System

Abstract

A system is provided.SOLUTION: A system, comprising: a marine antenna means for collecting marine information in real time; a pre-processing means for pre-processing the collected marine information; a communication means for uploading the pre-processed marine information to a server in a cloud; a database means for storing the uploaded marine information; a multi-modal AI means for analyzing the stored marine information; and a information providing means for providing an analysis result to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Modern ocean data collection technology is important in a variety of fields, including navigation, fishing, and resource development. However, due to a lack of data collection technology, its accuracy, and public-private collaboration, there are challenges in that sufficient data is not shared in real time. This hinders efforts to improve safety and efficiency using ocean data. The present invention aims to solve these challenges and provide more accurate, real-time ocean data. [Means for solving the problem]

[0005] The present invention provides a system including a marine antenna means for collecting marine data in real time, a preprocessing means for initially processing the collected marine data, a communication means for uploading the preprocessed data to a server on the cloud, a database means for storing the uploaded data, a multimodal AI means for analyzing the stored data, and a data providing means for providing the analysis results to users. This system improves the accuracy of marine data and enables real-time sharing, thereby improving navigation safety and enabling efficient fisheries management.

[0006] "Marine antenna means" means an antenna device or system for collecting marine data in real time.

[0007] "Preprocessing means" refers to a device or system that initially processes collected oceanographic data, removes noise, and standardizes data formats.

[0008] "Communication means" refers to a communication device or system for uploading pre-processed data to a server on the cloud.

[0009] "Database means" refers to a database system for storing and managing data uploaded to the cloud server.

[0010] A "multimodal AI method" is an artificial intelligence system that integrates multiple sensor information to analyze stored data.

[0011] The "data providing means" is a device or system for providing the analysis results to the user.

[0012] "Ocean data" refers to various physical data related to the marine environment, such as ocean currents, wave height, and wave direction.

[0013] A "cloud server" is a server that can be accessed via the Internet and is a system for storing, managing, and analyzing data.

[0014] "User" refers to a person or system that uses the provided analysis data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0023] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0036] The present invention is a system for collecting, processing, and analyzing oceanographic data and providing it to users in real time. An embodiment of this system will be described in detail below.

[0037] System Overview

[0038] The system consists of the following main components:

[0039] 1. Marine Antenna Means

[0040] 2. Pretreatment Methods

[0041] 3. Means of communication

[0042] 4. Database Means

[0043] 5. Multimodal AI Methods

[0044] 6. Means of providing data

[0045] Marine Antenna Means

[0046] The marine antenna means is a device for collecting marine data such as ocean currents, wave height, and wave direction in real time. For example, data is acquired using various sensors installed on the sea surface. This data is first stored in temporary memory within the antenna.

[0047] Pretreatment means

[0048] Pre-processing involves processes to remove noise from the collected raw data and convert it into a standard format, which is suitable for subsequent analysis and interpretation.

[0049] communication means

[0050] The communication means includes a communication module for uploading pre-processed data to a cloud server. Communication is mainly via LTE or satellite communications, which enables real-time data transmission even in remote locations.

[0051] Database Means

[0052] The database means is a database placed on a cloud server, which stores and manages data uploaded in real time. The data is recorded in an orderly format and is designed to be immediately accessible when needed.

[0053] Multimodal AI methods

[0054] Multimodal AI tools are artificial intelligence systems that integrate multiple sensor information and perform highly accurate analysis to generate detailed models and predictions of the marine environment. These AI systems also integrate data from sonar and drones to provide richer information.

[0055] Data provision method

[0056] The data provision means is an interface for providing analyzed data to users, who can view the analysis results in real time via their PCs or smartphones.

[0057] A natural language description of what the program does

[0058] The processing flow of the system program and its specific operations will be explained below.

[0059] 1. Ocean data collection (terminal)

[0060] The terminal acquires oceanographic data in real time through an ocean antenna. For example, the terminal reads ocean current data in the South Pacific Ocean from a sensor.

[0061] 2. Initial processing of data (terminal)

[0062] The device removes noise from the data it acquires and standardizes the format. For example, the device filters wave height data and converts it into a standard format.

[0063] 3. Real-time upload (terminal)

[0064] The device uploads the data to the cloud server. For example, the device transmits the pre-processed data to the server using an LTE connection.

[0065] 4. Data storage (server)

[0066] The server stores the received data in a database. For example, the server records ocean current data for the South Pacific Ocean in an SQL database.

[0067] 5. Data analysis (server)

[0068] The server uses multimodal AI to integrate information from multiple sensors and perform detailed analysis. For example, the server integrates sonar and drone data to generate a highly accurate ocean current model.

[0069] 6. Data Provision (Server & User)

[0070] The server provides the analysis results to the user, who then views the data on their smartphone or PC. For example, the user can check recommended navigation route information in real time on their smartphone.

[0071] Specific examples

[0072] Example 1: Navigation support system

[0073] The device collects ocean current data in the Pacific Ocean and uploads it to a cloud server, where it is analyzed and provided to the user's e-navigator app as an optimized route for navigation.

[0074] Example 2: Tsunami observation system

[0075] When the device detects abnormal wave height, it reports it to the cloud in real time. The server analyzes this data and generates a tsunami warning, allowing users to immediately begin evacuation.

[0076] Example 3: Fisheries Management System

[0077] The device acquires fish detection data from the coastal waters and uploads it to the cloud, where the server analyzes the data and provides information on optimal fishing spots to the user's smartphone.

[0078] As described above, the system of the present invention can provide users with highly accurate, real-time information by consistently performing the collection, analysis, and provision of marine data.

[0079] The processing flow will be explained below.

[0080] Step 1:

[0081] The device uses an ocean antenna to collect ocean data such as ocean currents, wave height, and wave direction. The device acquires the data via sensors installed in the ocean and stores it in temporary memory. Specifically, the sensors measure the wave height on the ocean surface and transmit the data to the device.

[0082] Step 2:

[0083] This performs initial processing of raw data collected by the device. First, noise is removed through filtering. Then, a conversion process is performed to standardize the data format. For example, data collected at irregular time intervals is standardized to a timestamp with a regular interval.

[0084] Step 3:

[0085] The device uploads the preprocessed data to a cloud server. The device establishes a secure connection using LTE or satellite communications and transmits the data via HTTPS. For example, ocean current data for the South Pacific Ocean is uploaded to the cloud.

[0086] Step 4:

[0087] The server receives the uploaded data, checks the integrity of the data, and checks for any abnormalities. If there are no problems, the server records the data in a database.

[0088] Step 5:

[0089] The server stores the received data in a database. The stored data is used for subsequent analysis. The server also backs up the stored data in parallel to prevent data loss.

[0090] Step 6:

[0091] The server launches the multimodal AI and begins analyzing the data. The server integrates information from multiple sensors, such as sonar and drones, to generate a detailed model of the marine environment. For example, it analyzes flow patterns using multiple ocean current data.

[0092] Step 7:

[0093] The server then stores the analysis results in a database, which includes detailed information on the marine environment and forecast data. This data is then managed so that users can access it in real time.

[0094] Step 8:

[0095] The server receives requests from users, who then use their smartphones or PCs to send requests for data and analysis results for specific ocean areas.

[0096] Step 9:

[0097] The server retrieves the appropriate data from a database based on the request, and returns it immediately to the user.

[0098] Step 10:

[0099] Users can view the provided data on their smartphones or PCs. For example, they can open a smartphone app and check current ocean current information and the optimal navigation route for their vessel. Based on the analysis results, users can navigate safely and conduct efficient fishing activities.

[0100] Example 1

[0101] 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."

[0102] There is a need for a means to quickly and accurately grasp changes in the marine environment, but existing technologies have difficulty in collecting and analyzing data in real time. Furthermore, there is a lack of a consistent system for effectively processing, storing, analyzing, and providing collected data, which means that users cannot obtain the information they need in a timely manner.

[0103] 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.

[0104] In this invention, the server includes a collection device means for collecting oceanographic data in real time, a preprocessing device means for initially processing the collected oceanographic data, a communication device means for uploading the preprocessed data to a centralized processing device on the cloud, a recording device means for saving the uploaded data, a multi-mode compatible artificial intelligence device means for analyzing the saved data, and an information providing device means for providing the analysis results to users, thereby enabling efficient collection, processing, analysis, and provision of real-time observation data of the marine environment.

[0105] The "collection device means" is a device for collecting oceanographic data such as ocean currents, wave heights, and wave directions in real time.

[0106] The "preprocessing device means" is a device for initially processing collected oceanographic data, removing noise, and standardizing the data format.

[0107] The "communication device means" is a device for uploading pre-processed data to a centralized processing device on the cloud.

[0108] "Recording device means" refers to a device for storing uploaded data.

[0109] The "multi-modal artificial intelligence device means" is an artificial intelligence device that can integrate multiple sensor information to analyze stored data.

[0110] The "information providing device means" is a device for providing the analysis results to the user.

[0111] This invention is a system for collecting, processing, and analyzing oceanographic data and providing it to users in real time. An embodiment of this system will be described in detail below.

[0112] System Overview

[0113] The system consists of the following main components:

[0114] 1. Collection Device Means

[0115] 2. Pretreatment Device Means

[0116] 3. Communication Device Means

[0117] 4. Recording Device Means

[0118] 5. Multimodal Artificial Intelligence Device Means

[0119] 6. Information provision device means

[0120] Collection Device Means

[0121] The collection device means is a device for collecting oceanographic data such as ocean currents, wave height, and wave direction in real time. For example, data is acquired using various sensors (ocean current sensors, wave height meters, etc.) installed on the sea surface. This data is first stored in temporary memory within the collection device.

[0122] Pretreatment device means

[0123] The preprocessing means involves processes to remove noise from the collected raw data and convert it into a standard format. The preprocessed data is then standardized into a form suitable for subsequent analysis. Software for noise filtering algorithms and formatting is used.

[0124] communication device means

[0125] The communication device means includes a communication module for uploading pre-processed data to a centralized processing unit on the cloud. Communication is mainly performed via LTE or satellite communication, which enables real-time data transmission even in remote locations.

[0126] Recording device means

[0127] The recording device is a database stored on a cloud server, which stores and manages the data uploaded in real time. The data is recorded in an orderly format and is designed to be immediately accessible when needed.

[0128] Multimodal artificial intelligence device means

[0129] The multi-modal AI system is an AI system that integrates multiple sensor information and performs highly accurate analysis to generate detailed models and predictions of the marine environment. This AI system also integrates data from sonar and drones to provide richer information. For example, deep learning frameworks such as TensorFlow and PyTorch are used.

[0130] Information providing device means

[0131] The information providing device means is an interface for providing analyzed data to users. Users can view the analysis results in real time via a PC or smartphone. Data is provided via a web interface or mobile application.

