with the weather forecast and specifically simulate how the scenery of a specified location will change, allowing users to visually confirm this. [Means for solving the problem]

[0005] This invention provides a system that simulates weather changes at a location specified by a user using artificial intelligence that has learned weather forecast data and video from video devices across the country. Specifically, the system receives video data and location information shot by the user, simulates predicted weather changes based on the weather forecast data, generates a video, and provides this to the user. Furthermore, the system includes a means for compressing the video data and location information shot by the user and sending it to the artificial intelligence for efficient processing, and a means for the simulation video generation means to apply weather effects to each frame of the video data shot based on the weather forecast data, making it possible to provide the user with specific and visually easy-to-understand weather information.

[0006] "Video device" refers to a device that captures video or provides video data, and examples include surveillance cameras and live cameras.

[0007] "Weather forecast data" means information about future weather conditions in a particular location or region, including specific numerical values ​​such as temperature, humidity, and probability of precipitation.

[0008] "Artificial intelligence" refers to computer systems that use machine learning and deep learning techniques to analyze data and make predictions and classifications.

[0009] "User" refers to an individual or organization that uses the System, including a person who provides video in order to obtain weather information for a specific location.

[0010] "Location information" is data used to identify a geographic location, and is generally GPS coordinates.

[0011] "Simulation" is a technology that virtually reproduces the results under specific conditions based on actual weather conditions.

[0012] "Weather change" refers to a change in weather conditions over time, such as a change from sunny to rainy weather.

[0013] "Video data" is a digital file consisting of a series of video frames, and includes scenes captured by a user.

[0014] "Compression" is the process of transforming data using a specific algorithm to reduce its volume.

[0015] "Video generation" is a technology that creates new video data based on the above data.

[0016] "Effects" refers to the technique of adding special effects to video and audio, and in this case refers to effects used to visually express changes in weather. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The system of the present invention simulates weather changes at a location designated by a user and provides the results in a form that can be visually confirmed. An embodiment of this system will be described below.

[0039] System configuration

[0040] 1. On the user's device:

[0041] The system uses smartphones or tablets with video recording and location information acquisition capabilities. Users use these devices to record short videos of designated locations and upload them via an application or web interface.

[0042] The dedicated application has the ability to compress video and location information and send them to a server.

[0043] 2. Server:

[0044] It has the function of temporarily storing and analyzing the received video and location information. The server retrieves the corresponding weather forecast data from an external API based on the location information.

[0045] Video analysis functions are used to extract and tag key environmental features from the video (buildings, roads, sky, etc.).

[0046] Using a trained artificial intelligence model, weather changes based on weather forecast data are simulated and videos are generated.

[0047] The generated simulation video is encoded and sent to the user's device.

[0048] Program processing explanation

[0049] Video and location upload

[0050] Device:

[0051] The user launches the application and shoots a short video of a specified location. The video is then sent to the server along with the location information, which is then efficiently encoded using data compression technology.

[0052] Data processing and storage

[0053] server:

[0054] The received video and location information are temporarily stored. Based on the stored location information, the corresponding weather forecast data is retrieved from an external weather forecast API. Next, the video data is analyzed to extract and tag basic environmental features (e.g., buildings, roads, sky).

[0055] Weather change simulation

[0056] server:

[0057] Using a trained AI model, a simulation is initiated based on the acquired weather forecast data. The model takes the video analysis data as input and applies the predicted weather changes to the original video data.

[0058] The generated simulation results are reconstructed as a video and encoded in a way that provides users with visual weather forecast information.

[0059] Providing simulation results to users

[0060] server:

[0061] The encoded simulation video is sent to the user's device, where it is played back using the user's dedicated application.

[0062] Specific examples

[0063] For example, if a user wants to check the weather on their commute route, the system operates as follows.

[0064] 1. User:

[0065] The user films the route from home to the station and uploads the video and location information to a server via an application.

[0066] 2. Server:

[0067] After receiving the video and location information, the weather forecast data for the commute route is retrieved and the video data is analyzed, after which an AI model is used to simulate weather changes and generate a simulated video.

[0068] 3. Server:

[0069] The generated simulation video is encoded and sent to the user's device.

[0070] 4. Terminal:

[0071] Users can play simulated videos through the application to visually check the specific weather conditions along their commute route.

[0072] This system allows users to obtain weather information in an intuitive format, rather than just numerical values ​​and icons, making it easier for them to decide what specific actions to take.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] User: Record a short video of a designated location (e.g., in front of your home, on your commute route, etc.) using your smartphone.

[0076] How it works: Open the camera app on your smartphone and record the specified location in video recording mode.

[0077] Step 2:

[0078] User: Launch the dedicated application and open the video upload screen.

[0079] How it works: Tap the "Upload Video" button in the app and select a video file you've already taken.

[0080] Step 3:

[0081] User: Allow location sharing.

[0082] How it works: Follow the prompts in the app and turn on the option to use location information (GPS data).

[0083] Step 4:

[0084] Device: Compress selected video and location information.

[0085] What it does: Applies a video compression algorithm to optimize the video data. Adds captured GPS data to the video file.

[0086] Step 5:

[0087] Device: Sends compressed video and location information to the server.

[0088] What it does: Creates an HTTP request and sends video data and location information.

[0089] Step 6:

[0090] Server: Temporarily stores received video and location information.

[0091] What it does: Stores the received data in a database or temporary file storage.

[0092] Step 7:

[0093] Server: Based on the saved location information, send a request to an external weather forecast API to retrieve the corresponding weather forecast data.

[0094] What it does: Uses location information to retrieve weather data from a weather API.

[0095] Step 8:

[0096] Server: Analyzes video data and extracts and tags basic environmental features.

[0097] How it works: It uses computer vision techniques to analyze video frame by frame, identifying and tagging elements such as buildings, roads, and sky.

[0098] Step 9:

[0099] Server: Uses trained artificial intelligence models to simulate weather changes based on weather forecast data.

[0100] How it works: Video analytics data and weather forecast data are fed into an AI model to generate specific weather effects.

[0101] Step 10:

[0102] Server: Overlays the simulation results onto the original video to generate a new simulation video.

[0103] How it works: Using image compositing techniques, weather effects are applied to the original footage to create a consistent video.

[0104] Step 11:

[0105] Server: Encodes the generated simulation video into a specified format.

[0106] How it works: Use video encoding software to convert the simulation video to MP4 or MKV format.

[0107] Step 12:

[0108] Server: Sends the encoded simulation video to the user's device.

[0109] What it does: Uploads a video file to cloud storage and provides a download link to the user or sends it directly.

[0110] Step 13:

[0111] Terminal: Plays the received simulation video and displays it to the user.

[0112] How it works: A dedicated application plays a downloaded video file and provides the user with visualized weather information.

[0113] In this way, the user can visually check the actual scenery together with the simulated weather information.

[0114] Example 1

[0115] 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."

[0116] Conventional weather forecast systems only provide weather information using numerical values ​​and icons, making it difficult for users to intuitively understand specific weather changes. In addition, there is a lack of technology to simulate detailed weather changes in a specific location, and there is no way for users to visually check the weather at any location.

[0117] 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.

[0118] In this invention, the server includes means for using the user's terminal to shoot a short video of a location specified by the user, compressing the video and location information and transmitting the compressed video to the server, means for the server to temporarily store the received video and location information and acquire corresponding weather forecast data from an external weather forecast service, means for the server to analyze the video data, extract and tag basic environmental features, means for using a trained generative AI model to simulate weather changes based on the acquired weather forecast data and analysis results and generate a simulation video, and means for encoding the generated simulation video and transmitting it to the user's terminal, thereby enabling the user to visually and intuitively understand weather changes at the specified location.

[0119] A "user device" is an electronic device such as a smartphone or tablet that has video recording and location information acquisition functions.

[0120] The "server" is a computer system that acquires weather forecast data from external weather forecast services and stores and analyzes video and location information.

[0121] "Video" is short video data taken at a location specified by the user.

[0122] "Location information" is data that indicates the geographical location where a video was taken.

[0123] "Data compression" refers to the process of compressing data to efficiently store and transmit video and location information.

[0124] "Weather Forecast Data" means current and future weather information for a particular location obtained from an external weather forecast service.

[0125] "Video analysis" is the process of extracting and tagging basic environmental features (buildings, roads, sky, etc.) from video data.

[0126] A "generative AI model" is a pre-trained artificial intelligence model used to simulate weather changes based on weather forecast data.

[0127] "Weather change simulation" is a process that reproduces weather changes in videos taken by the user based on acquired weather forecast data and the results of video analysis.

[0128] A "simulation video" is a user-filmed video in which weather changes are applied by a generative AI model.

[0129] "Encoding" is the process of converting the simulation video into a format that can be played on the user's device.

[0130] MODE FOR CARRYING OUT THE INVENTION

[0131] The system of the present invention provides a user with a visual indication of changes in the weather at a location designated by the user. Specific embodiments for implementing this system will be described below.

[0132] composition

[0133] 1. On the user's device:

[0134] Using electronic devices such as smartphones and tablets that have video recording and location information acquisition functions, users can record short videos of designated locations and upload the videos and location information to a server using a dedicated application.

[0135] The dedicated application has the ability to compress video and location information and send it to a server.

[0136] 2. Server:

[0137] The server has the function of temporarily storing and analyzing the received video and location information. The video data and location information are stored using a database system (e.g., MySQL) and a file system.

[0138] The server retrieves weather forecast data from an external weather forecast API (e.g., OpenWeatherMap API) based on the location information.

[0139] Use video analysis modules (e.g., OpenCV or TensorFlow) to extract and tag key environmental features from the video (e.g., buildings, roads, sky, etc.).

[0140] Using a trained generative AI model (e.g., PyTorch or TensorFlow), weather changes are simulated based on the acquired weather forecast data and analysis results, and a simulation video is generated.

[0141] The generated simulation video is encoded using FFmpeg or similar and sent to the user's device.

[0142] Usage example

[0143] As a concrete example, a scenario where a user wants to check the weather on his / her commute route will be shown.

[0144] 1. User:

[0145] The user takes a video of their commute from home to the station and uploads it to a server along with their location information via a dedicated application. For example, the user can use a prompt such as, "Please simulate the weather on tomorrow's commute route."

[0146] 2. Server:

[0147] The system receives video and location information and obtains weather forecast data for the commute route from an external weather forecast API. It then analyzes the video data and extracts and tags basic environmental features (buildings, roads, sky, etc.). Based on the video analysis results, a trained generative AI model simulates weather changes and generates a simulated video.

[0148] 3. Server:

[0149] The generated simulation video is encoded and sent to the user's device.

[0150] 4. Terminal:

[0151] Users can play simulation videos through a dedicated application and visually check the specific weather conditions on their commute route.

[0152] This allows users to obtain weather information in a concrete and intuitive format, rather than just numerical values ​​and icons, which can be used to help with everyday decisions and actions.

[0153] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0154] Step 1:

[0155] Capture and upload videos and location information

[0156] Device:

[0157] Users launch the dedicated application and shoot a short video of a specified location, and the application uses the device's GPS to obtain location information.

[0158] (Input): User operation (start shooting)

[0159] (Data processing): Video recording, location information acquisition

[0160] (Output): Recorded video file, acquired location information

[0161] Specific behavior:

[0162] The user presses the "Start shooting" button within the app.

[0163] The device's camera will activate and take a picture of the specified location.

[0164] After shooting is completed, the location information is acquired and the video and location information are compressed.

[0165] The compressed data is sent to the server.

[0166] Step 2:

[0167] Receiving and storing video and location information

[0168] server:

[0169] The server temporarily stores the received video and location information. It uses a database system (e.g., MySQL) and a file system to store the video data and location information.

[0170] (Input): Compressed video file, location information

[0171] (Data processing): Data storage

[0172] (Output): Saved video files, location database

[0173] Specific behavior:

[0174] The server's API receives the send request.

[0175] Save the video file to the file system.

[0176] Record location information in a database.

[0177] Step 3:

[0178] Obtaining weather forecast data

[0179] server:

[0180] Based on the location information, the corresponding weather forecast data is obtained from an external weather forecast API (e.g., OpenWeatherMap API).

[0181] (Input): Location data

[0182] (Data processing): Sending API requests, analyzing weather forecast data

[0183] (Output): Weather forecast data

[0184] Specific behavior:

[0185] Generate and send an API request to an external weather service.

[0186] Weather forecast data is returned as a response.

[0187] The acquired data is analyzed and stored in a database.

[0188] Step 4:

[0189] Video data analysis and tagging

[0190] server:

[0191] Using a video analysis module (e.g., OpenCV or TensorFlow), key environmental features (buildings, roads, sky, etc.) from the video are extracted and tagged.

[0192] (Input): Video data

[0193] (Data processing): Video data analysis, feature extraction, tagging

[0194] (Output): Analysis results (tagged data)

[0195] Specific behavior:

[0196] Video data is divided into frames.

[0197] An object detection algorithm is applied to each frame.

[0198] Tags the detected objects and stores the results in a database.

[0199] Step 5:

[0200] Generate videos of simulated weather changes

[0201] server:

[0202] Using a trained generative AI model (e.g., PyTorch or TensorFlow), weather changes are simulated based on the acquired weather forecast data and analysis results, and a simulation video is generated.

[0203] (Input): Weather forecast data, analysis results

[0204] (Data processing): Weather simulation, video generation

[0205] (Output): Simulation video

[0206] Specific behavior:

[0207] Weather forecast data and video analysis results are input into the AI ​​model.

[0208] A generative AI model runs weather change simulations.

[0209] The simulation results are applied to the original video data to generate a simulated video.

[0210] Step 6:

[0211] Encoding and sending simulation videos

[0212] server:

[0213] The generated simulation video is encoded (for example, using FFmpeg) and sent to the user's device.

[0214] (Input): Simulation video

[0215] (Data processing): Video encoding, data transmission

[0216] (Output): Encoded video file

[0217] Specific behavior:

[0218] The generated simulation video is encoded.

[0219] Send an API request to send the encoded video file to the user's device.

[0220] Step 7:

[0221] Simulation video playback

[0222] Device:

[0223] The user then launches the dedicated application again and plays the simulation video received from the server, allowing the user to visually confirm changes in the weather.

[0224] (Input): Received simulation video

[0225] (Data processing): Video playback

[0226] (Output): Played simulation video

[0227] Specific behavior:

[0228] The application receives the notification and notifies the user that a new simulation video is available.

[0229] When the user taps the notification, the application launches and plays the video.

[0230] By performing the above steps, it becomes possible for the user to visually check weather changes in a location specified by the user in real time.

[0231] (Application example 1)

[0232] 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."

[0233] In conventional food delivery services, delivery personnel have limited means of understanding weather changes in real time, which can lead to delays and safety issues due to unexpected weather changes. In addition, intuitive simulation tools to improve the efficiency of delivery routes are lacking. To solve these issues, a weather change simulation system based on video of delivery routes is needed.

[0234] 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.

[0235] In this invention, the server includes a means for a user to shoot video along a food delivery route and simulate weather changes, a means for determining the safety and efficiency of the delivery route based on the generated simulated video, and a means for compressing the video data and location information shot by the user and transmitting them to the artificial intelligence, thereby enabling delivery personnel to visually check weather changes in real time and select a safe and efficient delivery route.

[0236] "National video equipment" refers to equipment used to capture images, such as cameras and smartphones installed in each region.

[0237] "Weather forecast data" refers to forecast information about the weather in a specified area obtained from meteorological agencies or weather APIs.

[0238] "Artificial intelligence" refers to computer programs or systems that learn from large amounts of data, recognize patterns, and make inferences.

[0239] A "user" is a person or organization that uses this system to perform weather change simulations.

[0240] "Video data" is a media file that contains a series of image frames captured by a user.

[0241] "Location information" refers to the geographic coordinate data (latitude and longitude) at the time of shooting.

[0242] "Simulation video" refers to realistic video data generated based on predicted weather changes.

[0243] "Food delivery" is a service that delivers food from stores to customers.

[0244] A "delivery route" is the route or path a delivery person takes to deliver an order.

[0245] "Safety" refers to the conditions or circumstances under which delivery personnel do not encounter accidents or dangers during delivery.

[0246] "Efficiency" is the ability or state of achieving a goal in the shortest time using the least amount of effort or resources.

[0247] "Weather effects" refers to processing and effects used to visually express weather changes in video data.

[0248] "Data compression" refers to techniques and methods for efficiently storing and transmitting large amounts of data.

[0249] The present invention provides a system that allows a user to simulate and visually check weather changes along a food delivery route. Hereinafter, an embodiment of this system will be described.

[0250] System configuration

[0251] 1. User Device

[0252] The user (delivery worker) uses a smartphone, which has video recording and location information acquisition functions. The user records a short video of their delivery route and uploads the video and location information to the server using a dedicated application. At this time, the video and location information are efficiently encoded using data compression technology.

[0253] 2. Server

[0254] The server has the following functions:

[0255] Data reception and storage: The server receives the video data and location information sent from the user's device and temporarily stores them.

[0256] Obtaining weather forecast data: Based on the saved location information, the server obtains the corresponding weather forecast data from an external weather forecast API.

[0257] Video analysis and simulation: The received video data is analyzed to extract and tag basic environmental features, and then an artificial intelligence model is used to simulate weather changes based on the acquired weather forecast data.

[0258] Generation and encoding of simulation video: Video is generated based on the results of simulating weather changes and encoded into a format that can be sent to the user's device.

[0259] 3. User-facing applications

[0260] Users can play the generated simulation video through a dedicated application, which allows them to visually check weather changes along their delivery route and ensure safe and efficient deliveries.

[0261] System Operation

[0262] Video and location upload

[0263] The user's device records the delivery route, and the video and location information are uploaded to the server via the application. The data is compressed and transmitted during upload.

[0264] Data processing and storage

[0265] The server temporarily stores the video and location information sent, and then uses the weather forecast API to obtain weather forecast data for the corresponding area based on the stored location information.

[0266] Weather change simulation

[0267] The server analyzes the video data, extracts and tags key environmental features (buildings, roads, sky, etc.), then uses an artificial intelligence model to simulate weather changes based on weather forecast data, and generates and encodes the video based on the simulation results.

[0268] Providing simulation results to users

[0269] The server sends the generated simulation video to the user's device, and the user plays the simulation video in the application to visually check the weather changes along the delivery route.

[0270] Specific examples

[0271] For example, when a delivery person checks weather changes along a designated delivery route in Shibuya Ward, the following system operations are performed.

[0272] 1. User: Takes a video of the delivery route with a smartphone and uploads the video and location information to the server via the application.

[0273] 2. Server: The server stores the received video and location information, obtains weather forecast data for Shibuya Ward, analyzes the video data, and simulates weather changes.

[0274] 3. Server: Generates video based on the simulation results, encodes it, and sends it to the user's device.

[0275] 4. User device: The user plays a simulation video on the application and checks the weather changes along the delivery route.

[0276] Prompt Sentence Examples

[0277] video_data: base64_encoded_video_data

[0278] location: 35.6586, 139.7454

[0279] weather_forecast:

[0280] timestamp: "2023-10-10T08:00:00Z"

[0281] conditions: "rain"

[0282] temperature: 15

[0283] Using this prompt, the artificial intelligence model applies the specified weather conditions to the video and generates a visually verifiable simulation video.

[0284] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0285] Step 1:

[0286] The user's device captures video of the delivery route and acquires location information. The video data and location information are efficiently encoded by the application using data compression technology, reducing the file size of the video and location information and enabling faster transmission to the server.

[0287] Input: Delivery route video data, location information

[0288] Output: Compressed video data, compressed location information

[0289] Step 2:

[0290] The user's device uploads the compressed video data and location information to the server, and the application requests the server to send the video data and location information as a pair.

[0291] Input: Compressed video data, compressed location information

[0292] Output: Compressed data stored on the server

[0293] Step 3:

[0294] The server temporarily stores the received video data and location information. Based on the stored location information, the server calls an external weather forecast API to obtain weather forecast data for the corresponding area.

[0295] Input: Compressed data stored on the server, location information

[0296] Output: Weather forecast data

[0297] Step 4:

[0298] The server analyzes the received video data and extracts and tags basic environmental features (buildings, roads, sky, etc.) Video analysis is performed using computer vision technology to recognize specific objects in each frame and assign features as tags.

[0299] Input: Saved video data

[0300] Output: Extracted environmental features (tagged data)

[0301] Step 5:

[0302] The server uses an artificial intelligence model to simulate weather changes based on weather forecast data. The simulation takes tagged data obtained through video analysis as input and applies weather effects to each frame.

[0303] Input: Weather forecast data, tagged data

[0304] Output: Frame with weather effects applied

[0305] Step 6:

[0306] The server connects the frames that simulate weather changes to generate a simulation video, which is then encoded into a format that can be played on a user device.