[0132] Specific examples

[0133] Example 1: Navigation support system

[0134] The device collects ocean current data in the Pacific Ocean and uploads it to a cloud server, where it is analyzed and provided to the user's navigation app as an optimal route.

[0135] Example 2: Tsunami observation system

[0136] When the device detects abnormal wave height, it reports it to the cloud in real time. The server analyzes this data and generates a tsunami warning, allowing users to immediately begin evacuation.

[0137] Example 3: Fisheries Management System

[0138] The device acquires fish detection data from the coastal waters and uploads it to the cloud, where the server analyzes the data and provides information on optimal fishing spots to the user's smartphone.

[0139] Prompt Sentence Examples

[0140] TXT

[0141] We have developed a system that collects, processes, analyzes, and provides real-time oceanographic data to users. This system consists of six main components: collection device means, pre-processing device means, communication device means, recording device means, multi-mode AI device means, and information provision device means. Please explain in detail.

[0142] 1. Oceanographic data collection

[0143] 2. Initial data processing

[0144] 3. Real-time uploading

[0145] 4. Data storage

[0146] 5. Data Analysis

[0147] 6. Data provision

[0148] As described above, the system of the present invention can provide users with highly accurate, real-time information by consistently performing the collection, analysis, and provision of oceanographic data.

[0149] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0150] The flow of this system's program processing

[0151] Step 1: Collecting oceanographic data (terminal)

[0152] Specific description:

[0153] The terminal uses an ocean antenna to collect ocean data in real time. Specifically, data is acquired through various sensors (e.g., ocean current sensors, wave height meters, etc.) and the collected data is stored in temporary memory.

[0154] Input and Output:

[0155] Input: Oceanographic data acquired by sensors (e.g., ocean currents, wave height, wave direction)

[0156] Output: Raw data stored in temporary memory

[0157] Specific behavior:

[0158] The device retrieves the latest ocean current data at a specified point in the South Pacific.

[0159] The sensor measures the seawater temperature and salinity at regular intervals (such as every second) and transmits the data to the terminal.

[0160] Step 2: Initial processing of data (terminal)

[0161] Specific description:

[0162] The terminal removes noise from the collected data and standardizes the format. Using noise filtering algorithms and formatting standardization software, the raw data is converted into a standard format.

[0163] Input and Output:

[0164] Input: raw data collected

[0165] Output: Denoised and uniformly formatted data

[0166] Specific behavior:

[0167] The terminal uses a moving average filter to remove outliers from the collected wave height data.

[0168] Convert seawater temperature data into CSV format.

[0169] Step 3: Real-time upload (terminal)

[0170] Specific description:

[0171] The device uploads the preprocessed data to the cloud server. The data is sent to the cloud server via wireless communication (LTE or satellite communication).

[0172] Input and Output:

[0173] Input: Preprocessed data

[0174] Output: Data uploaded to the cloud server

[0175] Specific behavior:

[0176] The terminal uses an LTE line to send preprocessed wave height data to a cloud server.

[0177] The device uploads new data to the server in bulk every minute.

[0178] Step 4: Storing data (server)

[0179] Specific description:

[0180] The server stores the received data in a database, and the cloud server immediately records and indexes the data received for quick access.

[0181] Input and Output:

[0182] Input: Data uploaded to the cloud server

[0183] Output: Data stored in the database

[0184] Specific behavior:

[0185] The server receives new ocean current data and records it in the SQL database using INSERT statements.

[0186] Add a timestamp when saving data.

[0187] Step 5: Analyzing the data (server)

[0188] Specific description:

[0189] The server uses multi-modal artificial intelligence to integrate and perform detailed analysis of the multiple sensor data, generating detailed models and predictions of the marine environment.

[0190] Input and Output:

[0191] Input: Saved data

[0192] Output: Analyzed data and predictive models

[0193] Specific behavior:

[0194] The server uses TensorFlow and PyTorch to integrate sonar and drone data to generate a highly accurate ocean current model.

[0195] The AI ​​model makes future predictions about the marine environment and stores the results on a server.

[0196] Step 6: Providing Data (Server & User)

[0197] Specific description:

[0198] The server provides the analysis results to the user, who then views the data, which is provided through a web interface or mobile application.

[0199] Input and Output:

[0200] Input: Parsed data

[0201] Output: Data presented in a user-viewable format

[0202] Specific behavior:

[0203] The server provides the analysis results through web applications and mobile applications.

[0204] A user checks recommended navigation route information on their smartphone.

[0205] (Application example 1)

[0206] 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."

[0207] One of the challenges facing modern food delivery systems is a lack of technology to efficiently and quickly deliver fresh seafood. While it is possible to collect oceanographic data and use it to identify optimal fishing grounds, there is no system in place to use that information to quickly deliver fresh seafood to consumers. In particular, there is a need for technology that optimizes the supply chain from fisheries to consumers by calculating efficient delivery routes using real-time oceanographic data and controlling delivery methods such as drones.

[0208] 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.

[0209] In this invention, the server includes marine antenna means for collecting marine data in real time, preprocessing means for initially processing the collected marine data, communication means for uploading the preprocessed data to a server on the cloud, database means for storing the uploaded data, multimodal AI means for analyzing the stored data, data providing means for providing the analysis results to a user, delivery means for efficiently delivering fresh seafood using the data providing means, and route calculation means for controlling the delivery means and calculating an optimal delivery route. This enables efficient and prompt delivery of fresh seafood based on the marine data collected in real time.

[0210] The "marine antenna means" is a device for collecting marine data such as ocean currents, wave heights, and wave directions in real time.

[0211] A "pre-processing means" is a device that includes a process for removing noise from collected raw data and converting it into a standard format.

[0212] The "communication means" is a communication module for uploading preprocessed data to a server on the cloud.

[0213] "Database means" refers to a database placed on a cloud server, and is a system that stores and manages data uploaded in real time.

[0214] "Multimodal AI means" is an artificial intelligence system that integrates information from multiple sensors and performs highly accurate analysis.

[0215] The "data providing means" is an interface for providing analyzed data to the user.

[0216] "Delivery methods" refer to logistics methods for efficiently delivering fresh seafood, including drones and vehicles.

[0217] The "route calculation means" is a system for controlling delivery means and calculating the optimal delivery route.

[0218] This invention is a marine data collection, analysis and provision system for efficiently providing fresh seafood. The system consists of the following main components:

[0219] Hardware Configuration

[0220] 1. Marine Antenna Means

[0221] The various sensors include sensors that measure ocean currents, wave height, wave direction, etc. These sensor data are acquired in real time and stored in temporary memory.

[0222] 2. Terminal

[0223] The devices include smartphones and drones, which collect, pre-process and upload real-time data.

[0224] 3. Cloud Server

[0225] It is equipped with a database means and a multimodal AI means, and has an interface for providing analysis results to users.

[0226] Software Configuration

[0227] 1. Pretreatment methods

[0228] Software that includes processes for denoising raw data and converting it into a standard format.

[0229] 2. Means of communication

[0230] A communication module for uploading pre-processed data to a cloud server using LTE or satellite communications.

[0231] 3. Database Means

[0232] Software for storing and managing data uploaded in real time in a database on a cloud server.

[0233] 4. Multimodal AI Methods

[0234] An AI system that integrates information from multiple sensors and performs highly accurate analysis. For example, it can integrate data from sonar and drones to generate detailed models and predictions of the marine environment.

[0235] 5. Means of providing data

[0236] Software with an interface for providing analysis results to users.

[0237] 6. Delivery method

[0238] Logistics methods such as drones and vehicles to efficiently deliver fresh seafood.

[0239] 7. Route Calculation Method

[0240] A system that controls delivery means and calculates the optimal delivery route.

[0241] Successful examples

[0242] 1. Oceanographic data collection

[0243] Data such as ocean currents, wave height, and wave direction is collected in real time and stored in temporary memory.

[0244] 2. Pretreatment

[0245] The collected data is denoised and converted into a standard format, e.g., wave height data is filtered.

[0246] 3. Upload your data

[0247] The preprocessed data is uploaded to a cloud server via an LTE line or other means.

[0248] 4. Data Analysis

[0249] Multimodal AI is used to integrate data and generate highly accurate ocean models, for example, using sonar and drone data to generate detailed ocean current models.

[0250] 5. Data provision

[0251] The analysis results are provided to users, who can view real-time data via their smartphones and determine the optimal route for delivering fresh seafood.

[0252] Food delivery as a concrete example

[0253] 1. Optimal fishing ground identification system

[0254] Ocean data is collected and information on optimal fishing spots is uploaded to a cloud server. This information is analyzed and provided to users as optimal fishing spots.

[0255] 2. Real-time fresh seafood delivery system

[0256] Based on oceanographic data collected by drones, fresh seafood is delivered via the optimal route, and users can check the delivery status in real time via their smartphones.

[0257] Prompt Sentence Examples

[0258] Fisheries management system using ocean data. Please implement a function to collect and analyze fishing ground information in real time and provide users with the best fishing ground information.

[0259] As described above, the present invention is a system that can efficiently deliver fresh seafood to users by consistently performing everything from collecting marine data to analyzing it, providing the data, and delivering it along the optimal route.

[0260] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0261] Step 1:

[0262] The terminal collects oceanographic data

[0263] Input: Various sensor data (ocean currents, wave height, wave direction, etc.)

[0264] Specific operation: Acquire ocean data in real time using ocean antenna means. For example, the terminal collects data from a wave direction sensor and stores it in temporary memory.

[0265] Output: Raw data

[0266] Step 2:

[0267] The device preprocesses the data

[0268] Input: Raw data

[0269] Specific operation: Performs noise removal and standard format conversion. For example, it filters the wave height data acquired by the device and converts it into a unified format.

[0270] Output: Preprocessed, consistent data

[0271] Step 3:

[0272] The device uploads the preprocessed data to the cloud server.

[0273] Input: Preprocessed data

[0274] Specific operation: Data is transmitted to a cloud server using LTE or satellite communication. For example, the terminal uploads preprocessed ocean current data to the cloud server.

[0275] Output: Data stored on the cloud server

[0276] Step 4:

[0277] The server stores the data

[0278] Input: Data uploaded to the cloud server

[0279] Specific operation: Data is stored and managed using database means. For example, a cloud server records ocean current data in an SQL database.

[0280] Output: Real-time data stored in a database

[0281] Step 5:

[0282] The server analyzes the data

[0283] Input: Saved data

[0284] How it works: Multimodal AI methods are used to analyze data and generate predictive models. For example, a server integrates data from sonar and drones to generate a highly accurate ocean current model.

[0285] Output: Analysis results and predicted data

[0286] Step 6:

[0287] The server provides the analysis results to the user

[0288] Input: Model and predicted data as analysis results

[0289] Specific operation: The analysis results are provided to the user using the data provision means. For example, the user can view optimal fishing ground information in real time via their smartphone.

[0290] Output: Analysis result data provided to the user

[0291] Step 7:

[0292] The server controls the delivery method

[0293] Input: Parsed oceanographic data and shipping routes

[0294] Specific operation: The route calculation means is used to analyze data and calculate the optimal delivery route. For example, the server calculates and instructs a drone's delivery route to efficiently deliver fresh seafood.

[0295] Output: Optimal delivery route and control instructions

[0296] Step 8:

[0297] Delivery method provides fresh seafood

[0298] Input: Optimal delivery route and control instructions

[0299] Specific operation: Drones and vehicles efficiently deliver fresh seafood. For example, drones deliver fresh seafood to consumers by following optimal routes.

[0300] Output: Fresh seafood delivered to the consumer

[0301] 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.

[0302] The present invention is a system that collects, processes, and analyzes oceanographic data and provides it to users in real time, while also recognizing the user's emotions and dynamically adjusting the content of the data provided. An embodiment of this system will be described in detail below.

[0303] System Overview

[0304] The system consists of the following main components:

[0305] 1. Marine Antenna Means

[0306] 2. Pretreatment Methods

[0307] 3. Means of communication

[0308] 4. Database Means

[0309] 5. Multimodal AI Methods

[0310] 6. Means of providing data

[0311] 7. Emotion Engine

[0312] Marine Antenna Means

[0313] The marine antenna means is a device for collecting marine data such as ocean currents, wave height, and wave direction in real time. For example, data is acquired using various sensors installed on the sea surface. This data is first stored in temporary memory within the antenna.

[0314] Pretreatment means

[0315] Pre-processing involves processes to remove noise from the collected raw data and convert it into a standard format, which is suitable for subsequent analysis and interpretation.

[0316] communication means

[0317] The communication means includes a communication module for uploading pre-processed data to a cloud server. Communication is mainly via LTE or satellite communications, which enables real-time data transmission even in remote locations.

[0318] Database Means

[0319] The database means is a database placed on a cloud server, which stores and manages data uploaded in real time. The data is recorded in an orderly format and is designed to be immediately accessible when needed.

[0320] Multimodal AI methods

[0321] Multimodal AI tools are artificial intelligence systems that integrate multiple sensor information and perform highly accurate analysis to generate detailed models and predictions of the marine environment. These AI systems also integrate data from sonar and drones to provide richer information.

[0322] Data provision method

[0323] The data provision means is an interface for providing analyzed data to users, who can view the analysis results in real time via their PCs or smartphones.

[0324] Emotion Engine

[0325] The emotion engine is a system that recognizes the user's emotions, analyzes the data, and adjusts the display and content of the oceanographic data provided. For example, if the user is stressed, the data will be displayed more simply and easily understandable.

[0326] A natural language description of what the program does

[0327] The processing flow of the system program and its specific operations will be explained below.

[0328] 1. Ocean data collection (terminal)

[0329] The terminal acquires oceanographic data in real time through an ocean antenna. For example, the terminal reads ocean current data in the South Pacific Ocean from a sensor.

[0330] 2. Initial processing of data (terminal)

[0331] The device removes noise from the data it acquires and standardizes the format. For example, the device filters wave height data and converts it into a standard format.

[0332] 3. Real-time upload (terminal)

[0333] The device uploads the data to the cloud server. For example, the device transmits the pre-processed data to the server using an LTE connection.

[0334] 4. Data storage (server)

[0335] The server receives the uploaded data, checks the integrity of the data, and checks for any abnormalities. If there are no problems, the server records the data in a database.

[0336] 5. Data analysis (server)

[0337] The server uses multimodal AI to integrate information from multiple sensors and generate a detailed model of the marine environment, for example by analyzing flow patterns using multiple ocean current data.

[0338] 6. User Emotion Recognition (Server)

[0339] The server runs an emotion engine to collect and analyze the user's emotion data, for example, to determine whether the user is feeling stressed.

[0340] 7. Coordination of data provision (server)

[0341] The server adjusts how it presents data based on the user's emotions, for example, if the user is feeling stressed, it will present a concise summary of the data.

[0342] 8. Data Provision (Server & User)

[0343] The server provides the analysis results to the user, who then views the data on their smartphone or PC. For example, the user can check real-time oceanographic data and analysis results on their smartphone.

[0344] Specific examples

[0345] Example 1: Navigation support system

[0346] The device collects ocean current data in the Pacific Ocean and uploads it to a cloud server. This data is then analyzed and provided to the user's e-navigator app as an optimized route. Furthermore, an emotion engine recognizes the user's stress level and adjusts the displayed data accordingly.

[0347] Example 2: Tsunami observation system

[0348] When the device detects abnormal wave height, it reports it to the cloud in real time. The server analyzes this data and generates a tsunami warning. The emotion engine detects the user's emotions and displays evacuation instructions more intuitively, allowing the user to immediately begin evacuation.

[0349] Example 3: Fisheries Management System

[0350] The device collects fish detection data from the coastal waters and uploads it to the cloud. The server analyzes the data and provides optimal fishing spot information to the user's smartphone. The emotion engine analyzes the user's emotions and adjusts the priority and presentation of the information.

[0351] As described above, the system of the present invention provides optimal information to users by consistently performing all steps from collecting marine data to analyzing and providing it, as well as recognizing and adjusting user emotions.

[0352] The processing flow will be explained below.

[0353] Step 1:

[0354] The device uses an ocean antenna to collect ocean data such as ocean currents, wave height, and wave direction. The device acquires the data via sensors installed in the ocean and stores it in temporary memory. Specifically, the sensors measure the wave height on the ocean surface and transmit the data to the device.

[0355] Step 2:

[0356] This performs initial processing of raw data collected by the device. First, noise is removed through filtering. Then, a conversion process is performed to standardize the data format. For example, data collected at irregular time intervals is standardized to a timestamp with a regular interval.

[0357] Step 3:

[0358] The device uploads the preprocessed data to a cloud server. The device establishes a secure connection using LTE or satellite communications and transmits the data via HTTPS. For example, ocean current data for the South Pacific Ocean is uploaded to the cloud.

[0359] Step 4:

[0360] The server receives the uploaded data, checks the integrity of the data, and checks for any abnormalities. If there are no problems, the server records the data in a database.

[0361] Step 5:

[0362] The server stores the received data in a database. The stored data is used for subsequent analysis. The server also backs up the stored data in parallel to prevent data loss.

[0363] Step 6:

[0364] The server launches the multimodal AI and begins analyzing the data. The server integrates information from multiple sensors, such as sonar and drones, to generate a detailed model of the marine environment. For example, it analyzes flow patterns using multiple ocean current data.

[0365] Step 7:

[0366] The server then stores the analysis results in a database, which includes detailed information on the marine environment and forecast data. This data is then managed so that users can access it in real time.

[0367] Step 8:

[0368] The server starts the emotion engine, collects and analyzes the user's emotion data, and evaluates the user's state based on the emotion data obtained from the user's smartphone or PC. For example, it determines whether the user is feeling stressed.

[0369] Step 9:

[0370] The server dynamically adjusts the way data is presented based on the user's emotions. For example, if the user is feeling stressed, the server will avoid displaying complex data and provide simple, visually easy-to-understand information.

[0371] Step 10:

[0372] The server receives requests from users. Users use their smartphones or PCs to send requests for data and analysis results for specific ocean areas. For example, a user requests detailed data on a particular fishing ground.

[0373] Step 11:

[0374] The server retrieves the appropriate data from the database based on the request, and immediately returns the retrieved data to the user. For example, it retrieves the latest ocean current data for the South Pacific and provides it to the user.

[0375] Step 12:

[0376] Users can view the provided data in real time on their smartphones or PCs. For example, a user can open a smartphone app and check data for optimizing navigation routes or managing fisheries. The app takes into account the user's emotional state and displays the necessary information in an appropriate format.

[0377] Example 2

[0378] 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."

[0379] Conventional oceanographic data collection systems have the problem that the analysis and provision of acquired data is one-way, and they are unable to respond flexibly to the user's emotions or circumstances. Furthermore, when collecting and analyzing data in real time, managing huge amounts of data and ensuring the speed of analysis becomes an issue. This makes it difficult to provide users with the information they need, and there has been a demand for improved usability.

[0380] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0381] In this invention, the server includes marine antenna means for collecting marine data in real time, preprocessing means for initially processing the collected marine data, communication means for uploading the preprocessed data to a server on the cloud, database means for saving the uploaded data, multimodal AI means for analyzing the saved data, data providing means for providing the analysis results to the user, and emotion engine means for analyzing the user's emotions and dynamically adjusting the content to be provided. This makes it possible to effectively collect and analyze data in real time and to appropriately provide necessary information based on the user's emotions.

[0382] The "marine antenna means" is a device for collecting marine data such as ocean currents, wave heights, and wave directions in real time.

[0383] A "pre-processing means" is a device that includes a process for removing noise from collected raw data and converting it into a standard format.

[0384] A "communication means" is a device that includes a communication module for uploading preprocessed data to a server on the cloud.

[0385] "Database means" refers to a database placed on a cloud server, and is a device for storing and managing data uploaded in real time.

[0386] A "multimodal AI means" is a device that includes an artificial intelligence system for integrating information from multiple sensors and performing highly accurate analysis.

[0387] A "data providing means" is a device that includes an interface for providing analyzed data to a user.

[0388] The "emotion engine means" is a device that includes a system that recognizes the user's emotions, analyzes the data, and dynamically adjusts the display method and content of the oceanographic data provided.

[0389] The system of this invention collects, processes, and analyzes oceanographic data, and provides it to users in real time, while also recognizing the user's emotions and dynamically adjusting the content provided. The system consists of the following main components:

[0390] Components

[0391] 1. Marine Antenna Means

[0392] The marine antenna means is a device for collecting marine data such as ocean currents, wave height, and wave direction in real time. For example, it uses various sensors installed on the sea surface. This data is first stored in a temporary memory within the antenna.

[0393] 2. Pretreatment Methods

[0394] Pre-processing involves processes to remove noise from the collected raw data and convert it into a standard format, which is suitable for subsequent analysis and interpretation.

[0395] 3. Means of communication

[0396] The communication means includes a communication module for uploading pre-processed data to a cloud server. Communication is mainly via LTE or satellite communications, which enables real-time data transmission even in remote locations.

[0397] 4. Database Means

[0398] The database means is a database placed on a cloud server, which stores and manages data uploaded in real time. The data is recorded in an orderly format and is designed to be immediately accessible when needed.

[0399] 5. Multimodal AI Methods

[0400] Multimodal AI methods are artificial intelligence systems that integrate multiple sensor information and perform highly accurate analysis to generate detailed models and predictions of the marine environment. These AI systems are built using TensorFlow and PyTorch, for example. They also integrate data from sonar and drones to provide richer information.

[0401] 6. Means of providing data

[0402] The data provision means is an interface for providing analyzed data to users, who can view the analysis results in real time via their PCs or smartphones.