[0307] Input: Frame with weather effect applied

[0308] Output: Encoded simulation video

[0309] Step 7:

[0310] The server transmits the encoded simulation video to the user terminal, which receives and plays the simulation video through a dedicated application.

[0311] Input: Encoded simulation video

[0312] Output: Simulation video sent to the user's device

[0313] 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.

[0314] The system of the present invention simulates weather changes at a location specified by the user and provides them in a visually verifiable format. It also analyzes the user's emotions and adds a function to customize the simulation animation based on those emotions, providing more personalized weather information.

[0315] System configuration

[0316] 1. On the user's device:

[0317] The system uses smartphones or tablets with video recording and location information acquisition capabilities. Users use these devices to record short videos of designated locations and upload them via an application or web interface.

[0318] The dedicated application has the function of compressing video and location information and sending it to a server, and also has the function of collecting emotional data using a camera and microphone to recognize the user's emotions.

[0319] 2. Server:

[0320] It has the function of temporarily storing and analyzing the received video and location information, and obtaining weather forecast data from an external weather forecast API based on the stored location information.

[0321] Video analysis functions are used to extract and tag key environmental features from the video (buildings, roads, sky, etc.).

[0322] It has the function of using an emotion engine to analyze the user's emotional data and customize the weather simulation based on the analysis results.

[0323] Using a trained artificial intelligence model, weather changes based on weather forecast data and emotion data are simulated and videos are generated.

[0324] The generated simulation video is encoded and sent to the user's device.

[0325] Program processing explanation

[0326] Uploading videos, location information, and emotion data

[0327] Device:

[0328] Users launch the application and record a short video of a designated location. The video and location information are then encoded using data compression technology. Emotional data is also collected via the device's camera and microphone.

[0329] Data processing and storage

[0330] server:

[0331] The received video, location information, and emotion data are temporarily stored. Weather forecast data is retrieved from an external weather forecast API based on the stored location information. Next, the video footage is analyzed to extract and tag basic environmental features (buildings, roads, sky, etc.). The emotion engine is also used to analyze the emotion data and identify the user's current emotional state.

[0332] Weather change simulation

[0333] server:

[0334] Using a trained AI model, a simulation is initiated based on the acquired weather forecast data and emotional data. This model generates a simulation video based on video analysis data and weather forecast data, simultaneously adding emotional effects.

[0335] The generated simulation results are reconstructed as a video and encoded to provide users with visual weather forecast information. Furthermore, by incorporating simulation results that have effects tailored to the user's emotions, more personalized information is provided.

[0336] Providing simulation results to users

[0337] server:

[0338] The encoded simulation video is sent to the user's device, where it is played using the user's dedicated application.

[0339] Specific examples

[0340] For example, if a user wants to check the weather on their commute route, the system operates as follows.

[0341] 1. User:

[0342] The user films the commute from home to the station and uploads the video, location information, and emotional data to a server via an application.

[0343] 2. Server:

[0344] After receiving the video, location information, and emotion data, the system retrieves weather forecast data for the commute route and analyzes the video footage.Then, an AI model simulates weather changes, and an emotion engine generates a simulated video that takes into account effects based on the user's emotions.

[0345] 3. Server:

[0346] The generated simulation video is encoded and sent to the user's device.

[0347] 4. Terminal:

[0348] Users can play simulated videos through the application to visually check the specific weather conditions along their commute route. In addition, by adding information based on the user's emotions, users can understand the weather forecast more intuitively.

[0349] This system allows users to obtain weather information in an intuitive format, rather than just numerical values ​​and icons, making it easier for them to decide what specific actions to take. In addition, by combining it with an emotion engine, it is possible to provide information optimized for each individual user.

[0350] The processing flow will be explained below.

[0351] Step 1:

[0352] User: Record a short video of a designated location (e.g., in front of your home, on your commute route, etc.) using your smartphone.

[0353] How it works: Open the camera app on your smartphone and record the specified location in video recording mode.

[0354] Step 2:

[0355] User: Launch the dedicated application and open the video upload screen.

[0356] How it works: Tap the "Upload Video" button in the app and select a video file you've already taken.

[0357] Step 3:

[0358] User: Allow location sharing and start collecting emotional data.

[0359] How it works: Follow the app's prompts to allow the option to use location information (GPS data) and emotional data collection using the camera and microphone.

[0360] Step 4:

[0361] Device: Compresses selected video and location information, and collects emotional data.

[0362] How it works: It applies a video compression algorithm to optimize video data, and also analyzes the user's facial expressions and voice data obtained through the camera and microphone to extract emotional data.

[0363] Step 5:

[0364] Device: Compressed video, location information, and emotion data are sent to the server.

[0365] Behavior: Generates an HTTP request and sends video data, location information, and emotion data.

[0366] Step 6:

[0367] Server: Temporarily stores received video, location information, and emotion data.

[0368] What it does: Stores the received data in a database or temporary file storage.

[0369] Step 7:

[0370] Server: Based on the saved location information, send a request to an external weather forecast API to retrieve the corresponding weather forecast data.

[0371] What it does: Uses location information to retrieve weather data from a weather API.

[0372] Step 8:

[0373] Server: Analyzes emotion data using the emotion engine and stores the results.

[0374] Operation: The emotion engine runs, analyzes collected voice and facial expression data, and determines the user's emotional state. The analysis results are stored in a database.

[0375] Step 9:

[0376] Server: Analyzes video data and extracts and tags basic environmental features.

[0377] How it works: It uses computer vision techniques to analyze video frame by frame, identifying and tagging elements such as buildings, roads, and sky.

[0378] Step 10:

[0379] Server: Uses a trained artificial intelligence model to simulate weather changes based on weather forecast data and emotion data.

[0380] How it works: Video analysis data, weather forecast data, and emotional effects are fed into an AI model to generate specific weather effects.

[0381] Step 11:

[0382] Server: Overlays the simulation results onto the original video to generate a new simulation video.

[0383] How it works: It uses image synthesis technology to apply weather effects to the original footage, creating a consistent video, and adding visual effects that match the user's emotional state.

[0384] Step 12:

[0385] Server: Encodes the generated simulation video into a specified format.

[0386] How it works: Use video encoding software to convert the simulation video to MP4 or MKV format.

[0387] Step 13:

[0388] Server: Sends the encoded simulation video to the user's device.

[0389] What it does: Uploads a video file to cloud storage and provides a download link to the user or sends it directly.

[0390] Step 14:

[0391] Terminal: Plays the received simulation video and displays it to the user.

[0392] How it works: A dedicated application plays downloaded video files, providing users with visualized weather information and emotional effects.

[0393] This allows users to obtain weather forecast information while visually checking the actual scenery. In addition, the emotion engine customizes the video, providing more intuitive and familiar information to users.

[0394] Example 2

[0395] 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."

[0396] Conventional weather information systems simply display weather information using numerical data and icons, making it difficult for users to intuitively understand. Furthermore, they do not take into account the user's emotional state when providing information, making it difficult to provide information optimized for each individual user.

[0397] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring external weather forecast data using received video data and location information to simulate weather changes at a location specified by the user, means for analyzing the user's emotions and customizing the weather simulation based on the emotion data, and means for generating a video by simulating weather changes based on the weather forecast data and emotion data using a trained artificial intelligence model. This allows the user to obtain weather information that is visually easy to understand and individually optimized.

[0398] "Received video data" refers to digital video files uploaded to the system as videos taken by users.

[0399] "Location Information" means data collected from a user device that indicates specific geographic coordinates using GPS or other location measurement technology.

[0400] "Weather Forecast Data" means weather information for a specific region or time period obtained from an external weather forecast API or weather service.

[0401] "Emotion data" is data that represents the emotional state of the user, obtained by analyzing the user's facial expressions, voice, and the like.

[0402] An "artificial intelligence model" is a computer program that uses pre-trained machine learning algorithms to perform specific tasks.

[0403] "Weather simulation" refers to the process of using weather forecast data and other related data to predict future changes in weather conditions and generate the results in a visual format.

[0404] "Customization methods" are functions or methods for tailoring and optimizing the information and content provided based on a user's emotional data or other individual characteristics.

[0405] "Encoding" is the process of converting digital data into a particular format that allows for efficient storage or transmission.

[0406] "Weather effects" are visual effects applied to specific frames of a video that simulate real-world weather changes.

[0407] A "simulation video" is a new video file generated by applying a weather simulation to the received video data.

[0408] This invention is a system that simulates weather changes at a user-specified location and provides visually verifiable information. The system analyzes the user's emotional data and customizes the weather simulation based on that data to provide personalized information.

[0409] Specifically, the system consists of a user terminal and a server, the details of which are explained below.

[0410] Hardware and software used:

[0411] User device: A mobile device such as a smartphone or tablet. These devices have a camera, microphone, and GPS functionality, and have a dedicated application installed.

[0412] Server: A high-performance computer system equipped with an AI engine that includes machine learning models, and responsible for video analysis, weather data acquisition, emotion analysis, simulation generation, and other processes.

[0413] External API: A service for obtaining weather forecast data. Specifically, a weather data provider service is used.

[0414] Program processing:

[0415] 1. Video and emotion data collection:

[0416] The user launches the dedicated application and shoots a short video of a specified location. The application then uses the device's camera and microphone to collect emotion data. The application then encodes this data into a single data package and sends it to the server.

[0417] 2. Data Receipt and Analysis:

[0418] The server receives the data package sent by the user and analyzes the video data, location information, and emotion data. Based on the location information, it obtains the latest weather forecast data for the corresponding location from an external weather forecast API.

[0419] 3. Video and Emotion Analysis:

[0420] The server analyzes the received video data and extracts key environmental features such as buildings, roads, and the sky. It also uses an emotion analysis engine to analyze emotion data from the user's facial expressions and voice to identify the user's emotional state.

[0421] 4. Running the weather simulation:

[0422] The server uses a trained generative AI model to perform weather simulations based on weather forecast data and emotion data, generating a visually verifiable simulation video.

[0423] 5. Generate and provide simulation videos:

[0424] The server encodes the generated simulation video and sends it to the user's device, where the user can play the simulation video through a dedicated application and visually check the weather changes.

[0425] Examples:

[0426] For example, when a user wants to check the weather on his / her commute route, the specific operation is as follows.

[0427] 1. The user films their commute from home to the station and uploads the video, location information, and emotion data to the server via the application.

[0428] 2. The server receives the video, location information, and emotion data, and obtains weather forecast data for the commute route. It also analyzes the video data and extracts environmental features such as buildings and roads. The emotion analysis engine analyzes the user's emotions and generates a simulation video based on the results.

[0429] 3. The server encodes the generated simulation video and sends it to the user's device.

[0430] 4. The user plays a simulated video through the application to check the specific weather conditions along their commute route.

[0431] Example prompt sentence:

[0432] "Based on the specified location and weather forecast data, simulate the change from sunny to cloudy and create a simulation video that reflects the user's emotions as they relax."

[0433] "If the user is in a depressed state, generate a simulated video of a rainy cityscape."

[0434] In this way, the system provides weather information to users in a format that is intuitively understandable, realizing information delivery optimized for each individual user.

[0435] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0436] Step 1: Data collection

[0437] Input: Video, emotion data, and location information of the user-specified location

[0438] Processing: The user launches the dedicated application and shoots a short video of a designated location. The application also uses the device's camera and microphone to collect the user's facial expressions and voice as emotional data. This data is then encoded into a single data package within the application.

[0439] Output: Encoded data package (video, emotion data, location information)

[0440] Specific operation: The user presses the record button in the application to start recording. After recording is finished, emotion data is automatically collected and location information is attached.

[0441] Step 2: Send data

[0442] Input: Encoded data package (video, emotion data, location information)

[0443] Processing: The device compresses the encoded data package and sends it over the internet to a server.

[0444] Output: Data package sent to the server

[0445] Specific operation: The terminal automatically starts the data transmission process and sends the compressed data to the server.

[0446] Step 3: Data reception and analysis

[0447] Input: Data package sent to the server

[0448] Processing: The server unpacks the received data package and extracts the video data, emotion data, and location information. Based on the location information, it sends a request to an external weather forecast API to obtain weather forecast data for the corresponding location.

[0449] Output: Decompressed data (video, emotion data, location information), corresponding weather forecast data

[0450] Specific operation: The server unpacks the data package and uses the location information to access the weather forecast API to obtain the required data.

[0451] Step 4: Video and emotion analysis

[0452] Input: Decompressed video data, emotion data

[0453] Processing: The server uses a video analysis algorithm to analyze each frame of the video data and extract key environmental features such as buildings, roads, and sky. At the same time, it uses an emotion analysis engine to analyze the user's emotion data and determine the user's emotional state (e.g., joy, anger, sadness, or happiness).

[0454] Output: Environmental feature data, emotion judgment data

[0455] How it works: The server analyzes the video frame by frame and tags it with environmental features. The emotion analysis engine identifies the user's emotional state from their facial expressions and voice.

[0456] Step 5: Run the weather simulation

[0457] Input: Environmental feature data, emotion judgment data, weather forecast data

[0458] Processing: The server uses a trained generative AI model to simulate weather changes based on the acquired weather forecast data and emotion judgment data. The generative AI model takes these data as inputs and generates a visually easy-to-understand weather simulation video.

[0459] Output: Simulation video data

[0460] How it works: The server inputs data into the AI ​​model and runs a simulation process based on weather and emotions.

[0461] Step 6: Generate and provide simulation videos

[0462] Input: Simulation video data

[0463] Processing: The server encodes the generated simulation video and sends it to the user's device.

[0464] Output: Simulation video link sent to user's device

[0465] What happens: The server encodes the video into the appropriate format and sends a download link to the user's device.

[0466] Step 7: Play the video

[0467] Input: Simulation video link sent to user's device

[0468] Processing: The user plays the received simulation video using a dedicated application. The application streams or downloads the video file and plays it.

[0469] Output: Visual weather simulation video

[0470] Specific operation: Users can intuitively check weather changes by pressing the play button within the application and watching a simulation video.

[0471] (Application example 2)

[0472] 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."

[0473] In today's food delivery industry, delivery partners are vulnerable to real-time weather changes. Delivering in bad weather can increase stress and negatively impact delivery efficiency. Traditional weather forecasts alone are insufficient to deal with these situations, and adequate support for delivery partners is lacking. Therefore, there is a need for a system that provides visual real-time weather information along delivery routes and customized information tailored to delivery partners' emotions.

[0474] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0475] In this invention, the server includes means for simulating weather changes at a location specified by a user using artificial intelligence that has learned video from video devices nationwide and weather forecast data, means for receiving video data and location information taken by the user, means for simulating predicted weather changes based on the video data and generating a video, means for analyzing user emotion data and customizing the simulation video based on the analyzed data, and means for providing the generated simulation video to the user. This makes it possible to visually simulate real-time weather changes along a delivery route and provide customized information according to the delivery partner's emotions.

[0476] "Nationwide video equipment" refers to video cameras and fixed cameras installed in various locations, and includes video data acquired from these devices.

[0477] "Weather forecast data" is data intended for weather forecasting, and includes information such as temperature, humidity, precipitation, wind speed, and wind direction.

[0478] "Artificial intelligence" refers to computer systems that use techniques such as machine learning and data analysis to perform specific tasks.

[0479] "Means for simulating weather changes" refers to a device or program that virtually reproduces weather fluctuations based on acquired data.

[0480] "Location information" is data that indicates the physical location of a target object or person, and includes GPS data.

[0481] "Video data" refers to visual information consisting of a series of image frames.

[0482] "Predicted weather changes" means future weather conditions calculated based on acquired weather forecast data.

[0483] "Means for generating" refers to a device or program that creates new data or objects based on some data.

[0484] "Simulation video" refers to a video clip that visualizes predicted weather changes.

[0485] "User emotion data" refers to data that indicates the user's feelings and mental state, obtained through voice analysis, facial expression analysis, and the like.

[0486] "Means for analyzing" refers to a device or program that takes data and extracts useful information from it.

[0487] "Customization means" refers to a device or program that tailors information or functionality to a user's individual needs or circumstances.

[0488] "Means for providing" refers to a device or program that distributes or delivers the generated data or information to the user.

[0489] "Delivery Route" means the route taken by a Delivery Partner when delivering Products.

[0490] "Real-time weather information" refers to data that provides immediate information about current weather conditions.

[0491] "Customized Information" means information that is tailored to a user's particular circumstances and requirements.

[0492] A "delivery partner" refers to a person whose role is to deliver goods to customers in services such as food delivery.

[0493] The system of the present invention enables delivery partners in the food delivery industry to visually understand weather changes in real time and provides information customized to the delivery partner's emotions. This system is realized using smartphones, servers, and artificial intelligence models.

[0494] First, delivery partners use their smartphones to record short videos of their designated delivery route. The recorded video and location information are encoded using data compression technology within the smartphone and sent to a server. At the same time, emotion data collected through the smartphone's camera and microphone is also sent to the server.

[0495] The server temporarily stores the received video data, location information, and emotion data. Based on the location information, it obtains weather forecast data from an external weather forecast API, analyzes the video footage data to extract and tag basic environmental features (e.g., buildings, roads, sky), and uses an emotion engine to analyze the emotion data and identify the delivery partner's current emotional state.

[0496] The server then uses the trained AI model to simulate weather changes based on the acquired weather forecast data and emotion data, generating a simulation video. This simulation incorporates weather effects based on the weather forecast data and effects based on the emotion data. The generated simulation video is encoded and sent to the delivery partner's smartphone.

[0497] Delivery partners can view simulated videos on their smartphones, allowing them to visually check real-time weather changes along their delivery route. The app also provides personalized information based on their emotions, reducing stress and allowing them to carry out their work with peace of mind.

[0498] As a specific example, a delivery partner takes a video of their designated delivery route and uploads the video, location information, and emotional data to a server via an application. Based on this data, the server obtains weather forecast data and performs video and emotional analysis. After that, an AI model simulates weather changes and generates a simulated video that also incorporates emotional effects. This simulated video is sent to the delivery partner and played back via the application.

[0499] Examples of prompts include:

[0500] "Generate a weather simulation from Tokyo Station to Shinagawa Station."

[0501] "Include an encouraging message if your delivery partner is feeling stressed."

[0502] In this way, the system of the present invention provides delivery partners with real-time visual weather information and emotionally customized information, enabling safer and more efficient deliveries.

[0503] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0504] Step 1:

[0505] Shooting and uploading video, location information, and emotional data

[0506] How it works: The user (delivery partner) uses their smartphone to record a short video of their designated delivery route.

[0507] Input: Video captured by a smartphone camera and location information obtained from the smartphone's GPS.

[0508] Output: Video data, location information, and emotional data (user facial expressions and voice data collected by the camera and microphone). This data is compressed and sent to the server.

[0509] Step 2:

[0510] Receiving and storing data

[0511] Operation: The server receives data sent from the user terminal.

[0512] Input: Compressed video data, location information, and emotion data.

[0513] Output: Video data, location information, and emotion data stored on the server.

[0514] Step 3:

[0515] Obtaining weather forecast data

[0516] How it works: The server retrieves weather forecast data from an external weather API based on the saved location information.

[0517] Input: Location.

[0518] Output: Weather forecast data (e.g. temperature, humidity, precipitation, etc.).

[0519] Step 4:

[0520] Video data analysis

[0521] How it works: The server analyzes the video data, extracts and tags basic features of the environment (buildings, roads, sky, etc.).

[0522] Input: Video data.

[0523] Output: Tagged environment feature data.

[0524] Step 5:

[0525] Emotional Data Analysis

[0526] How it works: The server's emotion engine is used to parse the emotion data and identify the user's current emotional state.

[0527] Input: Emotion data.

[0528] Output: Sentiment analysis result (e.g. whether the user is stressed or relaxed).

[0529] Step 6:

[0530] Weather change simulation and video generation

[0531] Operation: Using the server's trained AI model, weather changes are simulated based on the acquired weather forecast data and emotion data, and a simulation video is generated.

[0532] Input: Weather forecast data, environmental feature data, and sentiment analysis results.

[0533] Output: Simulation video (with weather and emotion effects).

[0534] Step 7:

[0535] Encoding and sending simulation videos

[0536] How it works: The server encodes the generated simulation video and sends it to the user's smartphone.

[0537] Input: Simulation video.

[0538] Output: Encoded simulation video.

[0539] Step 8:

[0540] Simulation video playback

[0541] How it works: Users play the simulation video received through a smartphone app and visually check the weather changes along their delivery route.

[0542] Input: Encoded simulation video.

[0543] Output: Simulation video to be played and customization information according to emotions.

[0544] 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.

[0545] 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.

[0546] 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.

[0547] [Second embodiment]

[0548] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0549] 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.

[0550] 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).

[0551] 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.

[0552] 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.

[0553] 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).

[0554] 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.

[0555] 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.

[0556] 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.

[0557] 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.

[0558] 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.