[0403] 7. Emotional Engine Means

[0404] The emotion engine is a system that recognizes the user's emotions, analyzes the data, and dynamically adjusts the display method and content of the oceanographic data provided. For example, if the user is under stress, the data will be displayed in a more concise and easy-to-understand manner. The emotion recognition algorithm uses IBM Watson's emotion analysis.

[0405] Specific examples

[0406] Example 1: Navigation support system

[0407] The device collects ocean current data in the Pacific Ocean and uploads it to a cloud server. This data is analyzed by the server and provided to the user's navigation support app as an optimal route. In addition, an emotion engine recognizes the user's stress level and briefly adjusts the data presented.

[0408] Example prompt: "Based on ocean current data collected in the Pacific Ocean, provide the best sailing route. Also, assess the user's stress level and present the data in a concise format."

[0409] Example 2: Tsunami observation system

[0410] When the device detects abnormal wave height, it reports it to the cloud in real time. The server analyzes this data and generates a tsunami warning. The emotion engine analyzes the user's emotions and displays evacuation instructions more intuitively.

[0411] Example prompt: "If abnormal wave heights are detected, generate a real-time tsunami warning and provide evacuation instructions to the user in an intuitive manner."

[0412] Example 3: Fisheries Management System

[0413] The device collects fish detection data from the coastal waters and uploads it to the cloud. The server analyzes the data and provides optimal fishing spot information to the user's smartphone. The emotion engine analyzes the user's emotions and adjusts the priority and presentation of the information.

[0414] Example prompt: "Based on inshore fish detection data, provide information on optimal fishing spots. Also, assess user sentiment and adjust how the information is presented."

[0415] As described above, the system of the present invention provides optimal information to users by consistently performing all steps from collecting marine data to analyzing and providing it, as well as recognizing and adjusting user emotions.

[0416] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0417] Step 1:

[0418] Ocean data collection (terminal)

[0419] The terminal acquires oceanographic data in real time through an ocean antenna. Specifically, the terminal acquires data such as ocean currents, wave height, and wave direction from sensors installed in the Pacific Ocean at one-second intervals and records it in temporary memory. The input at this time is raw data from each sensor, and the output is raw oceanographic data stored in temporary memory.

[0420] Step 2:

[0421] Initial data processing (terminal)

[0422] The terminal removes noise from the acquired data and standardizes the format. In operation, the terminal uses a filtering algorithm to remove noise from the raw data and then converts the data into a standard format (e.g., CSV format). The input is the raw oceanographic data saved in step 1, and the output is the data that has been removed and converted into a standard format.

[0423] Step 3:

[0424] Real-time upload (terminal)

[0425] The device uploads the preprocessed data to a cloud server. Specifically, the device uses an LTE or satellite communication module to transmit the filtered data to the cloud server in packets every minute. The input is the preprocessed data, and the output is the data transmitted to the cloud server.

[0426] Step 4:

[0427] Data storage (server)

[0428] The server receives the data uploaded to the cloud, verifies it, and then records it in a database. The server then checks the integrity of the data it receives to ensure there are no outliers or missing data. It then stores the data in a database (e.g., Amazon RDS). The input at this time is the data uploaded to the cloud server, and the output is the data stored in the database, the integrity of which has been confirmed.

[0429] Step 5:

[0430] Data analysis (server)

[0431] The server uses multimodal AI to integrate multiple sensor information and generate a detailed marine environment model. Specifically, the server integrates collected ocean current and wave data and runs an AI model using TensorFlow and PyTorch, thereby generating ocean current patterns and predictions. The input is sensor data whose consistency has been confirmed, and the output is an analyzed marine environment model.

[0432] Step 6:

[0433] User emotion recognition (server)

[0434] The server launches an emotion engine to collect and analyze the user's emotional data. In operation, the server uses an emotion recognition algorithm (for example, IBM Watson's emotion analysis) to analyze the user's voice input and text messages and determine their stress level and relaxation level. The input is the user's emotional data, and the output is analyzed emotional information.

[0435] Step 7:

[0436] Coordination of data provision (server)

[0437] The server adjusts how it presents data based on the user's emotions. Specifically, it adjusts the amount and format of data presented based on the results obtained from the emotion engine. If the user is feeling stressed, it uses simple, visually easy-to-understand graphs and infographics. The inputs are the analyzed emotion information and the marine environment model, and the output is the adjusted display format of the data.

[0438] Step 8:

[0439] Data provision (server & user)

[0440] The server provides the analysis results to the user, who then views the data on their smartphone or PC. Specifically, the server sends the analysis results to the user's application via a Web API, and the user can view real-time oceanographic data and analysis results through a smartphone app or PC web browser. The input is data in an adjusted display format, and the output is the analysis results displayed on the user's device.

[0441] (Application example 2)

[0442] 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."

[0443] Conventional seafood delivery systems have difficulty effectively utilizing oceanographic data, making it impossible to grasp the status of fishing grounds and environmental changes in real time. This makes it difficult to efficiently supply fresh seafood and to optimize deliveries. Furthermore, the system is unable to adjust information provision based on the user's emotions, often resulting in a poor user experience. These issues need to be resolved.

[0444] 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.

[0445] In this invention, the server includes a marine antenna means for collecting marine data in real time, a preprocessing means for initially processing the collected marine data, a communication means for uploading the preprocessed data to a server on the cloud, a database means for saving the uploaded data, a multimodal AI means for analyzing the saved data, a data provision means for providing the analysis results to the user, an emotion engine that recognizes the user's emotions and dynamically adjusts the data provision method, and a route optimization means for using the analyzed marine data to support food delivery via the optimal route. This enables the server to analyze the marine data and provide information on optimal fishing grounds, as well as provide information based on the user's emotions, thereby realizing efficient delivery of fresh seafood that satisfies the user.

[0446] The "marine antenna means" is a device for collecting marine data such as ocean currents, wave heights, and wave directions in real time.

[0447] A "pre-processing means" is a device that includes a process for removing noise from collected raw data and converting it into a standard format.

[0448] A "communication means" is a device that includes a communication module for uploading preprocessed data to a server on the cloud.

[0449] "Database means" refers to a database placed on a cloud server, and is a device that serves to store and manage data uploaded in real time.

[0450] "Multimodal AI means" is an artificial intelligence system that integrates information from multiple sensors and performs highly accurate analysis.

[0451] The "data providing means" is an interface for providing analyzed data to the user.

[0452] An "emotion engine" is a system that recognizes a user's emotions, analyzes that data, and dynamically adjusts the display method and content of the information provided.

[0453] The "route optimization tool" is a device that has the function of supporting the procurement and delivery of fresh seafood along the optimal route based on analyzed oceanographic data.

[0454] The system for realizing the present invention comprises the following main components:

[0455] Oceanographic data collection

[0456] The server collects oceanographic data in real time from various sensors installed on the ocean surface, such as data on ocean currents, wave height, wave direction, etc. This data is first collected by an ocean antenna means and stored in temporary memory.

[0457] Initial processing of data

[0458] The server uses pre-processing means to remove noise from the collected data and convert it into a standard format, specifically filtering the wave height data and standardizing it into a format suitable for analysis.

[0459] Uploading data to the cloud

[0460] The preprocessed data is uploaded to a cloud server using a communication method, mainly via LTE or satellite communication, which enables real-time data transmission even from remote locations.

[0461] Data storage

[0462] The cloud server stores the uploaded data using a database system, which is designed to record the data in an organized format and make it instantly accessible when needed.

[0463] Data analysis

[0464] The server analyzes the stored data using multimodal AI methods, integrating information from multiple sensors to generate highly accurate marine environment models and predictions, enabling the provision of richer information.

[0465] User Emotion Recognition

[0466] The server uses an emotion engine to collect and analyze the user's emotional data, for example to determine whether the user is feeling stressed, and dynamically adjusts the display and content of the information provided to the user based on this data.

[0467] Customization of information provided

[0468] When a user views the analysis results on a PC or smartphone, the server takes into account the user's emotions and adjusts the display to display information concisely and intuitively, improving the user experience.

[0469] Fresh seafood delivery assistance

[0470] Based on the analysis results, route optimization methods are used to assist food delivery, providing information on the best fishing spots and enabling the fastest delivery of fresh seafood via the most optimal route.

[0471] Specific examples

[0472] As a concrete example, consider a fresh seafood delivery support system. The user's smartphone app displays information on optimal fishing grounds based on oceanographic data collected in real time. Based on this information, delivery personnel can purchase and deliver seafood along the optimal route. In addition, an emotion engine analyzes the user's emotions and adjusts the way information is displayed, allowing the user to obtain information without feeling stressed.

[0473] Prompt Sentence Examples

[0474] Here is an example of a prompt for generating code using a generative AI model:

[0475] I am implementing a seafood delivery support app. Please generate code for the following functions:

[0476] 1. Ability to collect and process oceanographic data in real time

[0477] 2. Ability to upload data to the cloud

[0478] 3. Function to analyze data and provide optimal fishing ground information

[0479] 4. Ability to customize information display based on user emotional data

[0480] 5. A feature that provides optimal routes for delivering fresh seafood.

[0481] Please provide the specific code and explanation for each function.

[0482] In this way, the system of the present invention consistently collects, analyzes, and provides marine data, as well as recognizes and adjusts user emotions, thereby providing optimal information to users and enabling the efficient and effective delivery of fresh seafood.

[0483] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0484] Step 1:

[0485] The terminal collects oceanographic data in real time through an ocean antenna means. This oceanographic data includes ocean currents, wave height, wave direction, etc. The input is data from sensors installed on the sea surface, and the output is raw oceanographic data stored in temporary memory. Specifically, the terminal reads data from the sensors and stores it in real time.

[0486] Step 2:

[0487] The terminal uses pre-processing means to remove noise from the collected oceanographic data and convert it into a standard format. The input is raw oceanographic data stored in temporary memory, and the output is data that has been filtered and converted into a standard format. Specifically, the terminal removes noise from the data and converts wave height and ocean current data into a format that is easy to analyze.

[0488] Step 3:

[0489] The device uploads the preprocessed data to a server on the cloud using a communication means. The input is the preprocessed data, and the output is the data sent to the cloud server. Specifically, the device transfers the data to the server in real time using an LTE line or satellite communication.

[0490] Step 4:

[0491] The server uses a database on the cloud to store and manage the uploaded data. The input is the data sent to the cloud server, and the output is the organized data recorded in the database. Specifically, the server checks the integrity of the data and checks for any abnormal values ​​before saving it to the database.

[0492] Step 5:

[0493] The server uses multimodal AI methods to analyze the stored data. The input is oceanographic data stored in the database, and the output is an analyzed ocean environmental model and prediction information. Specifically, the server integrates information from multiple sensors and performs analysis to generate a highly accurate ocean environmental model.

[0494] Step 6:

[0495] The server uses an emotion engine to collect and analyze the user's emotional data. The input is data indicating the user's emotional state, and the output is the analyzed user's emotional information. Specifically, the server uses an emotion recognition algorithm to determine whether the user is feeling stressed.

[0496] Step 7:

[0497] The server adjusts the data presentation method based on the user's emotions. The input is the analyzed marine environment model, forecast information, and the user's emotional information, and the output is the adjusted data presentation method. Specifically, the server dynamically adjusts the user interface to present information concisely and intuitively.

[0498] Step 8:

[0499] The server uses a route optimization method to support the procurement and delivery of fresh seafood along the optimal route. The input is the analyzed oceanographic data and the user's delivery request, and the output is the optimal delivery route. Specifically, the server provides the delivery person with the optimized route, taking into account the shortest route and traffic information.

[0500] By processing and calculating input data at each step and generating the required output, the system embodying this invention can operate efficiently.

[0501] 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.

[0502] 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.

[0503] 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.

[0504] [Second embodiment]

[0505] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0506] 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.

[0507] 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).

[0508] 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.

[0509] 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.

[0510] 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).

[0511] 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.

[0512] 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.

[0513] 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.

[0514] 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.

[0515] 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.

[0516] 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."

[0517] The present invention is a system for collecting, processing, and analyzing oceanographic data and providing it to users in real time. An embodiment of this system will be described in detail below.

[0518] System Overview

[0519] The system consists of the following main components:

[0520] 1. Marine Antenna Means

[0521] 2. Pretreatment Methods

[0522] 3. Means of communication

[0523] 4. Database Means

[0524] 5. Multimodal AI Methods

[0525] 6. Means of providing data

[0526] Marine Antenna Means

[0527] The marine antenna means is a device for collecting marine data such as ocean currents, wave height, and wave direction in real time. For example, data is acquired using various sensors installed on the sea surface. This data is first stored in temporary memory within the antenna.

[0528] Pretreatment means

[0529] Pre-processing involves processes to remove noise from the collected raw data and convert it into a standard format, which is suitable for subsequent analysis and interpretation.

[0530] communication means

[0531] The communication means includes a communication module for uploading pre-processed data to a cloud server. Communication is mainly via LTE or satellite communications, which enables real-time data transmission even in remote locations.

[0532] Database Means

[0533] The database means is a database placed on a cloud server, which stores and manages data uploaded in real time. The data is recorded in an orderly format and is designed to be immediately accessible when needed.

[0534] Multimodal AI methods

[0535] Multimodal AI tools are artificial intelligence systems that integrate multiple sensor information and perform highly accurate analysis to generate detailed models and predictions of the marine environment. These AI systems also integrate data from sonar and drones to provide richer information.

[0536] Data provision method

[0537] The data provision means is an interface for providing analyzed data to users, who can view the analysis results in real time via their PCs or smartphones.

[0538] A natural language description of what the program does

[0539] The processing flow of the system program and its specific operations will be explained below.

[0540] 1. Ocean data collection (terminal)

[0541] The terminal acquires oceanographic data in real time through an ocean antenna. For example, the terminal reads ocean current data in the South Pacific Ocean from a sensor.

[0542] 2. Initial processing of data (terminal)

[0543] The device removes noise from the data it acquires and standardizes the format. For example, the device filters wave height data and converts it into a standard format.

[0544] 3. Real-time upload (terminal)

[0545] The device uploads the data to the cloud server. For example, the device transmits the pre-processed data to the server using an LTE connection.

[0546] 4. Data storage (server)

[0547] The server stores the received data in a database. For example, the server records ocean current data for the South Pacific Ocean in an SQL database.

[0548] 5. Data analysis (server)

[0549] The server uses multimodal AI to integrate information from multiple sensors and perform detailed analysis. For example, the server integrates sonar and drone data to generate a highly accurate ocean current model.

[0550] 6. Data Provision (Server & User)

[0551] The server provides the analysis results to the user, who then views the data on their smartphone or PC. For example, the user can check recommended navigation route information in real time on their smartphone.

[0552] Specific examples

[0553] Example 1: Navigation support system

[0554] The device collects ocean current data in the Pacific Ocean and uploads it to a cloud server, where it is analyzed and provided to the user's e-navigator app as an optimized route for navigation.

[0555] Example 2: Tsunami observation system

[0556] When the device detects abnormal wave height, it reports it to the cloud in real time. The server analyzes this data and generates a tsunami warning, allowing users to immediately begin evacuation.

[0557] Example 3: Fisheries Management System

[0558] The device acquires fish detection data from the coastal waters and uploads it to the cloud, where the server analyzes the data and provides information on optimal fishing spots to the user's smartphone.

[0559] As described above, the system of the present invention can provide users with highly accurate, real-time information by consistently performing the collection, analysis, and provision of marine data.

[0560] The processing flow will be explained below.

[0561] Step 1:

[0562] The device uses an ocean antenna to collect ocean data such as ocean currents, wave height, and wave direction. The device acquires the data via sensors installed in the ocean and stores it in temporary memory. Specifically, the sensors measure the wave height on the ocean surface and transmit the data to the device.

[0563] Step 2:

[0564] This performs initial processing of raw data collected by the device. First, noise is removed through filtering. Then, a conversion process is performed to standardize the data format. For example, data collected at irregular time intervals is standardized to a timestamp with a regular interval.

[0565] Step 3:

[0566] The device uploads the preprocessed data to a cloud server. The device establishes a secure connection using LTE or satellite communications and transmits the data via HTTPS. For example, ocean current data for the South Pacific Ocean is uploaded to the cloud.

[0567] Step 4:

[0568] The server receives the uploaded data, checks the integrity of the data, and checks for any abnormalities. If there are no problems, the server records the data in a database.

[0569] Step 5:

[0570] The server stores the received data in a database. The stored data is used for subsequent analysis. The server also backs up the stored data in parallel to prevent data loss.

[0571] Step 6:

[0572] The server launches the multimodal AI and begins analyzing the data. The server integrates information from multiple sensors, such as sonar and drones, to generate a detailed model of the marine environment. For example, it analyzes flow patterns using multiple ocean current data.

[0573] Step 7:

[0574] The server then stores the analysis results in a database, which includes detailed information on the marine environment and forecast data. This data is then managed so that users can access it in real time.

[0575] Step 8:

[0576] The server receives requests from users, who then use their smartphones or PCs to send requests for data and analysis results for specific ocean areas.

[0577] Step 9:

[0578] The server retrieves the appropriate data from a database based on the request, and returns it immediately to the user.

[0579] Step 10:

[0580] Users can view the provided data on their smartphones or PCs. For example, they can open a smartphone app and check current ocean current information and the optimal navigation route for their vessel. Based on the analysis results, users can navigate safely and conduct efficient fishing activities.

[0581] Example 1

[0582] 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."

[0583] There is a need for a means to quickly and accurately grasp changes in the marine environment, but existing technologies have difficulty in collecting and analyzing data in real time. Furthermore, there is a lack of a consistent system for effectively processing, storing, analyzing, and providing collected data, which means that users cannot obtain the information they need in a timely manner.

[0584] 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.

[0585] In this invention, the server includes a collection device means for collecting oceanographic data in real time, a preprocessing device means for initially processing the collected oceanographic data, a communication device means for uploading the preprocessed data to a centralized processing device on the cloud, a recording device means for saving the uploaded data, a multi-mode compatible artificial intelligence device means for analyzing the saved data, and an information providing device means for providing the analysis results to users, thereby enabling efficient collection, processing, analysis, and provision of real-time observation data of the marine environment.

[0586] The "collection device means" is a device for collecting oceanographic data such as ocean currents, wave heights, and wave directions in real time.

[0587] The "preprocessing device means" is a device for initially processing collected oceanographic data, removing noise, and standardizing the data format.

[0588] The "communication device means" is a device for uploading pre-processed data to a centralized processing device on the cloud.

[0589] "Recording device means" refers to a device for storing uploaded data.

[0590] The "multi-modal artificial intelligence device means" is an artificial intelligence device that can integrate multiple sensor information to analyze stored data.

[0591] The "information providing device means" is a device for providing the analysis results to the user.

[0592] This invention is a system for collecting, processing, and analyzing oceanographic data and providing it to users in real time. An embodiment of this system will be described in detail below.

[0593] System Overview

[0594] The system consists of the following main components:

[0595] 1. Collection Device Means

[0596] 2. Pretreatment Device Means

[0597] 3. Communication Device Means

[0598] 4. Recording Device Means

[0599] 5. Multimodal Artificial Intelligence Device Means

[0600] 6. Information provision device means

[0601] Collection Device Means

[0602] The collection device means is a device for collecting oceanographic data such as ocean currents, wave height, and wave direction in real time. For example, data is acquired using various sensors (ocean current sensors, wave height meters, etc.) installed on the sea surface. This data is first stored in temporary memory within the collection device.

[0603] Pretreatment device means

[0604] The preprocessing means involves processes to remove noise from the collected raw data and convert it into a standard format. The preprocessed data is then standardized into a form suitable for subsequent analysis. Software for noise filtering algorithms and formatting is used.

[0605] communication device means

[0606] The communication device means includes a communication module for uploading pre-processed data to a centralized processing unit on the cloud. Communication is mainly performed via LTE or satellite communication, which enables real-time data transmission even in remote locations.

[0607] Recording device means

[0608] The recording device is a database stored on a cloud server, which stores and manages the data uploaded in real time. The data is recorded in an orderly format and is designed to be immediately accessible when needed.

[0609] Multimodal artificial intelligence device means

[0610] The multi-modal AI system is an AI system that integrates multiple sensor information and performs highly accurate analysis to generate detailed models and predictions of the marine environment. This AI system also integrates data from sonar and drones to provide richer information. For example, deep learning frameworks such as TensorFlow and PyTorch are used.

[0611] Information providing device means

[0612] The information providing device means is an interface for providing analyzed data to users. Users can view the analysis results in real time via a PC or smartphone. Data is provided via a web interface or mobile application.

[0613] Specific examples

[0614] Example 1: Navigation support system

[0615] The device collects ocean current data in the Pacific Ocean and uploads it to a cloud server, where it is analyzed and provided to the user's navigation app as an optimal route.

[0616] Example 2: Tsunami observation system

[0617] When the device detects abnormal wave height, it reports it to the cloud in real time. The server analyzes this data and generates a tsunami warning, allowing users to immediately begin evacuation.

[0618] Example 3: Fisheries Management System

[0619] The device acquires fish detection data from the coastal waters and uploads it to the cloud, where the server analyzes the data and provides information on optimal fishing spots to the user's smartphone.

[0620] Prompt Sentence Examples

[0621] TXT

[0622] We have developed a system that collects, processes, analyzes, and provides real-time oceanographic data to users. This system consists of six main components: collection device means, pre-processing device means, communication device means, recording device means, multi-mode AI device means, and information provision device means. Please explain in detail.