[0559] 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."

[0560] The system of the present invention simulates weather changes at a location designated by a user and provides the results in a form that can be visually confirmed. An embodiment of this system will be described below.

[0561] System configuration

[0562] 1. On the user's device:

[0563] The system uses smartphones or tablets with video recording and location information acquisition capabilities. Users use these devices to record short videos of designated locations and upload them via an application or web interface.

[0564] The dedicated application has the ability to compress video and location information and send them to a server.

[0565] 2. Server:

[0566] It has the function of temporarily storing and analyzing the received video and location information. The server retrieves the corresponding weather forecast data from an external API based on the location information.

[0567] Video analysis functions are used to extract and tag key environmental features from the video (buildings, roads, sky, etc.).

[0568] Using a trained artificial intelligence model, weather changes based on weather forecast data are simulated and videos are generated.

[0569] The generated simulation video is encoded and sent to the user's device.

[0570] Program processing explanation

[0571] Video and location upload

[0572] Device:

[0573] The user launches the application and shoots a short video of a specified location. The video is then sent to the server along with the location information, which is then efficiently encoded using data compression technology.

[0574] Data processing and storage

[0575] server:

[0576] The received video and location information are temporarily stored. Based on the stored location information, the corresponding weather forecast data is retrieved from an external weather forecast API. Next, the video data is analyzed to extract and tag basic environmental features (e.g., buildings, roads, sky).

[0577] Weather change simulation

[0578] server:

[0579] Using a trained AI model, a simulation is initiated based on the acquired weather forecast data. The model takes the video analysis data as input and applies the predicted weather changes to the original video data.

[0580] The generated simulation results are reconstructed as a video and encoded in a way that provides users with visual weather forecast information.

[0581] Providing simulation results to users

[0582] server:

[0583] The encoded simulation video is sent to the user's device, where it is played back using the user's dedicated application.

[0584] Specific examples

[0585] For example, if a user wants to check the weather on their commute route, the system operates as follows.

[0586] 1. User:

[0587] The user films the route from home to the station and uploads the video and location information to a server via an application.

[0588] 2. Server:

[0589] After receiving the video and location information, the weather forecast data for the commute route is retrieved and the video data is analyzed, after which an AI model is used to simulate weather changes and generate a simulated video.

[0590] 3. Server:

[0591] The generated simulation video is encoded and sent to the user's device.

[0592] 4. Terminal:

[0593] Users can play simulated videos through the application to visually check the specific weather conditions along their commute route.

[0594] This system allows users to obtain weather information in an intuitive format, rather than just numerical values ​​and icons, making it easier for them to decide what specific actions to take.

[0595] The processing flow will be explained below.

[0596] Step 1:

[0597] User: Record a short video of a designated location (e.g., in front of your home, on your commute route, etc.) using your smartphone.

[0598] How it works: Open the camera app on your smartphone and record the specified location in video recording mode.

[0599] Step 2:

[0600] User: Launch the dedicated application and open the video upload screen.

[0601] How it works: Tap the "Upload Video" button in the app and select a video file you've already taken.

[0602] Step 3:

[0603] User: Allow location sharing.

[0604] How it works: Follow the prompts in the app and turn on the option to use location information (GPS data).

[0605] Step 4:

[0606] Device: Compress selected video and location information.

[0607] What it does: Applies a video compression algorithm to optimize the video data. Adds captured GPS data to the video file.

[0608] Step 5:

[0609] Device: Sends compressed video and location information to the server.

[0610] What it does: Creates an HTTP request and sends video data and location information.

[0611] Step 6:

[0612] Server: Temporarily stores received video and location information.

[0613] What it does: Stores the received data in a database or temporary file storage.

[0614] Step 7:

[0615] Server: Based on the saved location information, send a request to an external weather forecast API to retrieve the corresponding weather forecast data.

[0616] What it does: Uses location information to retrieve weather data from a weather API.

[0617] Step 8:

[0618] Server: Analyzes video data and extracts and tags basic environmental features.

[0619] How it works: It uses computer vision techniques to analyze video frame by frame, identifying and tagging elements such as buildings, roads, and sky.

[0620] Step 9:

[0621] Server: Uses trained artificial intelligence models to simulate weather changes based on weather forecast data.

[0622] How it works: Video analytics data and weather forecast data are fed into an AI model to generate specific weather effects.

[0623] Step 10:

[0624] Server: Overlays the simulation results onto the original video to generate a new simulation video.

[0625] How it works: Using image compositing techniques, weather effects are applied to the original footage to create a consistent video.

[0626] Step 11:

[0627] Server: Encodes the generated simulation video into a specified format.

[0628] How it works: Use video encoding software to convert the simulation video to MP4 or MKV format.

[0629] Step 12:

[0630] Server: Sends the encoded simulation video to the user's device.

[0631] What it does: Uploads a video file to cloud storage and provides a download link to the user or sends it directly.

[0632] Step 13:

[0633] Terminal: Plays the received simulation video and displays it to the user.

[0634] How it works: A dedicated application plays a downloaded video file and provides the user with visualized weather information.

[0635] In this way, the user can visually check the actual scenery together with the simulated weather information.

[0636] Example 1

[0637] 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."

[0638] Conventional weather forecast systems only provide weather information using numerical values ​​and icons, making it difficult for users to intuitively understand specific weather changes. In addition, there is a lack of technology to simulate detailed weather changes in a specific location, and there is no way for users to visually check the weather at any location.

[0639] 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.

[0640] In this invention, the server includes means for using the user's terminal to shoot a short video of a location specified by the user, compressing the video and location information and transmitting the compressed video to the server, means for the server to temporarily store the received video and location information and acquire corresponding weather forecast data from an external weather forecast service, means for the server to analyze the video data, extract and tag basic environmental features, means for using a trained generative AI model to simulate weather changes based on the acquired weather forecast data and analysis results and generate a simulation video, and means for encoding the generated simulation video and transmitting it to the user's terminal, thereby enabling the user to visually and intuitively understand weather changes at the specified location.

[0641] A "user device" is an electronic device such as a smartphone or tablet that has video recording and location information acquisition functions.

[0642] The "server" is a computer system that acquires weather forecast data from external weather forecast services and stores and analyzes video and location information.

[0643] "Video" is short video data taken at a location specified by the user.

[0644] "Location information" is data that indicates the geographical location where a video was taken.

[0645] "Data compression" refers to the process of compressing data to efficiently store and transmit video and location information.

[0646] "Weather Forecast Data" means current and future weather information for a particular location obtained from an external weather forecast service.

[0647] "Video analysis" is the process of extracting and tagging basic environmental features (buildings, roads, sky, etc.) from video data.

[0648] A "generative AI model" is a pre-trained artificial intelligence model used to simulate weather changes based on weather forecast data.

[0649] "Weather change simulation" is a process that reproduces weather changes in videos taken by the user based on acquired weather forecast data and the results of video analysis.

[0650] A "simulation video" is a user-filmed video in which weather changes are applied by a generative AI model.

[0651] "Encoding" is the process of converting the simulation video into a format that can be played on the user's device.

[0652] MODE FOR CARRYING OUT THE INVENTION

[0653] The system of the present invention provides a user with a visual indication of changes in the weather at a location designated by the user. Specific embodiments for implementing this system will be described below.

[0654] composition

[0655] 1. On the user's device:

[0656] Using electronic devices such as smartphones and tablets that have video recording and location information acquisition functions, users can record short videos of designated locations and upload the videos and location information to a server using a dedicated application.

[0657] The dedicated application has the ability to compress video and location information and send it to a server.

[0658] 2. Server:

[0659] The server has the function of temporarily storing and analyzing the received video and location information. The video data and location information are stored using a database system (e.g., MySQL) and a file system.

[0660] The server retrieves weather forecast data from an external weather forecast API (e.g., OpenWeatherMap API) based on the location information.

[0661] Use video analysis modules (e.g., OpenCV or TensorFlow) to extract and tag key environmental features from the video (e.g., buildings, roads, sky, etc.).

[0662] Using a trained generative AI model (e.g., PyTorch or TensorFlow), weather changes are simulated based on the acquired weather forecast data and analysis results, and a simulation video is generated.

[0663] The generated simulation video is encoded using FFmpeg or similar and sent to the user's device.

[0664] Usage example

[0665] As a concrete example, a scenario where a user wants to check the weather on his / her commute route will be shown.

[0666] 1. User:

[0667] The user takes a video of their commute from home to the station and uploads it to a server along with their location information via a dedicated application. For example, the user can use a prompt such as, "Please simulate the weather on tomorrow's commute route."

[0668] 2. Server:

[0669] The system receives video and location information and obtains weather forecast data for the commute route from an external weather forecast API. It then analyzes the video data and extracts and tags basic environmental features (buildings, roads, sky, etc.). Based on the video analysis results, a trained generative AI model simulates weather changes and generates a simulated video.

[0670] 3. Server:

[0671] The generated simulation video is encoded and sent to the user's device.

[0672] 4. Terminal:

[0673] Users can play simulation videos through a dedicated application and visually check the specific weather conditions on their commute route.

[0674] This allows users to obtain weather information in a concrete and intuitive format, rather than just numerical values ​​and icons, which can be used to help with everyday decisions and actions.

[0675] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0676] Step 1:

[0677] Capture and upload videos and location information

[0678] Device:

[0679] Users launch the dedicated application and shoot a short video of a specified location, and the application uses the device's GPS to obtain location information.

[0680] (Input): User operation (start shooting)

[0681] (Data processing): Video recording, location information acquisition

[0682] (Output): Recorded video file, acquired location information

[0683] Specific behavior:

[0684] The user presses the "Start shooting" button within the app.

[0685] The device's camera will activate and take a picture of the specified location.

[0686] After shooting is completed, the location information is acquired and the video and location information are compressed.

[0687] The compressed data is sent to the server.

[0688] Step 2:

[0689] Receiving and storing video and location information

[0690] server:

[0691] The server temporarily stores the received video and location information. It uses a database system (e.g., MySQL) and a file system to store the video data and location information.

[0692] (Input): Compressed video file, location information

[0693] (Data processing): Data storage

[0694] (Output): Saved video files, location database

[0695] Specific behavior:

[0696] The server's API receives the send request.

[0697] Save the video file to the file system.

[0698] Record location information in a database.

[0699] Step 3:

[0700] Obtaining weather forecast data

[0701] server:

[0702] Based on the location information, the corresponding weather forecast data is obtained from an external weather forecast API (e.g., OpenWeatherMap API).

[0703] (Input): Location data

[0704] (Data processing): Sending API requests, analyzing weather forecast data

[0705] (Output): Weather forecast data

[0706] Specific behavior:

[0707] Generate and send an API request to an external weather service.

[0708] Weather forecast data is returned as a response.

[0709] The acquired data is analyzed and stored in a database.

[0710] Step 4:

[0711] Video data analysis and tagging

[0712] server:

[0713] Using a video analysis module (e.g., OpenCV or TensorFlow), key environmental features (buildings, roads, sky, etc.) from the video are extracted and tagged.

[0714] (Input): Video data

[0715] (Data processing): Video data analysis, feature extraction, tagging

[0716] (Output): Analysis results (tagged data)

[0717] Specific behavior:

[0718] Video data is divided into frames.

[0719] An object detection algorithm is applied to each frame.

[0720] Tags the detected objects and stores the results in a database.

[0721] Step 5:

[0722] Generate videos of simulated weather changes

[0723] server:

[0724] Using a trained generative AI model (e.g., PyTorch or TensorFlow), weather changes are simulated based on the acquired weather forecast data and analysis results, and a simulation video is generated.

[0725] (Input): Weather forecast data, analysis results

[0726] (Data processing): Weather simulation, video generation

[0727] (Output): Simulation video

[0728] Specific behavior:

[0729] Weather forecast data and video analysis results are input into the AI ​​model.

[0730] A generative AI model runs weather change simulations.

[0731] The simulation results are applied to the original video data to generate a simulated video.

[0732] Step 6:

[0733] Encoding and sending simulation videos

[0734] server:

[0735] The generated simulation video is encoded (for example, using FFmpeg) and sent to the user's device.

[0736] (Input): Simulation video

[0737] (Data processing): Video encoding, data transmission

[0738] (Output): Encoded video file

[0739] Specific behavior:

[0740] The generated simulation video is encoded.

[0741] Send an API request to send the encoded video file to the user's device.

[0742] Step 7:

[0743] Simulation video playback

[0744] Device:

[0745] The user then launches the dedicated application again and plays the simulation video received from the server, allowing the user to visually confirm changes in the weather.

[0746] (Input): Received simulation video

[0747] (Data processing): Video playback

[0748] (Output): Played simulation video

[0749] Specific behavior:

[0750] The application receives the notification and notifies the user that a new simulation video is available.

[0751] When the user taps the notification, the application launches and plays the video.

[0752] By performing the above steps, it becomes possible for the user to visually check weather changes in a location specified by the user in real time.

[0753] (Application example 1)

[0754] 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."

[0755] In conventional food delivery services, delivery personnel have limited means of understanding weather changes in real time, which can lead to delays and safety issues due to unexpected weather changes. In addition, intuitive simulation tools to improve the efficiency of delivery routes are lacking. To solve these issues, a weather change simulation system based on video of delivery routes is needed.

[0756] 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.

[0757] In this invention, the server includes a means for a user to shoot video along a food delivery route and simulate weather changes, a means for determining the safety and efficiency of the delivery route based on the generated simulated video, and a means for compressing the video data and location information shot by the user and transmitting them to the artificial intelligence, thereby enabling delivery personnel to visually check weather changes in real time and select a safe and efficient delivery route.

[0758] "National video equipment" refers to equipment used to capture images, such as cameras and smartphones installed in each region.

[0759] "Weather forecast data" refers to forecast information about the weather in a specified area obtained from meteorological agencies or weather APIs.

[0760] "Artificial intelligence" refers to computer programs or systems that learn from large amounts of data, recognize patterns, and make inferences.

[0761] A "user" is a person or organization that uses this system to perform weather change simulations.

[0762] "Video data" is a media file that contains a series of image frames captured by a user.

[0763] "Location information" refers to the geographic coordinate data (latitude and longitude) at the time of shooting.

[0764] "Simulation video" refers to realistic video data generated based on predicted weather changes.

[0765] "Food delivery" is a service that delivers food from stores to customers.

[0766] A "delivery route" is the route or path a delivery person takes to deliver an order.

[0767] "Safety" refers to the conditions or circumstances under which delivery personnel do not encounter accidents or dangers during delivery.

[0768] "Efficiency" is the ability or state of achieving a goal in the shortest time using the least amount of effort or resources.

[0769] "Weather effects" refers to processing and effects used to visually express weather changes in video data.

[0770] "Data compression" refers to techniques and methods for efficiently storing and transmitting large amounts of data.

[0771] The present invention provides a system that allows a user to simulate and visually check weather changes along a food delivery route. Hereinafter, an embodiment of this system will be described.

[0772] System configuration

[0773] 1. User Device

[0774] The user (delivery worker) uses a smartphone, which has video recording and location information acquisition functions. The user records a short video of their delivery route and uploads the video and location information to the server using a dedicated application. At this time, the video and location information are efficiently encoded using data compression technology.

[0775] 2. Server

[0776] The server has the following functions:

[0777] Data reception and storage: The server receives the video data and location information sent from the user's device and temporarily stores them.

[0778] Obtaining weather forecast data: Based on the saved location information, the server obtains the corresponding weather forecast data from an external weather forecast API.

[0779] Video analysis and simulation: The received video data is analyzed to extract and tag basic environmental features, and then an artificial intelligence model is used to simulate weather changes based on the acquired weather forecast data.

[0780] Generation and encoding of simulation video: Video is generated based on the results of simulating weather changes and encoded into a format that can be sent to the user's device.

[0781] 3. User-facing applications

[0782] Users can play the generated simulation video through a dedicated application, which allows them to visually check weather changes along their delivery route and ensure safe and efficient deliveries.

[0783] System Operation

[0784] Video and location upload

[0785] The user's device records the delivery route, and the video and location information are uploaded to the server via the application. The data is compressed and transmitted during upload.

[0786] Data processing and storage

[0787] The server temporarily stores the video and location information sent, and then uses the weather forecast API to obtain weather forecast data for the corresponding area based on the stored location information.

[0788] Weather change simulation

[0789] The server analyzes the video data, extracts and tags key environmental features (buildings, roads, sky, etc.), then uses an artificial intelligence model to simulate weather changes based on weather forecast data, and generates and encodes the video based on the simulation results.

[0790] Providing simulation results to users

[0791] The server sends the generated simulation video to the user's device, and the user plays the simulation video in the application to visually check the weather changes along the delivery route.

[0792] Specific examples

[0793] For example, when a delivery person checks weather changes along a designated delivery route in Shibuya Ward, the following system operations are performed.

[0794] 1. User: Takes a video of the delivery route with a smartphone and uploads the video and location information to the server via the application.

[0795] 2. Server: The server stores the received video and location information, obtains weather forecast data for Shibuya Ward, analyzes the video data, and simulates weather changes.

[0796] 3. Server: Generates video based on the simulation results, encodes it, and sends it to the user's device.

[0797] 4. User device: The user plays a simulation video on the application and checks the weather changes along the delivery route.

[0798] Prompt Sentence Examples

[0799] video_data: base64_encoded_video_data

[0800] location: 35.6586, 139.7454

[0801] weather_forecast:

[0802] timestamp: "2023-10-10T08:00:00Z"

[0803] conditions: "rain"

[0804] temperature: 15

[0805] Using this prompt, the artificial intelligence model applies the specified weather conditions to the video and generates a visually verifiable simulation video.

[0806] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0807] Step 1:

[0808] The user's device captures video of the delivery route and acquires location information. The video data and location information are efficiently encoded by the application using data compression technology, reducing the file size of the video and location information and enabling faster transmission to the server.

[0809] Input: Delivery route video data, location information

[0810] Output: Compressed video data, compressed location information

[0811] Step 2:

[0812] The user's device uploads the compressed video data and location information to the server, and the application requests the server to send the video data and location information as a pair.

[0813] Input: Compressed video data, compressed location information

[0814] Output: Compressed data stored on the server

[0815] Step 3:

[0816] The server temporarily stores the received video data and location information. Based on the stored location information, the server calls an external weather forecast API to obtain weather forecast data for the corresponding area.

[0817] Input: Compressed data stored on the server, location information

[0818] Output: Weather forecast data

[0819] Step 4:

[0820] The server analyzes the received video data and extracts and tags basic environmental features (buildings, roads, sky, etc.) Video analysis is performed using computer vision technology to recognize specific objects in each frame and assign features as tags.

[0821] Input: Saved video data

[0822] Output: Extracted environmental features (tagged data)

[0823] Step 5:

[0824] The server uses an artificial intelligence model to simulate weather changes based on weather forecast data. The simulation takes tagged data obtained through video analysis as input and applies weather effects to each frame.

[0825] Input: Weather forecast data, tagged data

[0826] Output: Frame with weather effects applied

[0827] Step 6:

[0828] The server connects the frames that simulate weather changes to generate a simulation video, which is then encoded into a format that can be played on a user device.

[0829] Input: Frame with weather effect applied

[0830] Output: Encoded simulation video

[0831] Step 7:

[0832] The server transmits the encoded simulation video to the user terminal, which receives and plays the simulation video through a dedicated application.

[0833] Input: Encoded simulation video

[0834] Output: Simulation video sent to the user's device

[0835] 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.

[0836] The system of the present invention simulates weather changes at a location specified by the user and provides them in a visually verifiable format. It also analyzes the user's emotions and adds a function to customize the simulation animation based on those emotions, providing more personalized weather information.

[0837] System configuration

[0838] 1. On the user's device:

[0839] The system uses smartphones or tablets with video recording and location information acquisition capabilities. Users use these devices to record short videos of designated locations and upload them via an application or web interface.

[0840] The dedicated application has the function of compressing video and location information and sending it to a server, and also has the function of collecting emotional data using a camera and microphone to recognize the user's emotions.

[0841] 2. Server:

[0842] It has the function of temporarily storing and analyzing the received video and location information, and obtaining weather forecast data from an external weather forecast API based on the stored location information.

[0843] Video analysis functions are used to extract and tag key environmental features from the video (buildings, roads, sky, etc.).

[0844] It has the function of using an emotion engine to analyze the user's emotional data and customize the weather simulation based on the analysis results.

[0845] Using a trained artificial intelligence model, weather changes based on weather forecast data and emotion data are simulated and videos are generated.

[0846] The generated simulation video is encoded and sent to the user's device.

[0847] Program processing explanation

[0848] Uploading videos, location information, and emotion data

[0849] Device:

[0850] Users launch the application and record a short video of a designated location. The video and location information are then encoded using data compression technology. Emotional data is also collected via the device's camera and microphone.