[0623] 1. Oceanographic data collection

[0624] 2. Initial data processing

[0625] 3. Real-time uploading

[0626] 4. Data storage

[0627] 5. Data Analysis

[0628] 6. Data provision

[0629] As described above, the system of the present invention can provide users with highly accurate, real-time information by consistently performing the collection, analysis, and provision of oceanographic data.

[0630] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0631] The flow of this system's program processing

[0632] Step 1: Collecting oceanographic data (terminal)

[0633] Specific description:

[0634] The terminal uses an ocean antenna to collect ocean data in real time. Specifically, data is acquired through various sensors (e.g., ocean current sensors, wave height meters, etc.) and the collected data is stored in temporary memory.

[0635] Input and Output:

[0636] Input: Oceanographic data acquired by sensors (e.g., ocean currents, wave height, wave direction)

[0637] Output: Raw data stored in temporary memory

[0638] Specific behavior:

[0639] The device retrieves the latest ocean current data at a specified point in the South Pacific.

[0640] The sensor measures the seawater temperature and salinity at regular intervals (such as every second) and transmits the data to the terminal.

[0641] Step 2: Initial processing of data (terminal)

[0642] Specific description:

[0643] The terminal removes noise from the collected data and standardizes the format. Using noise filtering algorithms and formatting standardization software, the raw data is converted into a standard format.

[0644] Input and Output:

[0645] Input: raw data collected

[0646] Output: Denoised and uniformly formatted data

[0647] Specific behavior:

[0648] The terminal uses a moving average filter to remove outliers from the collected wave height data.

[0649] Convert seawater temperature data into CSV format.

[0650] Step 3: Real-time upload (terminal)

[0651] Specific description:

[0652] The device uploads the preprocessed data to the cloud server. The data is sent to the cloud server via wireless communication (LTE or satellite communication).

[0653] Input and Output:

[0654] Input: Preprocessed data

[0655] Output: Data uploaded to the cloud server

[0656] Specific behavior:

[0657] The terminal uses an LTE line to send preprocessed wave height data to a cloud server.

[0658] The device uploads new data to the server in bulk every minute.

[0659] Step 4: Storing data (server)

[0660] Specific description:

[0661] The server stores the received data in a database, and the cloud server immediately records and indexes the data received for quick access.

[0662] Input and Output:

[0663] Input: Data uploaded to the cloud server

[0664] Output: Data stored in the database

[0665] Specific behavior:

[0666] The server receives new ocean current data and records it in the SQL database using INSERT statements.

[0667] Add a timestamp when saving data.

[0668] Step 5: Analyzing the data (server)

[0669] Specific description:

[0670] The server uses multi-modal artificial intelligence to integrate and perform detailed analysis of the multiple sensor data, generating detailed models and predictions of the marine environment.

[0671] Input and Output:

[0672] Input: Saved data

[0673] Output: Analyzed data and predictive models

[0674] Specific behavior:

[0675] The server uses TensorFlow and PyTorch to integrate sonar and drone data to generate a highly accurate ocean current model.

[0676] The AI ​​model makes future predictions about the marine environment and stores the results on a server.

[0677] Step 6: Providing Data (Server & User)

[0678] Specific description:

[0679] The server provides the analysis results to the user, who then views the data, which is provided through a web interface or mobile application.

[0680] Input and Output:

[0681] Input: Parsed data

[0682] Output: Data presented in a user-viewable format

[0683] Specific behavior:

[0684] The server provides the analysis results through web applications and mobile applications.

[0685] A user checks recommended navigation route information on their smartphone.

[0686] (Application example 1)

[0687] 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."

[0688] One of the challenges facing modern food delivery systems is a lack of technology to efficiently and quickly deliver fresh seafood. While it is possible to collect oceanographic data and use it to identify optimal fishing grounds, there is no system in place to use that information to quickly deliver fresh seafood to consumers. In particular, there is a need for technology that optimizes the supply chain from fisheries to consumers by calculating efficient delivery routes using real-time oceanographic data and controlling delivery methods such as drones.

[0689] 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.

[0690] In this invention, the server includes marine antenna means for collecting marine data in real time, preprocessing means for initially processing the collected marine data, communication means for uploading the preprocessed data to a server on the cloud, database means for storing the uploaded data, multimodal AI means for analyzing the stored data, data providing means for providing the analysis results to a user, delivery means for efficiently delivering fresh seafood using the data providing means, and route calculation means for controlling the delivery means and calculating an optimal delivery route. This enables efficient and prompt delivery of fresh seafood based on the marine data collected in real time.

[0691] The "marine antenna means" is a device for collecting marine data such as ocean currents, wave heights, and wave directions in real time.

[0692] A "pre-processing means" is a device that includes a process for removing noise from collected raw data and converting it into a standard format.

[0693] The "communication means" is a communication module for uploading preprocessed data to a server on the cloud.

[0694] "Database means" refers to a database placed on a cloud server, and is a system that stores and manages data uploaded in real time.

[0695] "Multimodal AI means" is an artificial intelligence system that integrates information from multiple sensors and performs highly accurate analysis.

[0696] The "data providing means" is an interface for providing analyzed data to the user.

[0697] "Delivery methods" refer to logistics methods for efficiently delivering fresh seafood, including drones and vehicles.

[0698] The "route calculation means" is a system for controlling delivery means and calculating the optimal delivery route.

[0699] This invention is a marine data collection, analysis and provision system for efficiently providing fresh seafood. The system consists of the following main components:

[0700] Hardware Configuration

[0701] 1. Marine Antenna Means

[0702] The various sensors include sensors that measure ocean currents, wave height, wave direction, etc. These sensor data are acquired in real time and stored in temporary memory.

[0703] 2. Terminal

[0704] The devices include smartphones and drones, which collect, pre-process and upload real-time data.

[0705] 3. Cloud Server

[0706] It is equipped with a database means and a multimodal AI means, and has an interface for providing analysis results to users.

[0707] Software Configuration

[0708] 1. Pretreatment methods

[0709] Software that includes processes for denoising raw data and converting it into a standard format.

[0710] 2. Means of communication

[0711] A communication module for uploading pre-processed data to a cloud server using LTE or satellite communications.

[0712] 3. Database Means

[0713] Software for storing and managing data uploaded in real time in a database on a cloud server.

[0714] 4. Multimodal AI Methods

[0715] An AI system that integrates information from multiple sensors and performs highly accurate analysis. For example, it can integrate data from sonar and drones to generate detailed models and predictions of the marine environment.

[0716] 5. Means of providing data

[0717] Software with an interface for providing analysis results to users.

[0718] 6. Delivery method

[0719] Logistics methods such as drones and vehicles to efficiently deliver fresh seafood.

[0720] 7. Route Calculation Method

[0721] A system that controls delivery means and calculates the optimal delivery route.

[0722] Successful examples

[0723] 1. Oceanographic data collection

[0724] Data such as ocean currents, wave height, and wave direction is collected in real time and stored in temporary memory.

[0725] 2. Pretreatment

[0726] The collected data is denoised and converted into a standard format, e.g., wave height data is filtered.

[0727] 3. Upload your data

[0728] The preprocessed data is uploaded to a cloud server via an LTE line or other means.

[0729] 4. Data Analysis

[0730] Multimodal AI is used to integrate data and generate highly accurate ocean models, for example, using sonar and drone data to generate detailed ocean current models.

[0731] 5. Data provision

[0732] The analysis results are provided to users, who can view real-time data via their smartphones and determine the optimal route for delivering fresh seafood.

[0733] Food delivery as a concrete example

[0734] 1. Optimal fishing ground identification system

[0735] Ocean data is collected and information on optimal fishing spots is uploaded to a cloud server. This information is analyzed and provided to users as optimal fishing spots.

[0736] 2. Real-time fresh seafood delivery system

[0737] Based on oceanographic data collected by drones, fresh seafood is delivered via the optimal route, and users can check the delivery status in real time via their smartphones.

[0738] Prompt Sentence Examples

[0739] Fisheries management system using ocean data. Please implement a function to collect and analyze fishing ground information in real time and provide users with the best fishing ground information.

[0740] As described above, the present invention is a system that can efficiently deliver fresh seafood to users by consistently performing everything from collecting marine data to analyzing it, providing the data, and delivering it along the optimal route.

[0741] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0742] Step 1:

[0743] The terminal collects oceanographic data

[0744] Input: Various sensor data (ocean currents, wave height, wave direction, etc.)

[0745] Specific operation: Acquire ocean data in real time using ocean antenna means. For example, the terminal collects data from a wave direction sensor and stores it in temporary memory.

[0746] Output: Raw data

[0747] Step 2:

[0748] The device preprocesses the data

[0749] Input: Raw data

[0750] Specific operation: Performs noise removal and standard format conversion. For example, it filters the wave height data acquired by the device and converts it into a unified format.

[0751] Output: Preprocessed, consistent data

[0752] Step 3:

[0753] The device uploads the preprocessed data to the cloud server.

[0754] Input: Preprocessed data

[0755] Specific operation: Data is transmitted to a cloud server using LTE or satellite communication. For example, the terminal uploads preprocessed ocean current data to the cloud server.

[0756] Output: Data stored on the cloud server

[0757] Step 4:

[0758] The server stores the data

[0759] Input: Data uploaded to the cloud server

[0760] Specific operation: Data is stored and managed using database means. For example, a cloud server records ocean current data in an SQL database.

[0761] Output: Real-time data stored in a database

[0762] Step 5:

[0763] The server analyzes the data

[0764] Input: Saved data

[0765] How it works: Multimodal AI methods are used to analyze data and generate predictive models. For example, a server integrates data from sonar and drones to generate a highly accurate ocean current model.

[0766] Output: Analysis results and predicted data

[0767] Step 6:

[0768] The server provides the analysis results to the user

[0769] Input: Model and predicted data as analysis results

[0770] Specific operation: The analysis results are provided to the user using the data provision means. For example, the user can view optimal fishing ground information in real time via their smartphone.

[0771] Output: Analysis result data provided to the user

[0772] Step 7:

[0773] The server controls the delivery method

[0774] Input: Parsed oceanographic data and shipping routes

[0775] Specific operation: The route calculation means is used to analyze data and calculate the optimal delivery route. For example, the server calculates and instructs a drone's delivery route to efficiently deliver fresh seafood.

[0776] Output: Optimal delivery route and control instructions

[0777] Step 8:

[0778] Delivery method provides fresh seafood

[0779] Input: Optimal delivery route and control instructions

[0780] Specific operation: Drones and vehicles efficiently deliver fresh seafood. For example, drones deliver fresh seafood to consumers by following optimal routes.

[0781] Output: Fresh seafood delivered to the consumer

[0782] 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.

[0783] The present invention is a system that collects, processes, and analyzes oceanographic data and provides it to users in real time, while also recognizing the user's emotions and dynamically adjusting the content of the data provided. An embodiment of this system will be described in detail below.

[0784] System Overview

[0785] The system consists of the following main components:

[0786] 1. Marine Antenna Means

[0787] 2. Pretreatment Methods

[0788] 3. Means of communication

[0789] 4. Database Means

[0790] 5. Multimodal AI Methods

[0791] 6. Means of providing data

[0792] 7. Emotion Engine

[0793] Marine Antenna Means

[0794] The marine antenna means is a device for collecting marine data such as ocean currents, wave height, and wave direction in real time. For example, data is acquired using various sensors installed on the sea surface. This data is first stored in temporary memory within the antenna.

[0795] Pretreatment means

[0796] Pre-processing involves processes to remove noise from the collected raw data and convert it into a standard format, which is suitable for subsequent analysis and interpretation.

[0797] communication means

[0798] The communication means includes a communication module for uploading pre-processed data to a cloud server. Communication is mainly via LTE or satellite communications, which enables real-time data transmission even in remote locations.

[0799] Database Means

[0800] The database means is a database placed on a cloud server, which stores and manages data uploaded in real time. The data is recorded in an orderly format and is designed to be immediately accessible when needed.

[0801] Multimodal AI methods

[0802] Multimodal AI tools are artificial intelligence systems that integrate multiple sensor information and perform highly accurate analysis to generate detailed models and predictions of the marine environment. These AI systems also integrate data from sonar and drones to provide richer information.

[0803] Data provision method

[0804] The data provision means is an interface for providing analyzed data to users, who can view the analysis results in real time via their PCs or smartphones.

[0805] Emotion Engine

[0806] The emotion engine is a system that recognizes the user's emotions, analyzes the data, and adjusts the display and content of the oceanographic data provided. For example, if the user is stressed, the data will be displayed more simply and easily understandable.

[0807] A natural language description of what the program does

[0808] The processing flow of the system program and its specific operations will be explained below.

[0809] 1. Ocean data collection (terminal)

[0810] The terminal acquires oceanographic data in real time through an ocean antenna. For example, the terminal reads ocean current data in the South Pacific Ocean from a sensor.

[0811] 2. Initial processing of data (terminal)

[0812] The device removes noise from the data it acquires and standardizes the format. For example, the device filters wave height data and converts it into a standard format.

[0813] 3. Real-time upload (terminal)

[0814] The device uploads the data to the cloud server. For example, the device transmits the pre-processed data to the server using an LTE connection.

[0815] 4. Data storage (server)

[0816] The server receives the uploaded data, checks the integrity of the data, and checks for any abnormalities. If there are no problems, the server records the data in a database.

[0817] 5. Data analysis (server)

[0818] The server uses multimodal AI to integrate information from multiple sensors and generate a detailed model of the marine environment, for example by analyzing flow patterns using multiple ocean current data.

[0819] 6. User Emotion Recognition (Server)

[0820] The server runs an emotion engine to collect and analyze the user's emotion data, for example, to determine whether the user is feeling stressed.

[0821] 7. Coordination of data provision (server)

[0822] The server adjusts how it presents data based on the user's emotions, for example, if the user is feeling stressed, it will present a concise summary of the data.

[0823] 8. Data Provision (Server & User)

[0824] The server provides the analysis results to the user, who then views the data on their smartphone or PC. For example, the user can check real-time oceanographic data and analysis results on their smartphone.

[0825] Specific examples

[0826] Example 1: Navigation support system

[0827] The device collects ocean current data in the Pacific Ocean and uploads it to a cloud server. This data is then analyzed and provided to the user's e-navigator app as an optimized route. Furthermore, an emotion engine recognizes the user's stress level and adjusts the displayed data accordingly.

[0828] Example 2: Tsunami observation system

[0829] When the device detects abnormal wave height, it reports it to the cloud in real time. The server analyzes this data and generates a tsunami warning. The emotion engine detects the user's emotions and displays evacuation instructions more intuitively, allowing the user to immediately begin evacuation.

[0830] Example 3: Fisheries Management System

[0831] The device collects fish detection data from the coastal waters and uploads it to the cloud. The server analyzes the data and provides optimal fishing spot information to the user's smartphone. The emotion engine analyzes the user's emotions and adjusts the priority and presentation of the information.

[0832] As described above, the system of the present invention provides optimal information to users by consistently performing all steps from collecting marine data to analyzing and providing it, as well as recognizing and adjusting user emotions.

[0833] The processing flow will be explained below.

[0834] Step 1:

[0835] The device uses an ocean antenna to collect ocean data such as ocean currents, wave height, and wave direction. The device acquires the data via sensors installed in the ocean and stores it in temporary memory. Specifically, the sensors measure the wave height on the ocean surface and transmit the data to the device.

[0836] Step 2:

[0837] This performs initial processing of raw data collected by the device. First, noise is removed through filtering. Then, a conversion process is performed to standardize the data format. For example, data collected at irregular time intervals is standardized to a timestamp with a regular interval.

[0838] Step 3:

[0839] The device uploads the preprocessed data to a cloud server. The device establishes a secure connection using LTE or satellite communications and transmits the data via HTTPS. For example, ocean current data for the South Pacific Ocean is uploaded to the cloud.

[0840] Step 4:

[0841] The server receives the uploaded data, checks the integrity of the data, and checks for any abnormalities. If there are no problems, the server records the data in a database.

[0842] Step 5:

[0843] The server stores the received data in a database. The stored data is used for subsequent analysis. The server also backs up the stored data in parallel to prevent data loss.

[0844] Step 6:

[0845] The server launches the multimodal AI and begins analyzing the data. The server integrates information from multiple sensors, such as sonar and drones, to generate a detailed model of the marine environment. For example, it analyzes flow patterns using multiple ocean current data.

[0846] Step 7:

[0847] The server then stores the analysis results in a database, which includes detailed information on the marine environment and forecast data. This data is then managed so that users can access it in real time.

[0848] Step 8:

[0849] The server starts the emotion engine, collects and analyzes the user's emotion data, and evaluates the user's state based on the emotion data obtained from the user's smartphone or PC. For example, it determines whether the user is feeling stressed.

[0850] Step 9:

[0851] The server dynamically adjusts the way data is presented based on the user's emotions. For example, if the user is feeling stressed, the server will avoid displaying complex data and provide simple, visually easy-to-understand information.

[0852] Step 10:

[0853] The server receives requests from users. Users use their smartphones or PCs to send requests for data and analysis results for specific ocean areas. For example, a user requests detailed data on a particular fishing ground.

[0854] Step 11:

[0855] The server retrieves the appropriate data from the database based on the request, and immediately returns the retrieved data to the user. For example, it retrieves the latest ocean current data for the South Pacific and provides it to the user.

[0856] Step 12:

[0857] Users can view the provided data in real time on their smartphones or PCs. For example, a user can open a smartphone app and check data for optimizing navigation routes or managing fisheries. The app takes into account the user's emotional state and displays the necessary information in an appropriate format.

[0858] Example 2

[0859] 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."

[0860] Conventional oceanographic data collection systems have the problem that the analysis and provision of acquired data is one-way, and they are unable to respond flexibly to the user's emotions or circumstances. Furthermore, when collecting and analyzing data in real time, managing huge amounts of data and ensuring the speed of analysis becomes an issue. This makes it difficult to provide users with the information they need, and there has been a demand for improved usability.

[0861] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0862] In this invention, the server includes marine antenna means for collecting marine data in real time, preprocessing means for initially processing the collected marine data, communication means for uploading the preprocessed data to a server on the cloud, database means for saving the uploaded data, multimodal AI means for analyzing the saved data, data providing means for providing the analysis results to the user, and emotion engine means for analyzing the user's emotions and dynamically adjusting the content to be provided. This makes it possible to effectively collect and analyze data in real time and to appropriately provide necessary information based on the user's emotions.

[0863] The "marine antenna means" is a device for collecting marine data such as ocean currents, wave heights, and wave directions in real time.

[0864] A "pre-processing means" is a device that includes a process for removing noise from collected raw data and converting it into a standard format.

[0865] A "communication means" is a device that includes a communication module for uploading preprocessed data to a server on the cloud.

[0866] "Database means" refers to a database placed on a cloud server, and is a device for storing and managing data uploaded in real time.

[0867] A "multimodal AI means" is a device that includes an artificial intelligence system for integrating information from multiple sensors and performing highly accurate analysis.

[0868] A "data providing means" is a device that includes an interface for providing analyzed data to a user.

[0869] The "emotion engine means" is a device that includes a system that recognizes the user's emotions, analyzes the data, and dynamically adjusts the display method and content of the oceanographic data provided.

[0870] The system of this invention collects, processes, and analyzes oceanographic data, and provides it to users in real time, while also recognizing the user's emotions and dynamically adjusting the content provided. The system consists of the following main components:

[0871] Components

[0872] 1. Marine Antenna Means

[0873] The marine antenna means is a device for collecting marine data such as ocean currents, wave height, and wave direction in real time. For example, it uses various sensors installed on the sea surface. This data is first stored in a temporary memory within the antenna.

[0874] 2. Pretreatment Methods

[0875] Pre-processing involves processes to remove noise from the collected raw data and convert it into a standard format, which is suitable for subsequent analysis and interpretation.

[0876] 3. Means of communication

[0877] The communication means includes a communication module for uploading pre-processed data to a cloud server. Communication is mainly via LTE or satellite communications, which enables real-time data transmission even in remote locations.

[0878] 4. Database Means

[0879] The database means is a database placed on a cloud server, which stores and manages data uploaded in real time. The data is recorded in an orderly format and is designed to be immediately accessible when needed.

[0880] 5. Multimodal AI Methods

[0881] Multimodal AI methods are artificial intelligence systems that integrate multiple sensor information and perform highly accurate analysis to generate detailed models and predictions of the marine environment. These AI systems are built using TensorFlow and PyTorch, for example. They also integrate data from sonar and drones to provide richer information.

[0882] 6. Means of providing data

[0883] The data provision means is an interface for providing analyzed data to users, who can view the analysis results in real time via their PCs or smartphones.

[0884] 7. Emotional Engine Means

[0885] The emotion engine is a system that recognizes the user's emotions, analyzes the data, and dynamically adjusts the display method and content of the oceanographic data provided. For example, if the user is under stress, the data will be displayed in a more concise and easy-to-understand manner. The emotion recognition algorithm uses IBM Watson's emotion analysis.

[0886] Specific examples

[0887] Example 1: Navigation support system

[0888] The device collects ocean current data in the Pacific Ocean and uploads it to a cloud server. This data is analyzed by the server and provided to the user's navigation support app as an optimal route. In addition, an emotion engine recognizes the user's stress level and briefly adjusts the data presented.

[0889] Example prompt: "Based on ocean current data collected in the Pacific Ocean, provide the best sailing route. Also, assess the user's stress level and present the data in a concise format."