[0851] Data processing and storage

[0852] server:

[0853] The received video, location information, and emotion data are temporarily stored. Weather forecast data is retrieved from an external weather forecast API based on the stored location information. Next, the video footage is analyzed to extract and tag basic environmental features (buildings, roads, sky, etc.). The emotion engine is also used to analyze the emotion data and identify the user's current emotional state.

[0854] Weather change simulation

[0855] server:

[0856] Using a trained AI model, a simulation is initiated based on the acquired weather forecast data and emotional data. This model generates a simulation video based on video analysis data and weather forecast data, simultaneously adding emotional effects.

[0857] The generated simulation results are reconstructed as a video and encoded to provide users with visual weather forecast information. Furthermore, by incorporating simulation results that have effects tailored to the user's emotions, more personalized information is provided.

[0858] Providing simulation results to users

[0859] server:

[0860] The encoded simulation video is sent to the user's device, where it is played using the user's dedicated application.

[0861] Specific examples

[0862] For example, if a user wants to check the weather on their commute route, the system operates as follows.

[0863] 1. User:

[0864] The user films the commute from home to the station and uploads the video, location information, and emotional data to a server via an application.

[0865] 2. Server:

[0866] After receiving the video, location information, and emotion data, the system retrieves weather forecast data for the commute route and analyzes the video footage.Then, an AI model simulates weather changes, and an emotion engine generates a simulated video that takes into account effects based on the user's emotions.

[0867] 3. Server:

[0868] The generated simulation video is encoded and sent to the user's device.

[0869] 4. Terminal:

[0870] Users can play simulated videos through the application to visually check the specific weather conditions along their commute route. In addition, by adding information based on the user's emotions, users can understand the weather forecast more intuitively.

[0871] This system allows users to obtain weather information in an intuitive format, rather than just numerical values ​​and icons, making it easier for them to decide what specific actions to take. In addition, by combining it with an emotion engine, it is possible to provide information optimized for each individual user.

[0872] The processing flow will be explained below.

[0873] Step 1:

[0874] User: Record a short video of a designated location (e.g., in front of your home, on your commute route, etc.) using your smartphone.

[0875] How it works: Open the camera app on your smartphone and record the specified location in video recording mode.

[0876] Step 2:

[0877] User: Launch the dedicated application and open the video upload screen.

[0878] How it works: Tap the "Upload Video" button in the app and select a video file you've already taken.

[0879] Step 3:

[0880] User: Allow location sharing and start collecting emotional data.

[0881] How it works: Follow the app's prompts to allow the option to use location information (GPS data) and emotional data collection using the camera and microphone.

[0882] Step 4:

[0883] Device: Compresses selected video and location information, and collects emotional data.

[0884] How it works: It applies a video compression algorithm to optimize video data, and also analyzes the user's facial expressions and voice data obtained through the camera and microphone to extract emotional data.

[0885] Step 5:

[0886] Device: Compressed video, location information, and emotion data are sent to the server.

[0887] Behavior: Generates an HTTP request and sends video data, location information, and emotion data.

[0888] Step 6:

[0889] Server: Temporarily stores received video, location information, and emotion data.

[0890] What it does: Stores the received data in a database or temporary file storage.

[0891] Step 7:

[0892] Server: Based on the saved location information, send a request to an external weather forecast API to retrieve the corresponding weather forecast data.

[0893] What it does: Uses location information to retrieve weather data from a weather API.

[0894] Step 8:

[0895] Server: Analyzes emotion data using the emotion engine and stores the results.

[0896] Operation: The emotion engine runs, analyzes collected voice and facial expression data, and determines the user's emotional state. The analysis results are stored in a database.

[0897] Step 9:

[0898] Server: Analyzes video data and extracts and tags basic environmental features.

[0899] How it works: It uses computer vision techniques to analyze video frame by frame, identifying and tagging elements such as buildings, roads, and sky.

[0900] Step 10:

[0901] Server: Uses a trained artificial intelligence model to simulate weather changes based on weather forecast data and emotion data.

[0902] How it works: Video analysis data, weather forecast data, and emotional effects are fed into an AI model to generate specific weather effects.

[0903] Step 11:

[0904] Server: Overlays the simulation results onto the original video to generate a new simulation video.

[0905] How it works: It uses image synthesis technology to apply weather effects to the original footage, creating a consistent video, and adding visual effects that match the user's emotional state.

[0906] Step 12:

[0907] Server: Encodes the generated simulation video into a specified format.

[0908] How it works: Use video encoding software to convert the simulation video to MP4 or MKV format.

[0909] Step 13:

[0910] Server: Sends the encoded simulation video to the user's device.

[0911] What it does: Uploads a video file to cloud storage and provides a download link to the user or sends it directly.

[0912] Step 14:

[0913] Terminal: Plays the received simulation video and displays it to the user.

[0914] How it works: A dedicated application plays downloaded video files, providing users with visualized weather information and emotional effects.

[0915] This allows users to obtain weather forecast information while visually checking the actual scenery. In addition, the emotion engine customizes the video, providing more intuitive and familiar information to users.

[0916] Example 2

[0917] 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."

[0918] Conventional weather information systems simply display weather information using numerical data and icons, making it difficult for users to intuitively understand. Furthermore, they do not take into account the user's emotional state when providing information, making it difficult to provide information optimized for each individual user.

[0919] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring external weather forecast data using received video data and location information to simulate weather changes at a location specified by the user, means for analyzing the user's emotions and customizing the weather simulation based on the emotion data, and means for generating a video by simulating weather changes based on the weather forecast data and emotion data using a trained artificial intelligence model. This allows the user to obtain weather information that is visually easy to understand and individually optimized.

[0920] "Received video data" refers to digital video files uploaded to the system as videos taken by users.

[0921] "Location Information" means data collected from a user device that indicates specific geographic coordinates using GPS or other location measurement technology.

[0922] "Weather Forecast Data" means weather information for a specific region or time period obtained from an external weather forecast API or weather service.

[0923] "Emotion data" is data that represents the emotional state of the user, obtained by analyzing the user's facial expressions, voice, and the like.

[0924] An "artificial intelligence model" is a computer program that uses pre-trained machine learning algorithms to perform specific tasks.

[0925] "Weather simulation" refers to the process of using weather forecast data and other related data to predict future changes in weather conditions and generate the results in a visual format.

[0926] "Customization methods" are functions or methods for tailoring and optimizing the information and content provided based on a user's emotional data or other individual characteristics.

[0927] "Encoding" is the process of converting digital data into a particular format that allows for efficient storage or transmission.

[0928] "Weather effects" are visual effects applied to specific frames of a video that simulate real-world weather changes.

[0929] A "simulation video" is a new video file generated by applying a weather simulation to the received video data.

[0930] This invention is a system that simulates weather changes at a user-specified location and provides visually verifiable information. The system analyzes the user's emotional data and customizes the weather simulation based on that data to provide personalized information.

[0931] Specifically, the system consists of a user terminal and a server, the details of which are explained below.

[0932] Hardware and software used:

[0933] User device: A mobile device such as a smartphone or tablet. These devices have a camera, microphone, and GPS functionality, and have a dedicated application installed.

[0934] Server: A high-performance computer system equipped with an AI engine that includes machine learning models, and responsible for video analysis, weather data acquisition, emotion analysis, simulation generation, and other processes.

[0935] External API: A service for obtaining weather forecast data. Specifically, a weather data provider service is used.

[0936] Program processing:

[0937] 1. Video and emotion data collection:

[0938] The user launches the dedicated application and shoots a short video of a specified location. The application then uses the device's camera and microphone to collect emotion data. The application then encodes this data into a single data package and sends it to the server.

[0939] 2. Data Receipt and Analysis:

[0940] The server receives the data package sent by the user and analyzes the video data, location information, and emotion data. Based on the location information, it obtains the latest weather forecast data for the corresponding location from an external weather forecast API.

[0941] 3. Video and Emotion Analysis:

[0942] The server analyzes the received video data and extracts key environmental features such as buildings, roads, and the sky. It also uses an emotion analysis engine to analyze emotion data from the user's facial expressions and voice to identify the user's emotional state.

[0943] 4. Running the weather simulation:

[0944] The server uses a trained generative AI model to perform weather simulations based on weather forecast data and emotion data, generating a visually verifiable simulation video.

[0945] 5. Generate and provide simulation videos:

[0946] The server encodes the generated simulation video and sends it to the user's device, where the user can play the simulation video through a dedicated application and visually check the weather changes.

[0947] Examples:

[0948] For example, when a user wants to check the weather on his / her commute route, the specific operation is as follows.

[0949] 1. The user films their commute from home to the station and uploads the video, location information, and emotion data to the server via the application.

[0950] 2. The server receives the video, location information, and emotion data, and obtains weather forecast data for the commute route. It also analyzes the video data and extracts environmental features such as buildings and roads. The emotion analysis engine analyzes the user's emotions and generates a simulation video based on the results.

[0951] 3. The server encodes the generated simulation video and sends it to the user's device.

[0952] 4. The user plays a simulated video through the application to check the specific weather conditions along their commute route.

[0953] Example prompt sentence:

[0954] "Based on the specified location and weather forecast data, simulate the change from sunny to cloudy and create a simulation video that reflects the user's emotions as they relax."

[0955] "If the user is in a depressed state, generate a simulated video of a rainy cityscape."

[0956] In this way, the system provides weather information to users in a format that is intuitively understandable, realizing information delivery optimized for each individual user.

[0957] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0958] Step 1: Data collection

[0959] Input: Video, emotion data, and location information of the user-specified location

[0960] Processing: The user launches the dedicated application and shoots a short video of a designated location. The application also uses the device's camera and microphone to collect the user's facial expressions and voice as emotional data. This data is then encoded into a single data package within the application.

[0961] Output: Encoded data package (video, emotion data, location information)

[0962] Specific operation: The user presses the record button in the application to start recording. After recording is finished, emotion data is automatically collected and location information is attached.

[0963] Step 2: Send data

[0964] Input: Encoded data package (video, emotion data, location information)

[0965] Processing: The device compresses the encoded data package and sends it over the internet to a server.

[0966] Output: Data package sent to the server

[0967] Specific operation: The terminal automatically starts the data transmission process and sends the compressed data to the server.

[0968] Step 3: Data reception and analysis

[0969] Input: Data package sent to the server

[0970] Processing: The server unpacks the received data package and extracts the video data, emotion data, and location information. Based on the location information, it sends a request to an external weather forecast API to obtain weather forecast data for the corresponding location.

[0971] Output: Decompressed data (video, emotion data, location information), corresponding weather forecast data

[0972] Specific operation: The server unpacks the data package and uses the location information to access the weather forecast API to obtain the required data.

[0973] Step 4: Video and emotion analysis

[0974] Input: Decompressed video data, emotion data

[0975] Processing: The server uses a video analysis algorithm to analyze each frame of the video data and extract key environmental features such as buildings, roads, and sky. At the same time, it uses an emotion analysis engine to analyze the user's emotion data and determine the user's emotional state (e.g., joy, anger, sadness, or happiness).

[0976] Output: Environmental feature data, emotion judgment data

[0977] How it works: The server analyzes the video frame by frame and tags it with environmental features. The emotion analysis engine identifies the user's emotional state from their facial expressions and voice.

[0978] Step 5: Run the weather simulation

[0979] Input: Environmental feature data, emotion judgment data, weather forecast data

[0980] Processing: The server uses a trained generative AI model to simulate weather changes based on the acquired weather forecast data and emotion judgment data. The generative AI model takes these data as inputs and generates a visually easy-to-understand weather simulation video.

[0981] Output: Simulation video data

[0982] How it works: The server inputs data into the AI ​​model and runs a simulation process based on weather and emotions.

[0983] Step 6: Generate and provide simulation videos

[0984] Input: Simulation video data

[0985] Processing: The server encodes the generated simulation video and sends it to the user's device.

[0986] Output: Simulation video link sent to user's device

[0987] What happens: The server encodes the video into the appropriate format and sends a download link to the user's device.

[0988] Step 7: Play the video

[0989] Input: Simulation video link sent to user's device

[0990] Processing: The user plays the received simulation video using a dedicated application. The application streams or downloads the video file and plays it.

[0991] Output: Visual weather simulation video

[0992] Specific operation: Users can intuitively check weather changes by pressing the play button within the application and watching a simulation video.

[0993] (Application example 2)

[0994] 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."

[0995] In today's food delivery industry, delivery partners are vulnerable to real-time weather changes. Delivering in bad weather can increase stress and negatively impact delivery efficiency. Traditional weather forecasts alone are insufficient to deal with these situations, and adequate support for delivery partners is lacking. Therefore, there is a need for a system that provides visual real-time weather information along delivery routes and customized information tailored to delivery partners' emotions.

[0996] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0997] In this invention, the server includes means for simulating weather changes at a location specified by a user using artificial intelligence that has learned video from video devices nationwide and weather forecast data, means for receiving video data and location information taken by the user, means for simulating predicted weather changes based on the video data and generating a video, means for analyzing user emotion data and customizing the simulation video based on the analyzed data, and means for providing the generated simulation video to the user. This makes it possible to visually simulate real-time weather changes along a delivery route and provide customized information according to the delivery partner's emotions.

[0998] "Nationwide video equipment" refers to video cameras and fixed cameras installed in various locations, and includes video data acquired from these devices.

[0999] "Weather forecast data" is data intended for weather forecasting, and includes information such as temperature, humidity, precipitation, wind speed, and wind direction.

[1000] "Artificial intelligence" refers to computer systems that use techniques such as machine learning and data analysis to perform specific tasks.

[1001] "Means for simulating weather changes" refers to a device or program that virtually reproduces weather fluctuations based on acquired data.

[1002] "Location information" is data that indicates the physical location of a target object or person, and includes GPS data.

[1003] "Video data" refers to visual information consisting of a series of image frames.

[1004] "Predicted weather changes" means future weather conditions calculated based on acquired weather forecast data.

[1005] "Means for generating" refers to a device or program that creates new data or objects based on some data.

[1006] "Simulation video" refers to a video clip that visualizes predicted weather changes.

[1007] "User emotion data" refers to data that indicates the user's feelings and mental state, obtained through voice analysis, facial expression analysis, and the like.

[1008] "Means for analyzing" refers to a device or program that takes data and extracts useful information from it.

[1009] "Customization means" refers to a device or program that tailors information or functionality to a user's individual needs or circumstances.

[1010] "Means for providing" refers to a device or program that distributes or delivers the generated data or information to the user.

[1011] "Delivery Route" means the route taken by a Delivery Partner when delivering Products.

[1012] "Real-time weather information" refers to data that provides immediate information about current weather conditions.

[1013] "Customized Information" means information that is tailored to a user's particular circumstances and requirements.

[1014] A "delivery partner" refers to a person whose role is to deliver goods to customers in services such as food delivery.

[1015] The system of the present invention enables delivery partners in the food delivery industry to visually understand weather changes in real time and provides information customized to the delivery partner's emotions. This system is realized using smartphones, servers, and artificial intelligence models.

[1016] First, delivery partners use their smartphones to record short videos of their designated delivery route. The recorded video and location information are encoded using data compression technology within the smartphone and sent to a server. At the same time, emotion data collected through the smartphone's camera and microphone is also sent to the server.

[1017] The server temporarily stores the received video data, location information, and emotion data. Based on the location information, it obtains weather forecast data from an external weather forecast API, analyzes the video footage data to extract and tag basic environmental features (e.g., buildings, roads, sky), and uses an emotion engine to analyze the emotion data and identify the delivery partner's current emotional state.

[1018] The server then uses the trained AI model to simulate weather changes based on the acquired weather forecast data and emotion data, generating a simulation video. This simulation incorporates weather effects based on the weather forecast data and effects based on the emotion data. The generated simulation video is encoded and sent to the delivery partner's smartphone.

[1019] Delivery partners can view simulated videos on their smartphones, allowing them to visually check real-time weather changes along their delivery route. The app also provides personalized information based on their emotions, reducing stress and allowing them to carry out their work with peace of mind.

[1020] As a specific example, a delivery partner takes a video of their designated delivery route and uploads the video, location information, and emotional data to a server via an application. Based on this data, the server obtains weather forecast data and performs video and emotional analysis. After that, an AI model simulates weather changes and generates a simulated video that also incorporates emotional effects. This simulated video is sent to the delivery partner and played back via the application.

[1021] Examples of prompts include:

[1022] "Generate a weather simulation from Tokyo Station to Shinagawa Station."

[1023] "Include an encouraging message if your delivery partner is feeling stressed."

[1024] In this way, the system of the present invention provides delivery partners with real-time visual weather information and emotionally customized information, enabling safer and more efficient deliveries.

[1025] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1026] Step 1:

[1027] Shooting and uploading video, location information, and emotional data

[1028] How it works: The user (delivery partner) uses their smartphone to record a short video of their designated delivery route.

[1029] Input: Video captured by a smartphone camera and location information obtained from the smartphone's GPS.

[1030] Output: Video data, location information, and emotional data (user facial expressions and voice data collected by the camera and microphone). This data is compressed and sent to the server.

[1031] Step 2:

[1032] Receiving and storing data

[1033] Operation: The server receives data sent from the user terminal.

[1034] Input: Compressed video data, location information, and emotion data.

[1035] Output: Video data, location information, and emotion data stored on the server.

[1036] Step 3:

[1037] Obtaining weather forecast data

[1038] How it works: The server retrieves weather forecast data from an external weather API based on the saved location information.

[1039] Input: Location.

[1040] Output: Weather forecast data (e.g. temperature, humidity, precipitation, etc.).

[1041] Step 4:

[1042] Video data analysis

[1043] How it works: The server analyzes the video data, extracts and tags basic features of the environment (buildings, roads, sky, etc.).

[1044] Input: Video data.

[1045] Output: Tagged environment feature data.

[1046] Step 5:

[1047] Emotional Data Analysis

[1048] How it works: The server's emotion engine is used to parse the emotion data and identify the user's current emotional state.

[1049] Input: Emotion data.

[1050] Output: Sentiment analysis result (e.g. whether the user is stressed or relaxed).

[1051] Step 6:

[1052] Weather change simulation and video generation

[1053] Operation: Using the server's trained AI model, weather changes are simulated based on the acquired weather forecast data and emotion data, and a simulation video is generated.

[1054] Input: Weather forecast data, environmental feature data, and sentiment analysis results.

[1055] Output: Simulation video (with weather and emotion effects).

[1056] Step 7:

[1057] Encoding and sending simulation videos

[1058] How it works: The server encodes the generated simulation video and sends it to the user's smartphone.

[1059] Input: Simulation video.

[1060] Output: Encoded simulation video.

[1061] Step 8:

[1062] Simulation video playback

[1063] How it works: Users play the simulation video received through a smartphone app and visually check the weather changes along their delivery route.

[1064] Input: Encoded simulation video.

[1065] Output: Simulation video to be played and customization information according to emotions.

[1066] 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.

[1067] 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.

[1068] 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.

[1069] [Third embodiment]

[1070] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1071] 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.

[1072] 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).

[1073] 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.

[1074] 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.

[1075] 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).

[1076] 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.

[1077] 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.

[1078] 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.

[1079] 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.

[1080] 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.

[1081] 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."

[1082] The system of the present invention simulates weather changes at a location designated by a user and provides the results in a form that can be visually confirmed. An embodiment of this system will be described below.

[1083] System configuration

[1084] 1. On the user's device:

[1085] The system uses smartphones or tablets with video recording and location information acquisition capabilities. Users use these devices to record short videos of designated locations and upload them via an application or web interface.

[1086] The dedicated application has the ability to compress video and location information and send them to a server.

[1087] 2. Server:

[1088] It has the function of temporarily storing and analyzing the received video and location information. The server retrieves the corresponding weather forecast data from an external API based on the location information.

[1089] Video analysis functions are used to extract and tag key environmental features from the video (buildings, roads, sky, etc.).

[1090] Using a trained artificial intelligence model, weather changes based on weather forecast data are simulated and videos are generated.

[1091] The generated simulation video is encoded and sent to the user's device.

[1092] Program processing explanation

[1093] Video and location upload

[1094] Device:

[1095] The user launches the application and shoots a short video of a specified location. The video is then sent to the server along with the location information, which is then efficiently encoded using data compression technology.

[1096] Data processing and storage

[1097] server:

[1098] The received video and location information are temporarily stored. Based on the stored location information, the corresponding weather forecast data is retrieved from an external weather forecast API. Next, the video data is analyzed to extract and tag basic environmental features (e.g., buildings, roads, sky).

[1099] Weather change simulation

[1100] server:

[1101] Using a trained AI model, a simulation is initiated based on the acquired weather forecast data. The model takes the video analysis data as input and applies the predicted weather changes to the original video data.

[1102] The generated simulation results are reconstructed as a video and encoded in a way that provides users with visual weather forecast information.

[1103] Providing simulation results to users

[1104] server:

[1105] The encoded simulation video is sent to the user's device, where it is played back using the user's dedicated application.

[1106] Specific examples

[1107] For example, if a user wants to check the weather on their commute route, the system operates as follows.

[1108] 1. User:

[1109] The user films the route from home to the station and uploads the video and location information to a server via an application.

[1110] 2. Server:

[1111] After receiving the video and location information, the weather forecast data for the commute route is retrieved and the video data is analyzed, after which an AI model is used to simulate weather changes and generate a simulated video.

[1112] 3. Server:

[1113] The generated simulation video is encoded and sent to the user's device.

[1114] 4. Terminal:

[1115] Users can play simulated videos through the application to visually check the specific weather conditions along their commute route.

[1116] This system allows users to obtain weather information in an intuitive format, rather than just numerical values ​​and icons, making it easier for them to decide what specific actions to take.

[1117] The processing flow will be explained below.

[1118] Step 1:

[1119] User: Record a short video of a designated location (e.g., in front of your home, on your commute route, etc.) using your smartphone.

[1120] How it works: Open the camera app on your smartphone and record the specified location in video recording mode.

[1121] Step 2:

[1122] User: Launch the dedicated application and open the video upload screen.

[1123] How it works: Tap the "Upload Video" button in the app and select a video file you've already taken.

[1124] Step 3:

[1125] User: Allow location sharing.

[1126] How it works: Follow the prompts in the app and turn on the option to use location information (GPS data).

[1127] Step 4:

[1128] Device: Compress selected video and location information.

[1129] What it does: Applies a video compression algorithm to optimize the video data. Adds captured GPS data to the video file.

[1130] Step 5:

[1131] Device: Sends compressed video and location information to the server.

[1132] What it does: Creates an HTTP request and sends video data and location information.

[1133] Step 6:

[1134] Server: Temporarily stores received video and location information.

[1135] What it does: Stores the received data in a database or temporary file storage.

[1136] Step 7:

[1137] Server: Based on the saved location information, send a request to an external weather forecast API to retrieve the corresponding weather forecast data.

[1138] What it does: Uses location information to retrieve weather data from a weather API.

[1139] Step 8:

[1140] Server: Analyzes video data and extracts and tags basic environmental features.

[1141] How it works: It uses computer vision techniques to analyze video frame by frame, identifying and tagging elements such as buildings, roads, and sky.

[1142] Step 9:

[1143] Server: Uses trained artificial intelligence models to simulate weather changes based on weather forecast data.

[1144] How it works: Video analytics data and weather forecast data are fed into an AI model to generate specific weather effects.

[1145] Step 10:

[1146] Server: Overlays the simulation results onto the original video to generate a new simulation video.

[1147] How it works: Using image compositing techniques, weather effects are applied to the original footage to create a consistent video.

[1148] Step 11:

[1149] Server: Encodes the generated simulation video into a specified format.

[1150] How it works: Use video encoding software to convert the simulation video to MP4 or MKV format.

[1151] Step 12:

[1152] Server: Sends the encoded simulation video to the user's device.

[1153] What it does: Uploads a video file to cloud storage and provides a download link to the user or sends it directly.

[1154] Step 13:

[1155] Terminal: Plays the received simulation video and displays it to the user.

[1156] How it works: A dedicated application plays a downloaded video file and provides the user with visualized weather information.

[1157] In this way, the user can visually check the actual scenery together with the simulated weather information.

[1158] Example 1

[1159] 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."

[1160] Conventional weather forecast systems only provide weather information using numerical values ​​and icons, making it difficult for users to intuitively understand specific weather changes. In addition, there is a lack of technology to simulate detailed weather changes in a specific location, and there is no way for users to visually check the weather at any location.

[1161] 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.

[1162] In this invention, the server includes means for using the user's terminal to shoot a short video of a location specified by the user, compressing the video and location information and transmitting the compressed video to the server, means for the server to temporarily store the received video and location information and acquire corresponding weather forecast data from an external weather forecast service, means for the server to analyze the video data, extract and tag basic environmental features, means for using a trained generative AI model to simulate weather changes based on the acquired weather forecast data and analysis results and generate a simulation video, and means for encoding the generated simulation video and transmitting it to the user's terminal, thereby enabling the user to visually and intuitively understand weather changes at the specified location.

[1163] A "user device" is an electronic device such as a smartphone or tablet that has video recording and location information acquisition functions.

[1164] The "server" is a computer system that acquires weather forecast data from external weather forecast services and stores and analyzes video and location information.

[1165] "Video" is short video data taken at a location specified by the user.

[1166] "Location information" is data that indicates the geographical location where a video was taken.

[1167] "Data compression" refers to the process of compressing data to efficiently store and transmit video and location information.

[1168] "Weather Forecast Data" means current and future weather information for a particular location obtained from an external weather forecast service.

[1169] "Video analysis" is the process of extracting and tagging basic environmental features (buildings, roads, sky, etc.) from video data.

[1170] A "generative AI model" is a pre-trained artificial intelligence model used to simulate weather changes based on weather forecast data.

[1171] "Weather change simulation" is a process that reproduces weather changes in videos taken by the user based on acquired weather forecast data and the results of video analysis.

[1172] A "simulation video" is a user-filmed video in which weather changes are applied by a generative AI model.

[1173] "Encoding" is the process of converting the simulation video into a format that can be played on the user's device.

[1174] MODE FOR CARRYING OUT THE INVENTION

[1175] The system of the present invention provides a user with a visual indication of changes in the weather at a location designated by the user. Specific embodiments for implementing this system will be described below.

[1176] composition

[1177] 1. On the user's device:

[1178] Using electronic devices such as smartphones and tablets that have video recording and location information acquisition functions, users can record short videos of designated locations and upload the videos and location information to a server using a dedicated application.

[1179] The dedicated application has the ability to compress video and location information and send it to a server.

[1180] 2. Server:

[1181] The server has the function of temporarily storing and analyzing the received video and location information. The video data and location information are stored using a database system (e.g., MySQL) and a file system.

[1182] The server retrieves weather forecast data from an external weather forecast API (e.g., OpenWeatherMap API) based on the location information.

[1183] Use video analysis modules (e.g., OpenCV or TensorFlow) to extract and tag key environmental features from the video (e.g., buildings, roads, sky, etc.).

[1184] Using a trained generative AI model (e.g., PyTorch or TensorFlow), weather changes are simulated based on the acquired weather forecast data and analysis results, and a simulation video is generated.

[1185] The generated simulation video is encoded using FFmpeg or similar and sent to the user's device.

[1186] Usage example

[1187] As a concrete example, a scenario where a user wants to check the weather on his / her commute route will be shown.

[1188] 1. User:

[1189] The user takes a video of their commute from home to the station and uploads it to a server along with their location information via a dedicated application. For example, the user can use a prompt such as, "Please simulate the weather on tomorrow's commute route."

[1190] 2. Server:

[1191] The system receives video and location information and obtains weather forecast data for the commute route from an external weather forecast API. It then analyzes the video data and extracts and tags basic environmental features (buildings, roads, sky, etc.). Based on the video analysis results, a trained generative AI model simulates weather changes and generates a simulated video.

[1192] 3. Server:

[1193] The generated simulation video is encoded and sent to the user's device.

[1194] 4. Terminal:

[1195] Users can play simulation videos through a dedicated application and visually check the specific weather conditions on their commute route.

[1196] This allows users to obtain weather information in a concrete and intuitive format, rather than just numerical values ​​and icons, which can be used to help with everyday decisions and actions.

[1197] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1198] Step 1:

[1199] Capture and upload videos and location information

[1200] Device:

[1201] Users launch the dedicated application and shoot a short video of a specified location, and the application uses the device's GPS to obtain location information.

[1202] (Input): User operation (start shooting)

[1203] (Data processing): Video recording, location information acquisition

[1204] (Output): Recorded video file, acquired location information

[1205] Specific behavior:

[1206] The user presses the "Start shooting" button within the app.

[1207] The device's camera will activate and take a picture of the specified location.

[1208] After shooting is completed, the location information is acquired and the video and location information are compressed.

[1209] The compressed data is sent to the server.

[1210] Step 2:

[1211] Receiving and storing video and location information

[1212] server:

[1213] The server temporarily stores the received video and location information. It uses a database system (e.g., MySQL) and a file system to store the video data and location information.

[1214] (Input): Compressed video file, location information

[1215] (Data processing): Data storage

[1216] (Output): Saved video files, location database

[1217] Specific behavior:

[1218] The server's API receives the send request.

[1219] Save the video file to the file system.

[1220] Record location information in a database.

[1221] Step 3:

[1222] Obtaining weather forecast data

[1223] server:

[1224] Based on the location information, the corresponding weather forecast data is obtained from an external weather forecast API (e.g., OpenWeatherMap API).

[1225] (Input): Location data

[1226] (Data processing): Sending API requests, analyzing weather forecast data

[1227] (Output): Weather forecast data

[1228] Specific behavior:

[1229] Generate and send an API request to an external weather service.

[1230] Weather forecast data is returned as a response.

[1231] The acquired data is analyzed and stored in a database.

[1232] Step 4:

[1233] Video data analysis and tagging

[1234] server:

[1235] Using a video analysis module (e.g., OpenCV or TensorFlow), key environmental features (buildings, roads, sky, etc.) from the video are extracted and tagged.

[1236] (Input): Video data

[1237] (Data processing): Video data analysis, feature extraction, tagging

[1238] (Output): Analysis results (tagged data)

[1239] Specific behavior:

[1240] Video data is divided into frames.

[1241] An object detection algorithm is applied to each frame.

[1242] Tags the detected objects and stores the results in a database.

[1243] Step 5:

[1244] Generate videos of simulated weather changes

[1245] server:

[1246] Using a trained generative AI model (e.g., PyTorch or TensorFlow), weather changes are simulated based on the acquired weather forecast data and analysis results, and a simulation video is generated.

[1247] (Input): Weather forecast data, analysis results

[1248] (Data processing): Weather simulation, video generation

[1249] (Output): Simulation video

[1250] Specific behavior:

[1251] Weather forecast data and video analysis results are input into the AI ​​model.

[1252] A generative AI model runs weather change simulations.

[1253] The simulation results are applied to the original video data to generate a simulated video.

[1254] Step 6:

[1255] Encoding and sending simulation videos

[1256] server:

[1257] The generated simulation video is encoded (for example, using FFmpeg) and sent to the user's device.

[1258] (Input): Simulation video

[1259] (Data processing): Video encoding, data transmission

[1260] (Output): Encoded video file

[1261] Specific behavior:

[1262] The generated simulation video is encoded.

[1263] Send an API request to send the encoded video file to the user's device.

[1264] Step 7:

[1265] Simulation video playback

[1266] Device:

[1267] The user then launches the dedicated application again and plays the simulation video received from the server, allowing the user to visually confirm changes in the weather.

[1268] (Input): Received simulation video

[1269] (Data processing): Video playback

[1270] (Output): Played simulation video

[1271] Specific behavior:

[1272] The application receives the notification and notifies the user that a new simulation video is available.

[1273] When the user taps the notification, the application launches and plays the video.

[1274] By performing the above steps, it becomes possible for the user to visually check weather changes in a location specified by the user in real time.

[1275] (Application example 1)

[1276] 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."

[1277] In conventional food delivery services, delivery personnel have limited means of understanding weather changes in real time, which can lead to delays and safety issues due to unexpected weather changes. In addition, intuitive simulation tools to improve the efficiency of delivery routes are lacking. To solve these issues, a weather change simulation system based on video of delivery routes is needed.

[1278] 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.

[1279] In this invention, the server includes a means for a user to shoot video along a food delivery route and simulate weather changes, a means for determining the safety and efficiency of the delivery route based on the generated simulated video, and a means for compressing the video data and location information shot by the user and transmitting them to the artificial intelligence, thereby enabling delivery personnel to visually check weather changes in real time and select a safe and efficient delivery route.

[1280] "National video equipment" refers to equipment used to capture images, such as cameras and smartphones installed in each region.

[1281] "Weather forecast data" refers to forecast information about the weather in a specified area obtained from meteorological agencies or weather APIs.

[1282] "Artificial intelligence" refers to computer programs or systems that learn from large amounts of data, recognize patterns, and make inferences.

[1283] A "user" is a person or organization that uses this system to perform weather change simulations.

[1284] "Video data" is a media file that contains a series of image frames captured by a user.

[1285] "Location information" refers to the geographic coordinate data (latitude and longitude) at the time of shooting.

[1286] "Simulation video" refers to realistic video data generated based on predicted weather changes.

[1287] "Food delivery" is a service that delivers food from stores to customers.

[1288] A "delivery route" is the route or path a delivery person takes to deliver an order.

[1289] "Safety" refers to the conditions or circumstances under which delivery personnel do not encounter accidents or dangers during delivery.

[1290] "Efficiency" is the ability or state of achieving a goal in the shortest time using the least amount of effort or resources.

[1291] "Weather effects" refers to processing and effects used to visually express weather changes in video data.

[1292] "Data compression" refers to techniques and methods for efficiently storing and transmitting large amounts of data.

[1293] The present invention provides a system that allows a user to simulate and visually check weather changes along a food delivery route. Hereinafter, an embodiment of this system will be described.

[1294] System configuration

[1295] 1. User Device

[1296] The user (delivery worker) uses a smartphone, which has video recording and location information acquisition functions. The user records a short video of their delivery route and uploads the video and location information to the server using a dedicated application. At this time, the video and location information are efficiently encoded using data compression technology.

[1297] 2. Server

[1298] The server has the following functions:

[1299] Data reception and storage: The server receives the video data and location information sent from the user's device and temporarily stores them.

[1300] Obtaining weather forecast data: Based on the saved location information, the server obtains the corresponding weather forecast data from an external weather forecast API.

[1301] Video analysis and simulation: The received video data is analyzed to extract and tag basic environmental features, and then an artificial intelligence model is used to simulate weather changes based on the acquired weather forecast data.

[1302] Generation and encoding of simulation video: Video is generated based on the results of simulating weather changes and encoded into a format that can be sent to the user's device.

[1303] 3. User-facing applications

[1304] Users can play the generated simulation video through a dedicated application, which allows them to visually check weather changes along their delivery route and ensure safe and efficient deliveries.

[1305] System Operation

[1306] Video and location upload

[1307] The user's device records the delivery route, and the video and location information are uploaded to the server via the application. The data is compressed and transmitted during upload.

[1308] Data processing and storage

[1309] The server temporarily stores the video and location information sent, and then uses the weather forecast API to obtain weather forecast data for the corresponding area based on the stored location information.

[1310] Weather change simulation

[1311] The server analyzes the video data, extracts and tags key environmental features (buildings, roads, sky, etc.), then uses an artificial intelligence model to simulate weather changes based on weather forecast data, and generates and encodes the video based on the simulation results.

[1312] Providing simulation results to users

[1313] The server sends the generated simulation video to the user's device, and the user plays the simulation video in the application to visually check the weather changes along the delivery route.

[1314] Specific examples

[1315] For example, when a delivery person checks weather changes along a designated delivery route in Shibuya Ward, the following system operations are performed.

[1316] 1. User: Takes a video of the delivery route with a smartphone and uploads the video and location information to the server via the application.

[1317] 2. Server: The server stores the received video and location information, obtains weather forecast data for Shibuya Ward, analyzes the video data, and simulates weather changes.

[1318] 3. Server: Generates video based on the simulation results, encodes it, and sends it to the user's device.

[1319] 4. User device: The user plays a simulation video on the application and checks the weather changes along the delivery route.

[1320] Prompt Sentence Examples

[1321] video_data: base64_encoded_video_data

[1322] location: 35.6586, 139.7454

[1323] weather_forecast:

[1324] timestamp: "2023-10-10T08:00:00Z"

[1325] conditions: "rain"

[1326] temperature: 15

[1327] Using this prompt, the artificial intelligence model applies the specified weather conditions to the video and generates a visually verifiable simulation video.

[1328] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1329] Step 1:

[1330] The user's device captures video of the delivery route and acquires location information. The video data and location information are efficiently encoded by the application using data compression technology, reducing the file size of the video and location information and enabling faster transmission to the server.

[1331] Input: Delivery route video data, location information

[1332] Output: Compressed video data, compressed location information

[1333] Step 2:

[1334] The user's device uploads the compressed video data and location information to the server, and the application requests the server to send the video data and location information as a pair.

[1335] Input: Compressed video data, compressed location information

[1336] Output: Compressed data stored on the server

[1337] Step 3:

[1338] The server temporarily stores the received video data and location information. Based on the stored location information, the server calls an external weather forecast API to obtain weather forecast data for the corresponding area.

[1339] Input: Compressed data stored on the server, location information

[1340] Output: Weather forecast data

[1341] Step 4:

[1342] The server analyzes the received video data and extracts and tags basic environmental features (buildings, roads, sky, etc.) Video analysis is performed using computer vision technology to recognize specific objects in each frame and assign features as tags.

[1343] Input: Saved video data

[1344] Output: Extracted environmental features (tagged data)

[1345] Step 5:

[1346] The server uses an artificial intelligence model to simulate weather changes based on weather forecast data. The simulation takes tagged data obtained through video analysis as input and applies weather effects to each frame.

[1347] Input: Weather forecast data, tagged data

[1348] Output: Frame with weather effects applied

[1349] Step 6:

[1350] The server connects the frames that simulate weather changes to generate a simulation video, which is then encoded into a format that can be played on a user device.

[1351] Input: Frame with weather effect applied

[1352] Output: Encoded simulation video

[1353] Step 7:

[1354] The server transmits the encoded simulation video to the user terminal, which receives and plays the simulation video through a dedicated application.

[1355] Input: Encoded simulation video

[1356] Output: Simulation video sent to the user's device

[1357] 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.

[1358] The system of the present invention simulates weather changes at a location specified by the user and provides them in a visually verifiable format. It also analyzes the user's emotions and adds a function to customize the simulation animation based on those emotions, providing more personalized weather information.

[1359] System configuration

[1360] 1. On the user's device:

[1361] The system uses smartphones or tablets with video recording and location information acquisition capabilities. Users use these devices to record short videos of designated locations and upload them via an application or web interface.

[1362] The dedicated application has the function of compressing video and location information and sending it to a server, and also has the function of collecting emotional data using a camera and microphone to recognize the user's emotions.

[1363] 2. Server:

[1364] It has the function of temporarily storing and analyzing the received video and location information, and obtaining weather forecast data from an external weather forecast API based on the stored location information.

[1365] Video analysis functions are used to extract and tag key environmental features from the video (buildings, roads, sky, etc.).

[1366] It has the function of using an emotion engine to analyze the user's emotional data and customize the weather simulation based on the analysis results.

[1367] Using a trained artificial intelligence model, weather changes based on weather forecast data and emotion data are simulated and videos are generated.

[1368] The generated simulation video is encoded and sent to the user's device.

[1369] Program processing explanation

[1370] Uploading videos, location information, and emotion data

[1371] Device:

[1372] Users launch the application and record a short video of a designated location. The video and location information are then encoded using data compression technology. Emotional data is also collected via the device's camera and microphone.

[1373] Data processing and storage

[1374] server:

[1375] The received video, location information, and emotion data are temporarily stored. Weather forecast data is retrieved from an external weather forecast API based on the stored location information. Next, the video footage is analyzed to extract and tag basic environmental features (buildings, roads, sky, etc.). The emotion engine is also used to analyze the emotion data and identify the user's current emotional state.

[1376] Weather change simulation

[1377] server:

[1378] Using a trained AI model, a simulation is initiated based on the acquired weather forecast data and emotional data. This model generates a simulation video based on video analysis data and weather forecast data, simultaneously adding emotional effects.

[1379] The generated simulation results are reconstructed as a video and encoded to provide users with visual weather forecast information. Furthermore, by incorporating simulation results that have effects tailored to the user's emotions, more personalized information is provided.

[1380] Providing simulation results to users

[1381] server:

[1382] The encoded simulation video is sent to the user's device, where it is played using the user's dedicated application.

[1383] Specific examples

[1384] For example, if a user wants to check the weather on their commute route, the system operates as follows.

[1385] 1. User:

[1386] The user films the commute from home to the station and uploads the video, location information, and emotional data to a server via an application.

[1387] 2. Server:

[1388] After receiving the video, location information, and emotion data, the system retrieves weather forecast data for the commute route and analyzes the video footage.Then, an AI model simulates weather changes, and an emotion engine generates a simulated video that takes into account effects based on the user's emotions.

[1389] 3. Server:

[1390] The generated simulation video is encoded and sent to the user's device.

[1391] 4. Terminal:

[1392] Users can play simulated videos through the application to visually check the specific weather conditions along their commute route. In addition, by adding information based on the user's emotions, users can understand the weather forecast more intuitively.

[1393] This system allows users to obtain weather information in an intuitive format, rather than just numerical values ​​and icons, making it easier for them to decide what specific actions to take. In addition, by combining it with an emotion engine, it is possible to provide information optimized for each individual user.

[1394] The processing flow will be explained below.

[1395] Step 1:

[1396] User: Record a short video of a designated location (e.g., in front of your home, on your commute route, etc.) using your smartphone.

[1397] How it works: Open the camera app on your smartphone and record the specified location in video recording mode.

[1398] Step 2:

[1399] User: Launch the dedicated application and open the video upload screen.

[1400] How it works: Tap the "Upload Video" button in the app and select a video file you've already taken.

[1401] Step 3:

[1402] User: Allow location sharing and start collecting emotional data.

[1403] How it works: Follow the app's prompts to allow the option to use location information (GPS data) and emotional data collection using the camera and microphone.

[1404] Step 4:

[1405] Device: Compresses selected video and location information, and collects emotional data.

[1406] How it works: It applies a video compression algorithm to optimize video data, and also analyzes the user's facial expressions and voice data obtained through the camera and microphone to extract emotional data.

[1407] Step 5:

[1408] Device: Compressed video, location information, and emotion data are sent to the server.

[1409] Behavior: Generates an HTTP request and sends video data, location information, and emotion data.

[1410] Step 6:

[1411] Server: Temporarily stores received video, location information, and emotion data.

[1412] What it does: Stores the received data in a database or temporary file storage.

[1413] Step 7:

[1414] Server: Based on the saved location information, send a request to an external weather forecast API to retrieve the corresponding weather forecast data.

[1415] What it does: Uses location information to retrieve weather data from a weather API.

[1416] Step 8:

[1417] Server: Analyzes emotion data using the emotion engine and stores the results.

[1418] Operation: The emotion engine runs, analyzes collected voice and facial expression data, and determines the user's emotional state. The analysis results are stored in a database.

[1419] Step 9:

[1420] Server: Analyzes video data and extracts and tags basic environmental features.

[1421] How it works: It uses computer vision techniques to analyze video frame by frame, identifying and tagging elements such as buildings, roads, and sky.

[1422] Step 10:

[1423] Server: Uses a trained artificial intelligence model to simulate weather changes based on weather forecast data and emotion data.

[1424] How it works: Video analysis data, weather forecast data, and emotional effects are fed into an AI model to generate specific weather effects.

[1425] Step 11:

[1426] Server: Overlays the simulation results onto the original video to generate a new simulation video.

[1427] How it works: It uses image synthesis technology to apply weather effects to the original footage, creating a consistent video, and adding visual effects that match the user's emotional state.

[1428] Step 12:

[1429] Server: Encodes the generated simulation video into a specified format.

[1430] How it works: Use video encoding software to convert the simulation video to MP4 or MKV format.

[1431] Step 13:

[1432] Server: Sends the encoded simulation video to the user's device.

[1433] What it does: Uploads a video file to cloud storage and provides a download link to the user or sends it directly.

[1434] Step 14:

[1435] Terminal: Plays the received simulation video and displays it to the user.

[1436] How it works: A dedicated application plays downloaded video files, providing users with visualized weather information and emotional effects.

[1437] This allows users to obtain weather forecast information while visually checking the actual scenery. In addition, the emotion engine customizes the video, providing more intuitive and familiar information to users.

[1438] Example 2

[1439] 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."

[1440] Conventional weather information systems simply display weather information using numerical data and icons, making it difficult for users to intuitively understand. Furthermore, they do not take into account the user's emotional state when providing information, making it difficult to provide information optimized for each individual user.

[1441] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring external weather forecast data using received video data and location information to simulate weather changes at a location specified by the user, means for analyzing the user's emotions and customizing the weather simulation based on the emotion data, and means for generating a video by simulating weather changes based on the weather forecast data and emotion data using a trained artificial intelligence model. This allows the user to obtain weather information that is visually easy to understand and individually optimized.

[1442] "Received video data" refers to digital video files uploaded to the system as videos taken by users.

[1443] "Location Information" means data collected from a user device that indicates specific geographic coordinates using GPS or other location measurement technology.

[1444] "Weather Forecast Data" means weather information for a specific region or time period obtained from an external weather forecast API or weather service.

[1445] "Emotion data" is data that represents the emotional state of the user, obtained by analyzing the user's facial expressions, voice, and the like.

[1446] An "artificial intelligence model" is a computer program that uses pre-trained machine learning algorithms to perform specific tasks.

[1447] "Weather simulation" refers to the process of using weather forecast data and other related data to predict future changes in weather conditions and generate the results in a visual format.

[1448] "Customization methods" are functions or methods for tailoring and optimizing the information and content provided based on a user's emotional data or other individual characteristics.

[1449] "Encoding" is the process of converting digital data into a particular format that allows for efficient storage or transmission.

[1450] "Weather effects" are visual effects applied to specific frames of a video that simulate real-world weather changes.

[1451] A "simulation video" is a new video file generated by applying a weather simulation to the received video data.

[1452] This invention is a system that simulates weather changes at a user-specified location and provides visually verifiable information. The system analyzes the user's emotional data and customizes the weather simulation based on that data to provide personalized information.

[1453] Specifically, the system consists of a user terminal and a server, the details of which are explained below.

[1454] Hardware and software used:

[1455] User device: A mobile device such as a smartphone or tablet. These devices have a camera, microphone, and GPS functionality, and have a dedicated application installed.

[1456] Server: A high-performance computer system equipped with an AI engine that includes machine learning models, and responsible for video analysis, weather data acquisition, emotion analysis, simulation generation, and other processes.

[1457] External API: A service for obtaining weather forecast data. Specifically, a weather data provider service is used.

[1458] Program processing:

[1459] 1. Video and emotion data collection:

[1460] The user launches the dedicated application and shoots a short video of a specified location. The application then uses the device's camera and microphone to collect emotion data. The application then encodes this data into a single data package and sends it to the server.

[1461] 2. Data Receipt and Analysis:

[1462] The server receives the data package sent by the user and analyzes the video data, location information, and emotion data. Based on the location information, it obtains the latest weather forecast data for the corresponding location from an external weather forecast API.

[1463] 3. Video and Emotion Analysis:

[1464] The server analyzes the received video data and extracts key environmental features such as buildings, roads, and the sky. It also uses an emotion analysis engine to analyze emotion data from the user's facial expressions and voice to identify the user's emotional state.

[1465] 4. Running the weather simulation:

[1466] The server uses a trained generative AI model to perform weather simulations based on weather forecast data and emotion data, generating a visually verifiable simulation video.

[1467] 5. Generate and provide simulation videos:

[1468] The server encodes the generated simulation video and sends it to the user's device, where the user can play the simulation video through a dedicated application and visually check the weather changes.

[1469] Examples:

[1470] For example, when a user wants to check the weather on his / her commute route, the specific operation is as follows.

[1471] 1. The user films their commute from home to the station and uploads the video, location information, and emotion data to the server via the application.

[1472] 2. The server receives the video, location information, and emotion data, and obtains weather forecast data for the commute route. It also analyzes the video data and extracts environmental features such as buildings and roads. The emotion analysis engine analyzes the user's emotions and generates a simulation video based on the results.

[1473] 3. The server encodes the generated simulation video and sends it to the user's device.

[1474] 4. The user plays a simulated video through the application to check the specific weather conditions along their commute route.

[1475] Example prompt sentence:

[1476] "Based on the specified location and weather forecast data, simulate the change from sunny to cloudy and create a simulation video that reflects the user's emotions as they relax."

[1477] "If the user is in a depressed state, generate a simulated video of a rainy cityscape."

[1478] In this way, the system provides weather information to users in a format that is intuitively understandable, realizing information delivery optimized for each individual user.

[1479] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1480] Step 1: Data collection

[1481] Input: Video, emotion data, and location information of the user-specified location

[1482] Processing: The user launches the dedicated application and shoots a short video of a designated location. The application also uses the device's camera and microphone to collect the user's facial expressions and voice as emotional data. This data is then encoded into a single data package within the application.

[1483] Output: Encoded data package (video, emotion data, location information)

[1484] Specific operation: The user presses the record button in the application to start recording. After recording is finished, emotion data is automatically collected and location information is attached.

[1485] Step 2: Send data

[1486] Input: Encoded data package (video, emotion data, location information)

[1487] Processing: The device compresses the encoded data package and sends it over the internet to a server.

[1488] Output: Data package sent to the server

[1489] Specific operation: The terminal automatically starts the data transmission process and sends the compressed data to the server.

[1490] Step 3: Data reception and analysis

[1491] Input: Data package sent to the server

[1492] Processing: The server unpacks the received data package and extracts the video data, emotion data, and location information. Based on the location information, it sends a request to an external weather forecast API to obtain weather forecast data for the corresponding location.

[1493] Output: Decompressed data (video, emotion data, location information), corresponding weather forecast data

[1494] Specific operation: The server unpacks the data package and uses the location information to access the weather forecast API to obtain the required data.

[1495] Step 4: Video and emotion analysis

[1496] Input: Decompressed video data, emotion data

[1497] Processing: The server uses a video analysis algorithm to analyze each frame of the video data and extract key environmental features such as buildings, roads, and sky. At the same time, it uses an emotion analysis engine to analyze the user's emotion data and determine the user's emotional state (e.g., joy, anger, sadness, or happiness).

[1498] Output: Environmental feature data, emotion judgment data

[1499] How it works: The server analyzes the video frame by frame and tags it with environmental features. The emotion analysis engine identifies the user's emotional state from their facial expressions and voice.

[1500] Step 5: Run the weather simulation

[1501] Input: Environmental feature data, emotion judgment data, weather forecast data

[1502] Processing: The server uses a trained generative AI model to simulate weather changes based on the acquired weather forecast data and emotion judgment data. The generative AI model takes these data as inputs and generates a visually easy-to-understand weather simulation video.

[1503] Output: Simulation video data

[1504] How it works: The server inputs data into the AI ​​model and runs a simulation process based on weather and emotions.

[1505] Step 6: Generate and provide simulation videos

[1506] Input: Simulation video data

[1507] Processing: The server encodes the generated simulation video and sends it to the user's device.

[1508] Output: Simulation video link sent to user's device

[1509] What happens: The server encodes the video into the appropriate format and sends a download link to the user's device.

[1510] Step 7: Play the video

[1511] Input: Simulation video link sent to user's device

[1512] Processing: The user plays the received simulation video using a dedicated application. The application streams or downloads the video file and plays it.

[1513] Output: Visual weather simulation video

[1514] Specific operation: Users can intuitively check weather changes by pressing the play button within the application and watching a simulation video.

[1515] (Application example 2)

[1516] 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."

[1517] In today's food delivery industry, delivery partners are vulnerable to real-time weather changes. Delivering in bad weather can increase stress and negatively impact delivery efficiency. Traditional weather forecasts alone are insufficient to deal with these situations, and adequate support for delivery partners is lacking. Therefore, there is a need for a system that provides visual real-time weather information along delivery routes and customized information tailored to delivery partners' emotions.

[1518] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1519] In this invention, the server includes means for simulating weather changes at a location specified by a user using artificial intelligence that has learned video from video devices nationwide and weather forecast data, means for receiving video data and location information taken by the user, means for simulating predicted weather changes based on the video data and generating a video, means for analyzing user emotion data and customizing the simulation video based on the analyzed data, and means for providing the generated simulation video to the user. This makes it possible to visually simulate real-time weather changes along a delivery route and provide customized information according to the delivery partner's emotions.

[1520] "Nationwide video equipment" refers to video cameras and fixed cameras installed in various locations, and includes video data acquired from these devices.

[1521] "Weather forecast data" is data intended for weather forecasting, and includes information such as temperature, humidity, precipitation, wind speed, and wind direction.

[1522] "Artificial intelligence" refers to computer systems that use techniques such as machine learning and data analysis to perform specific tasks.

[1523] "Means for simulating weather changes" refers to a device or program that virtually reproduces weather fluctuations based on acquired data.

[1524] "Location information" is data that indicates the physical location of a target object or person, and includes GPS data.

[1525] "Video data" refers to visual information consisting of a series of image frames.

[1526] "Predicted weather changes" means future weather conditions calculated based on acquired weather forecast data.

[1527] "Means for generating" refers to a device or program that creates new data or objects based on some data.

[1528] "Simulation video" refers to a video clip that visualizes predicted weather changes.

[1529] "User emotion data" refers to data that indicates the user's feelings and mental state, obtained through voice analysis, facial expression analysis, and the like.

[1530] "Means for analyzing" refers to a device or program that takes data and extracts useful information from it.

[1531] "Customization means" refers to a device or program that tailors information or functionality to a user's individual needs or circumstances.

[1532] "Means for providing" refers to a device or program that distributes or delivers the generated data or information to the user.

[1533] "Delivery Route" means the route taken by a Delivery Partner when delivering Products.

[1534] "Real-time weather information" refers to data that provides immediate information about current weather conditions.

[1535] "Customized Information" means information that is tailored to a user's particular circumstances and requirements.

[1536] A "delivery partner" refers to a person whose role is to deliver goods to customers in services such as food delivery.

[1537] The system of the present invention enables delivery partners in the food delivery industry to visually understand weather changes in real time and provides information customized to the delivery partner's emotions. This system is realized using smartphones, servers, and artificial intelligence models.

[1538] First, delivery partners use their smartphones to record short videos of their designated delivery route. The recorded video and location information are encoded using data compression technology within the smartphone and sent to a server. At the same time, emotion data collected through the smartphone's camera and microphone is also sent to the server.

[1539] The server temporarily stores the received video data, location information, and emotion data. Based on the location information, it obtains weather forecast data from an external weather forecast API, analyzes the video footage data to extract and tag basic environmental features (e.g., buildings, roads, sky), and uses an emotion engine to analyze the emotion data and identify the delivery partner's current emotional state.

[1540] The server then uses the trained AI model to simulate weather changes based on the acquired weather forecast data and emotion data, generating a simulation video. This simulation incorporates weather effects based on the weather forecast data and effects based on the emotion data. The generated simulation video is encoded and sent to the delivery partner's smartphone.

[1541] Delivery partners can view simulated videos on their smartphones, allowing them to visually check real-time weather changes along their delivery route. The app also provides personalized information based on their emotions, reducing stress and allowing them to carry out their work with peace of mind.

[1542] As a specific example, a delivery partner takes a video of their designated delivery route and uploads the video, location information, and emotional data to a server via an application. Based on this data, the server obtains weather forecast data and performs video and emotional analysis. After that, an AI model simulates weather changes and generates a simulated video that also incorporates emotional effects. This simulated video is sent to the delivery partner and played back via the application.

[1543] Examples of prompts include:

[1544] "Generate a weather simulation from Tokyo Station to Shinagawa Station."

[1545] "Include an encouraging message if your delivery partner is feeling stressed."

[1546] In this way, the system of the present invention provides delivery partners with real-time visual weather information and emotionally customized information, enabling safer and more efficient deliveries.

[1547] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1548] Step 1:

[1549] Shooting and uploading video, location information, and emotional data

[1550] How it works: The user (delivery partner) uses their smartphone to record a short video of their designated delivery route.

[1551] Input: Video captured by a smartphone camera and location information obtained from the smartphone's GPS.

[1552] Output: Video data, location information, and emotional data (user facial expressions and voice data collected by the camera and microphone). This data is compressed and sent to the server.

[1553] Step 2:

[1554] Receiving and storing data

[1555] Operation: The server receives data sent from the user terminal.

[1556] Input: Compressed video data, location information, and emotion data.

[1557] Output: Video data, location information, and emotion data stored on the server.

[1558] Step 3:

[1559] Obtaining weather forecast data

[1560] How it works: The server retrieves weather forecast data from an external weather API based on the saved location information.

[1561] Input: Location.

[1562] Output: Weather forecast data (e.g. temperature, humidity, precipitation, etc.).

[1563] Step 4:

[1564] Video data analysis

[1565] How it works: The server analyzes the video data, extracts and tags basic features of the environment (buildings, roads, sky, etc.).

[1566] Input: Video data.

[1567] Output: Tagged environment feature data.

[1568] Step 5:

[1569] Emotional Data Analysis

[1570] How it works: The server's emotion engine is used to parse the emotion data and identify the user's current emotional state.

[1571] Input: Emotion data.

[1572] Output: Sentiment analysis result (e.g. whether the user is stressed or relaxed).

[1573] Step 6:

[1574] Weather change simulation and video generation

[1575] Operation: Using the server's trained AI model, weather changes are simulated based on the acquired weather forecast data and emotion data, and a simulation video is generated.

[1576] Input: Weather forecast data, environmental feature data, and sentiment analysis results.

[1577] Output: Simulation video (with weather and emotion effects).

[1578] Step 7:

[1579] Encoding and sending simulation videos

[1580] How it works: The server encodes the generated simulation video and sends it to the user's smartphone.

[1581] Input: Simulation video.

[1582] Output: Encoded simulation video.

[1583] Step 8:

[1584] Simulation video playback

[1585] How it works: Users play the simulation video received through a smartphone app and visually check the weather changes along their delivery route.

[1586] Input: Encoded simulation video.

[1587] Output: Simulation video to be played and customization information according to emotions.

[1588] 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.

[1589] 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.

[1590] 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.

[1591] [Fourth embodiment]

[1592] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1593] 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.

[1594] 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).

[1595] 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.

[1596] 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.

[1597] 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).

[1598] 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.

[1599] 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.

[1600] 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.

[1601] 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.

[1602] 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.

[1603] 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.

[1604] 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."

[1605] The system of the present invention simulates weather changes at a location designated by a user and provides the results in a form that can be visually confirmed. An embodiment of this system will be described below.

[1606] System configuration

[1607] 1. On the user's device:

[1608] The system uses smartphones or tablets with video recording and location information acquisition capabilities. Users use these devices to record short videos of designated locations and upload them via an application or web interface.

[1609] The dedicated application has the ability to compress video and location information and send them to a server.

[1610] 2. Server:

[1611] It has the function of temporarily storing and analyzing the received video and location information. The server retrieves the corresponding weather forecast data from an external API based on the location information.

[1612] Video analysis functions are used to extract and tag key environmental features from the video (buildings, roads, sky, etc.).

[1613] Using a trained artificial intelligence model, weather changes based on weather forecast data are simulated and videos are generated.

[1614] The generated simulation video is encoded and sent to the user's device.

[1615] Program processing explanation

[1616] Video and location upload

[1617] Device:

[1618] The user launches the application and shoots a short video of a specified location. The video is then sent to the server along with the location information, which is then efficiently encoded using data compression technology.

[1619] Data processing and storage

[1620] server:

[1621] The received video and location information are temporarily stored. Based on the stored location information, the corresponding weather forecast data is retrieved from an external weather forecast API. Next, the video data is analyzed to extract and tag basic environmental features (e.g., buildings, roads, sky).

[1622] Weather change simulation

[1623] server:

[1624] Using a trained AI model, a simulation is initiated based on the acquired weather forecast data. The model takes the video analysis data as input and applies the predicted weather changes to the original video data.

[1625] The generated simulation results are reconstructed as a video and encoded in a way that provides users with visual weather forecast information.

[1626] Providing simulation results to users

[1627] server:

[1628] The encoded simulation video is sent to the user's device, where it is played back using the user's dedicated application.

[1629] Specific examples

[1630] For example, if a user wants to check the weather on their commute route, the system operates as follows.

[1631] 1. User:

[1632] The user films the route from home to the station and uploads the video and location information to a server via an application.

[1633] 2. Server:

[1634] After receiving the video and location information, the weather forecast data for the commute route is retrieved and the video data is analyzed, after which an AI model is used to simulate weather changes and generate a simulated video.

[1635] 3. Server:

[1636] The generated simulation video is encoded and sent to the user's device.

[1637] 4. Terminal:

[1638] Users can play simulated videos through the application to visually check the specific weather conditions along their commute route.

[1639] This system allows users to obtain weather information in an intuitive format, rather than just numerical values ​​and icons, making it easier for them to decide what specific actions to take.

[1640] The processing flow will be explained below.

[1641] Step 1:

[1642] User: Record a short video of a designated location (e.g., in front of your home, on your commute route, etc.) using your smartphone.

[1643] How it works: Open the camera app on your smartphone and record the specified location in video recording mode.

[1644] Step 2:

[1645] User: Launch the dedicated application and open the video upload screen.

[1646] How it works: Tap the "Upload Video" button in the app and select a video file you've already taken.

[1647] Step 3:

[1648] User: Allow location sharing.

[1649] How it works: Follow the prompts in the app and turn on the option to use location information (GPS data).

[1650] Step 4:

[1651] Device: Compress selected video and location information.

[1652] What it does: Applies a video compression algorithm to optimize the video data. Adds captured GPS data to the video file.

[1653] Step 5:

[1654] Device: Sends compressed video and location information to the server.

[1655] What it does: Creates an HTTP request and sends video data and location information.

[1656] Step 6:

[1657] Server: Temporarily stores received video and location information.

[1658] What it does: Stores the received data in a database or temporary file storage.

[1659] Step 7:

[1660] Server: Based on the saved location information, send a request to an external weather forecast API to retrieve the corresponding weather forecast data.

[1661] What it does: Uses location information to retrieve weather data from a weather API.

[1662] Step 8:

[1663] Server: Analyzes video data and extracts and tags basic environmental features.

[1664] How it works: It uses computer vision techniques to analyze video frame by frame, identifying and tagging elements such as buildings, roads, and sky.

[1665] Step 9:

[1666] Server: Uses trained artificial intelligence models to simulate weather changes based on weather forecast data.

[1667] How it works: Video analytics data and weather forecast data are fed into an AI model to generate specific weather effects.

[1668] Step 10:

[1669] Server: Overlays the simulation results onto the original video to generate a new simulation video.

[1670] How it works: Using image compositing techniques, weather effects are applied to the original footage to create a consistent video.

[1671] Step 11:

[1672] Server: Encodes the generated simulation video into a specified format.

[1673] How it works: Use video encoding software to convert the simulation video to MP4 or MKV format.

[1674] Step 12:

[1675] Server: Sends the encoded simulation video to the user's device.

[1676] What it does: Uploads a video file to cloud storage and provides a download link to the user or sends it directly.

[1677] Step 13:

[1678] Terminal: Plays the received simulation video and displays it to the user.

[1679] How it works: A dedicated application plays a downloaded video file and provides the user with visualized weather information.

[1680] In this way, the user can visually check the actual scenery together with the simulated weather information.

[1681] Example 1

[1682] 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."

[1683] Conventional weather forecast systems only provide weather information using numerical values ​​and icons, making it difficult for users to intuitively understand specific weather changes. In addition, there is a lack of technology to simulate detailed weather changes in a specific location, and there is no way for users to visually check the weather at any location.

[1684] 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.

[1685] In this invention, the server includes means for using the user's terminal to shoot a short video of a location specified by the user, compressing the video and location information and transmitting the compressed video to the server, means for the server to temporarily store the received video and location information and acquire corresponding weather forecast data from an external weather forecast service, means for the server to analyze the video data, extract and tag basic environmental features, means for using a trained generative AI model to simulate weather changes based on the acquired weather forecast data and analysis results and generate a simulation video, and means for encoding the generated simulation video and transmitting it to the user's terminal, thereby enabling the user to visually and intuitively understand weather changes at the specified location.

[1686] A "user device" is an electronic device such as a smartphone or tablet that has video recording and location information acquisition functions.

[1687] The "server" is a computer system that acquires weather forecast data from external weather forecast services and stores and analyzes video and location information.

[1688] "Video" is short video data taken at a location specified by the user.

[1689] "Location information" is data that indicates the geographical location where a video was taken.

[1690] "Data compression" refers to the process of compressing data to efficiently store and transmit video and location information.

[1691] "Weather Forecast Data" means current and future weather information for a particular location obtained from an external weather forecast service.

[1692] "Video analysis" is the process of extracting and tagging basic environmental features (buildings, roads, sky, etc.) from video data.

[1693] A "generative AI model" is a pre-trained artificial intelligence model used to simulate weather changes based on weather forecast data.

[1694] "Weather change simulation" is a process that reproduces weather changes in videos taken by the user based on acquired weather forecast data and the results of video analysis.

[1695] A "simulation video" is a user-filmed video in which weather changes are applied by a generative AI model.

[1696] "Encoding" is the process of converting the simulation video into a format that can be played on the user's device.

[1697] MODE FOR CARRYING OUT THE INVENTION

[1698] The system of the present invention provides a user with a visual indication of changes in the weather at a location designated by the user. Specific embodiments for implementing this system will be described below.

[1699] composition

[1700] 1. On the user's device:

[1701] Using electronic devices such as smartphones and tablets that have video recording and location information acquisition functions, users can record short videos of designated locations and upload the videos and location information to a server using a dedicated application.

[1702] The dedicated application has the ability to compress video and location information and send it to a server.

[1703] 2. Server:

[1704] The server has the function of temporarily storing and analyzing the received video and location information. The video data and location information are stored using a database system (e.g., MySQL) and a file system.

[1705] The server retrieves weather forecast data from an external weather forecast API (e.g., OpenWeatherMap API) based on the location information.

[1706] Use video analysis modules (e.g., OpenCV or TensorFlow) to extract and tag key environmental features from the video (e.g., buildings, roads, sky, etc.).

[1707] Using a trained generative AI model (e.g., PyTorch or TensorFlow), weather changes are simulated based on the acquired weather forecast data and analysis results, and a simulation video is generated.

[1708] The generated simulation video is encoded using FFmpeg or similar and sent to the user's device.

[1709] Usage example

[1710] As a concrete example, a scenario where a user wants to check the weather on his / her commute route will be shown.

[1711] 1. User:

[1712] The user takes a video of their commute from home to the station and uploads it to a server along with their location information via a dedicated application. For example, the user can use a prompt such as, "Please simulate the weather on tomorrow's commute route."

[1713] 2. Server:

[1714] The system receives video and location information and obtains weather forecast data for the commute route from an external weather forecast API. It then analyzes the video data and extracts and tags basic environmental features (buildings, roads, sky, etc.). Based on the video analysis results, a trained generative AI model simulates weather changes and generates a simulated video.

[1715] 3. Server:

[1716] The generated simulation video is encoded and sent to the user's device.

[1717] 4. Terminal:

[1718] Users can play simulation videos through a dedicated application and visually check the specific weather conditions on their commute route.

[1719] This allows users to obtain weather information in a concrete and intuitive format, rather than just numerical values ​​and icons, which can be used to help with everyday decisions and actions.

[1720] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1721] Step 1:

[1722] Capture and upload videos and location information

[1723] Device:

[1724] Users launch the dedicated application and shoot a short video of a specified location, and the application uses the device's GPS to obtain location information.

[1725] (Input): User operation (start shooting)

[1726] (Data processing): Video recording, location information acquisition

[1727] (Output): Recorded video file, acquired location information

[1728] Specific behavior:

[1729] The user presses the "Start shooting" button within the app.

[1730] The device's camera will activate and take a picture of the specified location.

[1731] After shooting is completed, the location information is acquired and the video and location information are compressed.

[1732] The compressed data is sent to the server.

[1733] Step 2:

[1734] Receiving and storing video and location information

[1735] server:

[1736] The server temporarily stores the received video and location information. It uses a database system (e.g., MySQL) and a file system to store the video data and location information.

[1737] (Input): Compressed video file, location information

[1738] (Data processing): Data storage

[1739] (Output): Saved video files, location database

[1740] Specific behavior:

[1741] The server's API receives the send request.

[1742] Save the video file to the file system.

[1743] Record location information in a database.

[1744] Step 3:

[1745] Obtaining weather forecast data

[1746] server:

[1747] Based on the location information, the corresponding weather forecast data is obtained from an external weather forecast API (e.g., OpenWeatherMap API).

[1748] (Input): Location data

[1749] (Data processing): Sending API requests, analyzing weather forecast data

[1750] (Output): Weather forecast data

[1751] Specific behavior:

[1752] Generate and send an API request to an external weather service.

[1753] Weather forecast data is returned as a response.

[1754] The acquired data is analyzed and stored in a database.

[1755] Step 4:

[1756] Video data analysis and tagging

[1757] server:

[1758] Using a video analysis module (e.g., OpenCV or TensorFlow), key environmental features (buildings, roads, sky, etc.) from the video are extracted and tagged.

[1759] (Input): Video data

[1760] (Data processing): Video data analysis, feature extraction, tagging

[1761] (Output): Analysis results (tagged data)

[1762] Specific behavior:

[1763] Video data is divided into frames.

[1764] An object detection algorithm is applied to each frame.

[1765] Tags the detected objects and stores the results in a database.

[1766] Step 5:

[1767] Generate videos of simulated weather changes

[1768] server:

[1769] Using a trained generative AI model (e.g., PyTorch or TensorFlow), weather changes are simulated based on the acquired weather forecast data and analysis results, and a simulation video is generated.

[1770] (Input): Weather forecast data, analysis results

[1771] (Data processing): Weather simulation, video generation

[1772] (Output): Simulation video

[1773] Specific behavior:

[1774] Weather forecast data and video analysis results are input into the AI ​​model.

[1775] A generative AI model runs weather change simulations.

[1776] The simulation results are applied to the original video data to generate a simulated video.

[1777] Step 6:

[1778] Encoding and sending simulation videos

[1779] server:

[1780] The generated simulation video is encoded (for example, using FFmpeg) and sent to the user's device.

[1781] (Input): Simulation video

[1782] (Data processing): Video encoding, data transmission

[1783] (Output): Encoded video file

[1784] Specific behavior:

[1785] The generated simulation video is encoded.

[1786] Send an API request to send the encoded video file to the user's device.

[1787] Step 7:

[1788] Simulation video playback

[1789] Device:

[1790] The user then launches the dedicated application again and plays the simulation video received from the server, allowing the user to visually confirm changes in the weather.

[1791] (Input): Received simulation video

[1792] (Data processing): Video playback

[1793] (Output): Played simulation video

[1794] Specific behavior:

[1795] The application receives the notification and notifies the user that a new simulation video is available.

[1796] When the user taps the notification, the application launches and plays the video.

[1797] By performing the above steps, it becomes possible for the user to visually check weather changes in a location specified by the user in real time.

[1798] (Application example 1)

[1799] 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."

[1800] In conventional food delivery services, delivery personnel have limited means of understanding weather changes in real time, which can lead to delays and safety issues due to unexpected weather changes. In addition, intuitive simulation tools to improve the efficiency of delivery routes are lacking. To solve these issues, a weather change simulation system based on video of delivery routes is needed.

[1801] 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.

[1802] In this invention, the server includes a means for a user to shoot video along a food delivery route and simulate weather changes, a means for determining the safety and efficiency of the delivery route based on the generated simulated video, and a means for compressing the video data and location information shot by the user and transmitting them to the artificial intelligence, thereby enabling delivery personnel to visually check weather changes in real time and select a safe and efficient delivery route.

[1803] "National video equipment" refers to equipment used to capture images, such as cameras and smartphones installed in each region.

[1804] "Weather forecast data" refers to forecast information about the weather in a specified area obtained from meteorological agencies or weather APIs.

[1805] "Artificial intelligence" refers to computer programs or systems that learn from large amounts of data, recognize patterns, and make inferences.

[1806] A "user" is a person or organization that uses this system to perform weather change simulations.

[1807] "Video data" is a media file that contains a series of image frames captured by a user.

[1808] "Location information" refers to the geographic coordinate data (latitude and longitude) at the time of shooting.

[1809] "Simulation video" refers to realistic video data generated based on predicted weather changes.

[1810] "Food delivery" is a service that delivers food from stores to customers.

[1811] A "delivery route" is the route or path a delivery person takes to deliver an order.

[1812] "Safety" refers to the conditions or circumstances under which delivery personnel do not encounter accidents or dangers during delivery.

[1813] "Efficiency" is the ability or state of achieving a goal in the shortest time using the least amount of effort or resources.

[1814] "Weather effects" refers to processing and effects used to visually express weather changes in video data.

[1815] "Data compression" refers to techniques and methods for efficiently storing and transmitting large amounts of data.

[1816] The present invention provides a system that allows a user to simulate and visually check weather changes along a food delivery route. Hereinafter, an embodiment of this system will be described.

[1817] System configuration

[1818] 1. User Device

[1819] The user (delivery worker) uses a smartphone, which has video recording and location information acquisition functions. The user records a short video of their delivery route and uploads the video and location information to the server using a dedicated application. At this time, the video and location information are efficiently encoded using data compression technology.

[1820] 2. Server

[1821] The server has the following functions:

[1822] Data reception and storage: The server receives the video data and location information sent from the user's device and temporarily stores them.

[1823] Obtaining weather forecast data: Based on the saved location information, the server obtains the corresponding weather forecast data from an external weather forecast API.

[1824] Video analysis and simulation: The received video data is analyzed to extract and tag basic environmental features, and then an artificial intelligence model is used to simulate weather changes based on the acquired weather forecast data.

[1825] Generation and encoding of simulation video: Video is generated based on the results of simulating weather changes and encoded into a format that can be sent to the user's device.

[1826] 3. User-facing applications

[1827] Users can play the generated simulation video through a dedicated application, which allows them to visually check weather changes along their delivery route and ensure safe and efficient deliveries.

[1828] System Operation

[1829] Video and location upload

[1830] The user's device records the delivery route, and the video and location information are uploaded to the server via the application. The data is compressed and transmitted during upload.

[1831] Data processing and storage

[1832] The server temporarily stores the video and location information sent, and then uses the weather forecast API to obtain weather forecast data for the corresponding area based on the stored location information.

[1833] Weather change simulation

[1834] The server analyzes the video data, extracts and tags key environmental features (buildings, roads, sky, etc.), then uses an artificial intelligence model to simulate weather changes based on weather forecast data, and generates and encodes the video based on the simulation results.

[1835] Providing simulation results to users

[1836] The server sends the generated simulation video to the user's device, and the user plays the simulation video in the application to visually check the weather changes along the delivery route.

[1837] Specific examples

[1838] For example, when a delivery person checks weather changes along a designated delivery route in Shibuya Ward, the following system operations are performed.

[1839] 1. User: Takes a video of the delivery route with a smartphone and uploads the video and location information to the server via the application.

[1840] 2. Server: The server stores the received video and location information, obtains weather forecast data for Shibuya Ward, analyzes the video data, and simulates weather changes.

[1841] 3. Server: Generates video based on the simulation results, encodes it, and sends it to the user's device.

[1842] 4. User device: The user plays a simulation video on the application and checks the weather changes along the delivery route.

[1843] Prompt Sentence Examples

[1844] video_data: base64_encoded_video_data

[1845] location: 35.6586, 139.7454

[1846] weather_forecast:

[1847] timestamp: "2023-10-10T08:00:00Z"

[1848] conditions: "rain"

[1849] temperature: 15

[1850] Using this prompt, the artificial intelligence model applies the specified weather conditions to the video and generates a visually verifiable simulation video.

[1851] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1852] Step 1:

[1853] The user's device captures video of the delivery route and acquires location information. The video data and location information are efficiently encoded by the application using data compression technology, reducing the file size of the video and location information and enabling faster transmission to the server.

[1854] Input: Delivery route video data, location information

[1855] Output: Compressed video data, compressed location information

[1856] Step 2:

[1857] The user's device uploads the compressed video data and location information to the server, and the application requests the server to send the video data and location information as a pair.

[1858] Input: Compressed video data, compressed location information

[1859] Output: Compressed data stored on the server

[1860] Step 3:

[1861] The server temporarily stores the received video data and location information. Based on the stored location information, the server calls an external weather forecast API to obtain weather forecast data for the corresponding area.

[1862] Input: Compressed data stored on the server, location information

[1863] Output: Weather forecast data

[1864] Step 4:

[1865] The server analyzes the received video data and extracts and tags basic environmental features (buildings, roads, sky, etc.) Video analysis is performed using computer vision technology to recognize specific objects in each frame and assign features as tags.

[1866] Input: Saved video data

[1867] Output: Extracted environmental features (tagged data)

[1868] Step 5:

[1869] The server uses an artificial intelligence model to simulate weather changes based on weather forecast data. The simulation takes tagged data obtained through video analysis as input and applies weather effects to each frame.

[1870] Input: Weather forecast data, tagged data

[1871] Output: Frame with weather effects applied

[1872] Step 6:

[1873] The server connects the frames that simulate weather changes to generate a simulation video, which is then encoded into a format that can be played on a user device.

[1874] Input: Frame with weather effect applied

[1875] Output: Encoded simulation video

[1876] Step 7:

[1877] The server transmits the encoded simulation video to the user terminal, which receives and plays the simulation video through a dedicated application.

[1878] Input: Encoded simulation video

[1879] Output: Simulation video sent to the user's device

[1880] 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.

[1881] The system of the present invention simulates weather changes at a location specified by the user and provides them in a visually verifiable format. It also analyzes the user's emotions and adds a function to customize the simulation animation based on those emotions, providing more personalized weather information.

[1882] System configuration

[1883] 1. On the user's device:

[1884] The system uses smartphones or tablets with video recording and location information acquisition capabilities. Users use these devices to record short videos of designated locations and upload them via an application or web interface.

[1885] The dedicated application has the function of compressing video and location information and sending it to a server, and also has the function of collecting emotional data using a camera and microphone to recognize the user's emotions.

[1886] 2. Server:

[1887] It has the function of temporarily storing and analyzing the received video and location information, and obtaining weather forecast data from an external weather forecast API based on the stored location information.

[1888] Video analysis functions are used to extract and tag key environmental features from the video (buildings, roads, sky, etc.).

[1889] It has the function of using an emotion engine to analyze the user's emotional data and customize the weather simulation based on the analysis results.

[1890] Using a trained artificial intelligence model, weather changes based on weather forecast data and emotion data are simulated and videos are generated.

[1891] The generated simulation video is encoded and sent to the user's device.

[1892] Program processing explanation

[1893] Uploading videos, location information, and emotion data

[1894] Device:

[1895] Users launch the application and record a short video of a designated location. The video and location information are then encoded using data compression technology. Emotional data is also collected via the device's camera and microphone.

[1896] Data processing and storage

[1897] server:

[1898] The received video, location information, and emotion data are temporarily stored. Weather forecast data is retrieved from an external weather forecast API based on the stored location information. Next, the video footage is analyzed to extract and tag basic environmental features (buildings, roads, sky, etc.). The emotion engine is also used to analyze the emotion data and identify the user's current emotional state.

[1899] Weather change simulation

[1900] server:

[1901] Using a trained AI model, a simulation is initiated based on the acquired weather forecast data and emotional data. This model generates a simulation video based on video analysis data and weather forecast data, simultaneously adding emotional effects.

[1902] The generated simulation results are reconstructed as a video and encoded to provide users with visual weather forecast information. Furthermore, by incorporating simulation results that have effects tailored to the user's emotions, more personalized information is provided.

[1903] Providing simulation results to users

[1904] server:

[1905] The encoded simulation video is sent to the user's device, where it is played using the user's dedicated application.

[1906] Specific examples

[1907] For example, if a user wants to check the weather on their commute route, the system operates as follows.

[1908] 1. User:

[1909] The user films the commute from home to the station and uploads the video, location information, and emotional data to a server via an application.

[1910] 2. Server:

[1911] After receiving the video, location information, and emotion data, the system retrieves weather forecast data for the commute route and analyzes the video footage.Then, an AI model simulates weather changes, and an emotion engine generates a simulated video that takes into account effects based on the user's emotions.

[1912] 3. Server:

[1913] The generated simulation video is encoded and sent to the user's device.

[1914] 4. Terminal:

[1915] Users can play simulated videos through the application to visually check the specific weather conditions along their commute route. In addition, by adding information based on the user's emotions, users can understand the weather forecast more intuitively.

[1916] This system allows users to obtain weather information in an intuitive format, rather than just numerical values ​​and icons, making it easier for them to decide what specific actions to take. In addition, by combining it with an emotion engine, it is possible to provide information optimized for each individual user.

[1917] The processing flow will be explained below.

[1918] Step 1:

[1919] User: Record a short video of a designated location (e.g., in front of your home, on your commute route, etc.) using your smartphone.

[1920] How it works: Open the camera app on your smartphone and record the specified location in video recording mode.

[1921] Step 2:

[1922] User: Launch the dedicated application and open the video upload screen.

[1923] How it works: Tap the "Upload Video" button in the app and select a video file you've already taken.

[1924] Step 3:

[1925] User: Allow location sharing and start collecting emotional data.

[1926] How it works: Follow the app's prompts to allow the option to use location information (GPS data) and emotional data collection using the camera and microphone.

[1927] Step 4:

[1928] Device: Compresses selected video and location information, and collects emotional data.

[1929] How it works: It applies a video compression algorithm to optimize video data, and also analyzes the user's facial expressions and voice data obtained through the camera and microphone to extract emotional data.

[1930] Step 5:

[1931] Device: Compressed video, location information, and emotion data are sent to the server.

[1932] Behavior: Generates an HTTP request and sends video data, location information, and emotion data.

[1933] Step 6:

[1934] Server: Temporarily stores received video, location information, and emotion data.

[1935] What it does: Stores the received data in a database or temporary file storage.

[1936] Step 7:

[1937] Server: Based on the saved location information, send a request to an external weather forecast API to retrieve the corresponding weather forecast data.

[1938] What it does: Uses location information to retrieve weather data from a weather API.

[1939] Step 8:

[1940] Server: Analyzes emotion data using the emotion engine and stores the results.

[1941] Operation: The emotion engine runs, analyzes collected voice and facial expression data, and determines the user's emotional state. The analysis results are stored in a database.

[1942] Step 9:

[1943] Server: Analyzes video data and extracts and tags basic environmental features.

[1944] How it works: It uses computer vision techniques to analyze video frame by frame, identifying and tagging elements such as buildings, roads, and sky.

[1945] Step 10:

[1946] Server: Uses a trained artificial intelligence model to simulate weather changes based on weather forecast data and emotion data.

[1947] How it works: Video analysis data, weather forecast data, and emotional effects are fed into an AI model to generate specific weather effects.

[1948] Step 11:

[1949] Server: Overlays the simulation results onto the original video to generate a new simulation video.

[1950] How it works: It uses image synthesis technology to apply weather effects to the original footage, creating a consistent video, and adding visual effects that match the user's emotional state.

[1951] Step 12:

[1952] Server: Encodes the generated simulation video into a specified format.

[1953] How it works: Use video encoding software to convert the simulation video to MP4 or MKV format.

[1954] Step 13:

[1955] Server: Sends the encoded simulation video to the user's device.

[1956] What it does: Uploads a video file to cloud storage and provides a download link to the user or sends it directly.

[1957] Step 14:

[1958] Terminal: Plays the received simulation video and displays it to the user.

[1959] How it works: A dedicated application plays downloaded video files, providing users with visualized weather information and emotional effects.

[1960] This allows users to obtain weather forecast information while visually checking the actual scenery. In addition, the emotion engine customizes the video, providing more intuitive and familiar information to users.

[1961] Example 2

[1962] 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."

[1963] Conventional weather information systems simply display weather information using numerical data and icons, making it difficult for users to intuitively understand. Furthermore, they do not take into account the user's emotional state when providing information, making it difficult to provide information optimized for each individual user.

[1964] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring external weather forecast data using received video data and location information to simulate weather changes at a location specified by the user, means for analyzing the user's emotions and customizing the weather simulation based on the emotion data, and means for generating a video by simulating weather changes based on the weather forecast data and emotion data using a trained artificial intelligence model. This allows the user to obtain weather information that is visually easy to understand and individually optimized.

[1965] "Received video data" refers to digital video files uploaded to the system as videos taken by users.

[1966] "Location Information" means data collected from a user device that indicates specific geographic coordinates using GPS or other location measurement technology.

[1967] "Weather Forecast Data" means weather information for a specific region or time period obtained from an external weather forecast API or weather service.

[1968] "Emotion data" is data that represents the emotional state of the user, obtained by analyzing the user's facial expressions, voice, and the like.

[1969] An "artificial intelligence model" is a computer program that uses pre-trained machine learning algorithms to perform specific tasks.

[1970] "Weather simulation" refers to the process of using weather forecast data and other related data to predict future changes in weather conditions and generate the results in a visual format.

[1971] "Customization methods" are functions or methods for tailoring and optimizing the information and content provided based on a user's emotional data or other individual characteristics.

[1972] "Encoding" is the process of converting digital data into a particular format that allows for efficient storage or transmission.

[1973] "Weather effects" are visual effects applied to specific frames of a video that simulate real-world weather changes.

[1974] A "simulation video" is a new video file generated by applying a weather simulation to the received video data.

[1975] This invention is a system that simulates weather changes at a user-specified location and provides visually verifiable information. The system analyzes the user's emotional data and customizes the weather simulation based on that data to provide personalized information.

[1976] Specifically, the system consists of a user terminal and a server, the details of which are explained below.

[1977] Hardware and software used:

[1978] User device: A mobile device such as a smartphone or tablet. These devices have a camera, microphone, and GPS functionality, and have a dedicated application installed.

[1979] Server: A high-performance computer system equipped with an AI engine that includes machine learning models, and responsible for video analysis, weather data acquisition, emotion analysis, simulation generation, and other processes.

[1980] External API: A service for obtaining weather forecast data. Specifically, a weather data provider service is used.

[1981] Program processing:

[1982] 1. Video and emotion data collection:

[1983] The user launches the dedicated application and shoots a short video of a specified location. The application then uses the device's camera and microphone to collect emotion data. The application then encodes this data into a single data package and sends it to the server.

[1984] 2. Data Receipt and Analysis:

[1985] The server receives the data package sent by the user and analyzes the video data, location information, and emotion data. Based on the location information, it obtains the latest weather forecast data for the corresponding location from an external weather forecast API.

[1986] 3. Video and Emotion Analysis:

[1987] The server analyzes the received video data and extracts key environmental features such as buildings, roads, and the sky. It also uses an emotion analysis engine to analyze emotion data from the user's facial expressions and voice to identify the user's emotional state.

[1988] 4. Running the weather simulation:

[1989] The server uses a trained generative AI model to perform weather simulations based on weather forecast data and emotion data, generating a visually verifiable simulation video.

[1990] 5. Generate and provide simulation videos:

[1991] The server encodes the generated simulation video and sends it to the user's device, where the user can play the simulation video through a dedicated application and visually check the weather changes.

[1992] Examples:

[1993] For example, when a user wants to check the weather on his / her commute route, the specific operation is as follows.

[1994] 1. The user films their commute from home to the station and uploads the video, location information, and emotion data to the server via the application.

[1995] 2. The server receives the video, location information, and emotion data, and obtains weather forecast data for the commute route. It also analyzes the video data and extracts environmental features such as buildings and roads. The emotion analysis engine analyzes the user's emotions and generates a simulation video based on the results.

[1996] 3. The server encodes the generated simulation video and sends it to the user's device.

[1997] 4. The user plays a simulated video through the application to check the specific weather conditions along their commute route.

[1998] Example prompt sentence:

[1999] "Based on the specified location and weather forecast data, simulate the change from sunny to cloudy and create a simulation video that reflects the user's emotions as they relax."

[2000] "If the user is in a depressed state, generate a simulated video of a rainy cityscape."

[2001] In this way, the system provides weather information to users in a format that is intuitively understandable, realizing information delivery optimized for each individual user.

[2002] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2003] Step 1: Data collection

[2004] Input: Video, emotion data, and location information of the user-specified location

[2005] Processing: The user launches the dedicated application and shoots a short video of a designated location. The application also uses the device's camera and microphone to collect the user's facial expressions and voice as emotional data. This data is then encoded into a single data package within the application.

[2006] Output: Encoded data package (video, emotion data, location information)

[2007] Specific operation: The user presses the record button in the application to start recording. After recording is finished, emotion data is automatically collected and location information is attached.

[2008] Step 2: Send data

[2009] Input: Encoded data package (video, emotion data, location information)

[2010] Processing: The device compresses the encoded data package and sends it over the internet to a server.

[2011] Output: Data package sent to the server

[2012] Specific operation: The terminal automatically starts the data transmission process and sends the compressed data to the server.

[2013] Step 3: Data reception and analysis

[2014] Input: Data package sent to the server

[2015] Processing: The server unpacks the received data package and extracts the video data, emotion data, and location information. Based on the location information, it sends a request to an external weather forecast API to obtain weather forecast data for the corresponding location.

[2016] Output: Decompressed data (video, emotion data, location information), corresponding weather forecast data

[2017] Specific operation: The server unpacks the data package and uses the location information to access the weather forecast API to obtain the required data.

[2018] Step 4: Video and emotion analysis

[2019] Input: Decompressed video data, emotion data

[2020] Processing: The server uses a video analysis algorithm to analyze each frame of the video data and extract key environmental features such as buildings, roads, and sky. At the same time, it uses an emotion analysis engine to analyze the user's emotion data and determine the user's emotional state (e.g., joy, anger, sadness, or happiness).

[2021] Output: Environmental feature data, emotion judgment data

[2022] How it works: The server analyzes the video frame by frame and tags it with environmental features. The emotion analysis engine identifies the user's emotional state from their facial expressions and voice.

[2023] Step 5: Run the weather simulation

[2024] Input: Environmental feature data, emotion judgment data, weather forecast data

[2025] Processing: The server uses a trained generative AI model to simulate weather changes based on the acquired weather forecast data and emotion judgment data. The generative AI model takes these data as inputs and generates a visually easy-to-understand weather simulation video.

[2026] Output: Simulation video data

[2027] How it works: The server inputs data into the AI ​​model and runs a simulation process based on weather and emotions.

[2028] Step 6: Generate and provide simulation videos

[2029] Input: Simulation video data

[2030] Processing: The server encodes the generated simulation video and sends it to the user's device.

[2031] Output: Simulation video link sent to user's device

[2032] What happens: The server encodes the video into the appropriate format and sends a download link to the user's device.

[2033] Step 7: Play the video

[2034] Input: Simulation video link sent to user's device

[2035] Processing: The user plays the received simulation video using a dedicated application. The application streams or downloads the video file and plays it.

[2036] Output: Visual weather simulation video

[2037] Specific operation: Users can intuitively check weather changes by pressing the play button within the application and watching a simulation video.

[2038] (Application example 2)

[2039] 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."

[2040] In today's food delivery industry, delivery partners are vulnerable to real-time weather changes. Delivering in bad weather can increase stress and negatively impact delivery efficiency. Traditional weather forecasts alone are insufficient to deal with these situations, and adequate support for delivery partners is lacking. Therefore, there is a need for a system that provides visual real-time weather information along delivery routes and customized information tailored to delivery partners' emotions.

[2041] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2042] In this invention, the server includes means for simulating weather changes at a location specified by a user using artificial intelligence that has learned video from video devices nationwide and weather forecast data, means for receiving video data and location information taken by the user, means for simulating predicted weather changes based on the video data and generating a video, means for analyzing user emotion data and customizing the simulation video based on the analyzed data, and means for providing the generated simulation video to the user. This makes it possible to visually simulate real-time weather changes along a delivery route and provide customized information according to the delivery partner's emotions.

[2043] "Nationwide video equipment" refers to video cameras and fixed cameras installed in various locations, and includes video data acquired from these devices.

[2044] "Weather forecast data" is data intended for weather forecasting, and includes information such as temperature, humidity, precipitation, wind speed, and wind direction.

[2045] "Artificial intelligence" refers to computer systems that use techniques such as machine learning and data analysis to perform specific tasks.

[2046] "Means for simulating weather changes" refers to a device or program that virtually reproduces weather fluctuations based on acquired data.

[2047] "Location information" is data that indicates the physical location of a target object or person, and includes GPS data.

[2048] "Video data" refers to visual information consisting of a series of image frames.

[2049] "Predicted weather changes" means future weather conditions calculated based on acquired weather forecast data.

[2050] "Means for generating" refers to a device or program that creates new data or objects based on some data.

[2051] "Simulation video" refers to a video clip that visualizes predicted weather changes.

[2052] "User emotion data" refers to data that indicates the user's feelings and mental state, obtained through voice analysis, facial expression analysis, and the like.

[2053] "Means for analyzing" refers to a device or program that takes data and extracts useful information from it.

[2054] "Customization means" refers to a device or program that tailors information or functionality to a user's individual needs or circumstances.

[2055] "Means for providing" refers to a device or program that distributes or delivers the generated data or information to the user.

[2056] "Delivery Route" means the route taken by a Delivery Partner when delivering Products.

[2057] "Real-time weather information" refers to data that provides immediate information about current weather conditions.

[2058] "Customized Information" means information that is tailored to a user's particular circumstances and requirements.

[2059] A "delivery partner" refers to a person whose role is to deliver goods to customers in services such as food delivery.

[2060] The system of the present invention enables delivery partners in the food delivery industry to visually understand weather changes in real time and provides information customized to the delivery partner's emotions. This system is realized using smartphones, servers, and artificial intelligence models.

[2061] First, delivery partners use their smartphones to record short videos of their designated delivery route. The recorded video and location information are encoded using data compression technology within the smartphone and sent to a server. At the same time, emotion data collected through the smartphone's camera and microphone is also sent to the server.

[2062] The server temporarily stores the received video data, location information, and emotion data. Based on the location information, it obtains weather forecast data from an external weather forecast API, analyzes the video footage data to extract and tag basic environmental features (e.g., buildings, roads, sky), and uses an emotion engine to analyze the emotion data and identify the delivery partner's current emotional state.

[2063] The server then uses the trained AI model to simulate weather changes based on the acquired weather forecast data and emotion data, generating a simulation video. This simulation incorporates weather effects based on the weather forecast data and effects based on the emotion data. The generated simulation video is encoded and sent to the delivery partner's smartphone.

[2064] Delivery partners can view simulated videos on their smartphones, allowing them to visually check real-time weather changes along their delivery route. The app also provides personalized information based on their emotions, reducing stress and allowing them to carry out their work with peace of mind.

[2065] As a specific example, a delivery partner takes a video of their designated delivery route and uploads the video, location information, and emotional data to a server via an application. Based on this data, the server obtains weather forecast data and performs video and emotional analysis. After that, an AI model simulates weather changes and generates a simulated video that also incorporates emotional effects. This simulated video is sent to the delivery partner and played back via the application.

[2066] Examples of prompts include:

[2067] "Generate a weather simulation from Tokyo Station to Shinagawa Station."

[2068] "Include an encouraging message if your delivery partner is feeling stressed."

[2069] In this way, the system of the present invention provides delivery partners with real-time visual weather information and emotionally customized information, enabling safer and more efficient deliveries.

[2070] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2071] Step 1:

[2072] Shooting and uploading video, location information, and emotional data

[2073] How it works: The user (delivery partner) uses their smartphone to record a short video of their designated delivery route.

[2074] Input: Video captured by a smartphone camera and location information obtained from the smartphone's GPS.

[2075] Output: Video data, location information, and emotional data (user facial expressions and voice data collected by the camera and microphone). This data is compressed and sent to the server.

[2076] Step 2:

[2077] Receiving and storing data

[2078] Operation: The server receives data sent from the user terminal.

[2079] Input: Compressed video data, location information, and emotion data.

[2080] Output: Video data, location information, and emotion data stored on the server.

[2081] Step 3:

[2082] Obtaining weather forecast data

[2083] How it works: The server retrieves weather forecast data from an external weather API based on the saved location information.

[2084] Input: Location.

[2085] Output: Weather forecast data (e.g. temperature, humidity, precipitation, etc.).

[2086] Step 4:

[2087] Video data analysis

[2088] How it works: The server analyzes the video data, extracts and tags basic features of the environment (buildings, roads, sky, etc.).

[2089] Input: Video data.

[2090] Output: Tagged environment feature data.

[2091] Step 5:

[2092] Emotional Data Analysis

[2093] How it works: The server's emotion engine is used to parse the emotion data and identify the user's current emotional state.

[2094] Input: Emotion data.

[2095] Output: Sentiment analysis result (e.g. whether the user is stressed or relaxed).

[2096] Step 6:

[2097] Weather change simulation and video generation

[2098] Operation: Using the server's trained AI model, weather changes are simulated based on the acquired weather forecast data and emotion data, and a simulation video is generated.

[2099] Input: Weather forecast data, environmental feature data, and sentiment analysis results.

[2100] Output: Simulation video (with weather and emotion effects).

[2101] Step 7:

[2102] Encoding and sending simulation videos

[2103] How it works: The server encodes the generated simulation video and sends it to the user's smartphone.

[2104] Input: Simulation video.

[2105] Output: Encoded simulation video.

[2106] Step 8:

[2107] Simulation video playback

[2108] How it works: Users play the simulation video received through a smartphone app and visually check the weather changes along their delivery route.

[2109] Input: Encoded simulation video.

[2110] Output: Simulation video to be played and customization information according to emotions.

[2111] 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.

[2112] 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.

[2113] 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.

[2114] 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.

[2115] 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.

[2116] 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.

[2117] 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).

[2118] 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.

[2119] 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."

[2120] 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.

[2121] 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).

[2122] 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.

[2123] 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.

[2124] 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.

[2125] 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.

[2126] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another 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.

[2127] 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.

[2128] 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.

[2129] 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.

[2130] 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.

[2131] 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.

[2132] The following is further disclosed regarding the above embodiment.

[2133] (Claim 1)

[2134] A means for simulating weather changes at a location designated by a user using artificial intelligence that has learned weather forecast data and video from video devices across the country;

[2135] A means for receiving video data and location information captured by a user;

[2136] means for generating a video by simulating predicted weather changes based on the video data;

[2137] a means for providing the generated simulation video to a user;

[2138] A system including:

[2139] (Claim 2)

[2140] 2. The system according to claim 1, further comprising means for compressing video data and location information taken by a user and transmitting the compressed data to said artificial intelligence.

[2141] (Claim 3)

[2142] 2. The system according to claim 1, wherein the simulation video generating means applies a weather effect to each frame of the video data shot based on weather forecast data.

[2143] "Example 1"

[2144] (Claim 1)

[2145] A means for using a user's device to take a short video of a location designated by the user, compressing the video and location information, and transmitting the compressed video and location information to a server;

[2146] A means for temporarily storing the video and location information received by the server and obtaining corresponding weather forecast data from an external weather forecast service;

[2147] The server analyzes the video data, extracts basic environmental features, and tags them.

[2148] A means for simulating weather changes based on the acquired weather forecast data and analysis results using a trained generative AI model, and generating a simulation video;

[2149] means for encoding the generated simulation video and transmitting it to a user's terminal;

[2150] A system including:

[2151] (Claim 2)

[2152] 2. The system according to claim 1, further comprising means for compressing video data and location information captured by a user and transmitting the compressed data to a server.

[2153] (Claim 3)

[2154] 2. The system according to claim 1, wherein the simulation video generating means applies a weather effect to each frame of the video data shot based on weather forecast data.

[2155] "Application Example 1"

[2156] (Claim 1)

[2157] A means for simulating weather changes at a location designated by a user using artificial intelligence that has learned weather forecast data and video from video devices across the country;

[2158] A means for receiving video data and location information captured by a user;

[2159] means for generating a video by simulating predicted weather changes based on the video data;

[2160] a means for providing the generated simulation video to a user;

[2161] A means for users to shoot video along their food delivery route and simulate weather changes;

[2162] A means to judge the safety and efficiency of delivery routes based on the generated simulation video, and

[2163] A system including:

[2164] (Claim 2)

[2165] 2. The system according to claim 1, further comprising means for compressing video data and location information taken by a user and transmitting the compressed data to said artificial intelligence.

[2166] (Claim 3)

[2167] 2. The system according to claim 1, wherein the simulation video generating means applies a weather effect to each frame of the video data shot based on weather forecast data.

[2168] "Example 2: Combining Emotion Engines"

[2169] (Claim 1)

[2170] means for obtaining external weather forecast data using the received video data and location information to simulate weather changes at a location specified by a user;

[2171] A means for analyzing user emotions and customizing weather simulations based on the emotion data;

[2172] A means for generating videos by simulating weather changes based on weather forecast data and emotion data using a trained artificial intelligence model;

[2173] a means for providing the generated simulation video to a user;

[2174] A system including:

[2175] (Claim 2)

[2176] 2. The system according to claim 1, further comprising means for compressing and transmitting the video data, the position information, and the emotion data.

[2177] (Claim 3)

[2178] 2. The system according to claim 1, wherein the simulation video generating means applies a weather effect to each frame of the video data shot based on weather forecast data and emotion data.

[2179] "Application example 2 when combining emotion engines"

[2180] (Claim 1)

[2181] A means for simulating weather changes at a location designated by a user using artificial intelligence that has learned weather forecast data and video from video devices across the country;

[2182] A means for receiving video data and location information captured by a user;

[2183] means for generating a video by simulating predicted weather changes based on the video data;

[2184] A means for analyzing user emotion data and customizing a simulation video based on the data;

[2185] a means for providing the generated simulation video to a user;

[2186] A system including:

[2187] (Claim 2)

[2188] 2. The system according to claim 1, further comprising means for compressing video data and location information taken by a user and transmitting the compressed data to said artificial intelligence.

[2189] (Claim 3)

[2190] 2. The system according to claim 1, wherein the simulation video generating means applies a weather effect to each frame of the video data shot based on weather forecast data.

[2191] (Claim 4)

[2192] 10. The system of claim 1, further comprising means for visually simulating real-time weather changes along a delivery route.

[2193] (Claim 5)

[2194] 10. The system of claim 1, further comprising means for analyzing delivery partner emotion data and customizing information as needed. [Explanation of symbols]

[2195] 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. A means for simulating weather changes at a location designated by a user using artificial intelligence that has learned weather forecast data and video from video devices across the country; A means for receiving video data and location information captured by a user; means for generating a video by simulating predicted weather changes based on the video data; a means for providing the generated simulation video to a user; A system including:

2. The system according to claim 1 , further comprising means for compressing video data and location information taken by a user and transmitting the compressed data to said artificial intelligence.

3. 2. The system according to claim 1, wherein said simulation animation generating means applies a weather effect to each frame of the captured animation data based on weather forecast data.

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