[0890] Example 2: Tsunami observation system

[0891] When the device detects abnormal wave height, it reports it to the cloud in real time. The server analyzes this data and generates a tsunami warning. The emotion engine analyzes the user's emotions and displays evacuation instructions more intuitively.

[0892] Example prompt: "If abnormal wave heights are detected, generate a real-time tsunami warning and provide evacuation instructions to the user in an intuitive manner."

[0893] Example 3: Fisheries Management System

[0894] The device collects fish detection data from the coastal waters and uploads it to the cloud. The server analyzes the data and provides optimal fishing spot information to the user's smartphone. The emotion engine analyzes the user's emotions and adjusts the priority and presentation of the information.

[0895] Example prompt: "Based on inshore fish detection data, provide information on optimal fishing spots. Also, assess user sentiment and adjust how the information is presented."

[0896] As described above, the system of the present invention provides optimal information to users by consistently performing all steps from collecting marine data to analyzing and providing it, as well as recognizing and adjusting user emotions.

[0897] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0898] Step 1:

[0899] Ocean data collection (terminal)

[0900] The terminal acquires oceanographic data in real time through an ocean antenna. Specifically, the terminal acquires data such as ocean currents, wave height, and wave direction from sensors installed in the Pacific Ocean at one-second intervals and records it in temporary memory. The input at this time is raw data from each sensor, and the output is raw oceanographic data stored in temporary memory.

[0901] Step 2:

[0902] Initial data processing (terminal)

[0903] The terminal removes noise from the acquired data and standardizes the format. In operation, the terminal uses a filtering algorithm to remove noise from the raw data and then converts the data into a standard format (e.g., CSV format). The input is the raw oceanographic data saved in step 1, and the output is the data that has been removed and converted into a standard format.

[0904] Step 3:

[0905] Real-time upload (terminal)

[0906] The device uploads the preprocessed data to a cloud server. Specifically, the device uses an LTE or satellite communication module to transmit the filtered data to the cloud server in packets every minute. The input is the preprocessed data, and the output is the data transmitted to the cloud server.

[0907] Step 4:

[0908] Data storage (server)

[0909] The server receives the data uploaded to the cloud, verifies it, and then records it in a database. The server then checks the integrity of the data it receives to ensure there are no outliers or missing data. It then stores the data in a database (e.g., Amazon RDS). The input at this time is the data uploaded to the cloud server, and the output is the data stored in the database, the integrity of which has been confirmed.

[0910] Step 5:

[0911] Data analysis (server)

[0912] The server uses multimodal AI to integrate multiple sensor information and generate a detailed marine environment model. Specifically, the server integrates collected ocean current and wave data and runs an AI model using TensorFlow and PyTorch, thereby generating ocean current patterns and predictions. The input is sensor data whose consistency has been confirmed, and the output is an analyzed marine environment model.

[0913] Step 6:

[0914] User emotion recognition (server)

[0915] The server launches an emotion engine to collect and analyze the user's emotional data. In operation, the server uses an emotion recognition algorithm (for example, IBM Watson's emotion analysis) to analyze the user's voice input and text messages and determine their stress level and relaxation level. The input is the user's emotional data, and the output is analyzed emotional information.

[0916] Step 7:

[0917] Coordination of data provision (server)

[0918] The server adjusts how it presents data based on the user's emotions. Specifically, it adjusts the amount and format of data presented based on the results obtained from the emotion engine. If the user is feeling stressed, it uses simple, visually easy-to-understand graphs and infographics. The inputs are the analyzed emotion information and the marine environment model, and the output is the adjusted display format of the data.

[0919] Step 8:

[0920] Data provision (server & user)

[0921] The server provides the analysis results to the user, who then views the data on their smartphone or PC. Specifically, the server sends the analysis results to the user's application via a Web API, and the user can view real-time oceanographic data and analysis results through a smartphone app or PC web browser. The input is data in an adjusted display format, and the output is the analysis results displayed on the user's device.

[0922] (Application example 2)

[0923] 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."

[0924] Conventional seafood delivery systems have difficulty effectively utilizing oceanographic data, making it impossible to grasp the status of fishing grounds and environmental changes in real time. This makes it difficult to efficiently supply fresh seafood and to optimize deliveries. Furthermore, the system is unable to adjust information provision based on the user's emotions, often resulting in a poor user experience. These issues need to be resolved.

[0925] 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.

[0926] In this invention, the server includes a marine antenna means for collecting marine data in real time, a preprocessing means for initially processing the collected marine data, a communication means for uploading the preprocessed data to a server on the cloud, a database means for saving the uploaded data, a multimodal AI means for analyzing the saved data, a data provision means for providing the analysis results to the user, an emotion engine that recognizes the user's emotions and dynamically adjusts the data provision method, and a route optimization means for using the analyzed marine data to support food delivery via the optimal route. This enables the server to analyze the marine data and provide information on optimal fishing grounds, as well as provide information based on the user's emotions, thereby realizing efficient delivery of fresh seafood that satisfies the user.

[0927] The "marine antenna means" is a device for collecting marine data such as ocean currents, wave heights, and wave directions in real time.

[0928] A "pre-processing means" is a device that includes a process for removing noise from collected raw data and converting it into a standard format.

[0929] A "communication means" is a device that includes a communication module for uploading preprocessed data to a server on the cloud.

[0930] "Database means" refers to a database placed on a cloud server, and is a device that serves to store and manage data uploaded in real time.

[0931] "Multimodal AI means" is an artificial intelligence system that integrates information from multiple sensors and performs highly accurate analysis.

[0932] The "data providing means" is an interface for providing analyzed data to the user.

[0933] An "emotion engine" is a system that recognizes a user's emotions, analyzes that data, and dynamically adjusts the display method and content of the information provided.

[0934] The "route optimization tool" is a device that has the function of supporting the procurement and delivery of fresh seafood along the optimal route based on analyzed oceanographic data.

[0935] The system for realizing the present invention comprises the following main components:

[0936] Oceanographic data collection

[0937] The server collects oceanographic data in real time from various sensors installed on the ocean surface, such as data on ocean currents, wave height, wave direction, etc. This data is first collected by an ocean antenna means and stored in temporary memory.

[0938] Initial processing of data

[0939] The server uses pre-processing means to remove noise from the collected data and convert it into a standard format, specifically filtering the wave height data and standardizing it into a format suitable for analysis.

[0940] Uploading data to the cloud

[0941] The preprocessed data is uploaded to a cloud server using a communication method, mainly via LTE or satellite communication, which enables real-time data transmission even from remote locations.

[0942] Data storage

[0943] The cloud server stores the uploaded data using a database system, which is designed to record the data in an organized format and make it instantly accessible when needed.

[0944] Data analysis

[0945] The server analyzes the stored data using multimodal AI methods, integrating information from multiple sensors to generate highly accurate marine environment models and predictions, enabling the provision of richer information.

[0946] User Emotion Recognition

[0947] The server uses an emotion engine to collect and analyze the user's emotional data, for example to determine whether the user is feeling stressed, and dynamically adjusts the display and content of the information provided to the user based on this data.

[0948] Customization of information provided

[0949] When a user views the analysis results on a PC or smartphone, the server takes into account the user's emotions and adjusts the display to display information concisely and intuitively, improving the user experience.

[0950] Fresh seafood delivery assistance

[0951] Based on the analysis results, route optimization methods are used to assist food delivery, providing information on the best fishing spots and enabling the fastest delivery of fresh seafood via the most optimal route.

[0952] Specific examples

[0953] As a concrete example, consider a fresh seafood delivery support system. The user's smartphone app displays information on optimal fishing grounds based on oceanographic data collected in real time. Based on this information, delivery personnel can purchase and deliver seafood along the optimal route. In addition, an emotion engine analyzes the user's emotions and adjusts the way information is displayed, allowing the user to obtain information without feeling stressed.

[0954] Prompt Sentence Examples

[0955] Here is an example of a prompt for generating code using a generative AI model:

[0956] I am implementing a seafood delivery support app. Please generate code for the following functions:

[0957] 1. Ability to collect and process oceanographic data in real time

[0958] 2. Ability to upload data to the cloud

[0959] 3. Function to analyze data and provide optimal fishing ground information

[0960] 4. Ability to customize information display based on user emotional data

[0961] 5. A feature that provides optimal routes for delivering fresh seafood.

[0962] Please provide the specific code and explanation for each function.

[0963] In this way, the system of the present invention consistently collects, analyzes, and provides marine data, as well as recognizes and adjusts user emotions, thereby providing optimal information to users and enabling the efficient and effective delivery of fresh seafood.

[0964] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0965] Step 1:

[0966] The terminal collects oceanographic data in real time through an ocean antenna means. This oceanographic data includes ocean currents, wave height, wave direction, etc. The input is data from sensors installed on the sea surface, and the output is raw oceanographic data stored in temporary memory. Specifically, the terminal reads data from the sensors and stores it in real time.

[0967] Step 2:

[0968] The terminal uses pre-processing means to remove noise from the collected oceanographic data and convert it into a standard format. The input is raw oceanographic data stored in temporary memory, and the output is data that has been filtered and converted into a standard format. Specifically, the terminal removes noise from the data and converts wave height and ocean current data into a format that is easy to analyze.

[0969] Step 3:

[0970] The device uploads the preprocessed data to a server on the cloud using a communication means. The input is the preprocessed data, and the output is the data sent to the cloud server. Specifically, the device transfers the data to the server in real time using an LTE line or satellite communication.

[0971] Step 4:

[0972] The server uses a database on the cloud to store and manage the uploaded data. The input is the data sent to the cloud server, and the output is the organized data recorded in the database. Specifically, the server checks the integrity of the data and checks for any abnormal values ​​before saving it to the database.

[0973] Step 5:

[0974] The server uses multimodal AI methods to analyze the stored data. The input is oceanographic data stored in the database, and the output is an analyzed ocean environmental model and prediction information. Specifically, the server integrates information from multiple sensors and performs analysis to generate a highly accurate ocean environmental model.

[0975] Step 6:

[0976] The server uses an emotion engine to collect and analyze the user's emotional data. The input is data indicating the user's emotional state, and the output is the analyzed user's emotional information. Specifically, the server uses an emotion recognition algorithm to determine whether the user is feeling stressed.

[0977] Step 7:

[0978] The server adjusts the data presentation method based on the user's emotions. The input is the analyzed marine environment model, forecast information, and the user's emotional information, and the output is the adjusted data presentation method. Specifically, the server dynamically adjusts the user interface to present information concisely and intuitively.

[0979] Step 8:

[0980] The server uses a route optimization method to support the procurement and delivery of fresh seafood along the optimal route. The input is the analyzed oceanographic data and the user's delivery request, and the output is the optimal delivery route. Specifically, the server provides the delivery person with the optimized route, taking into account the shortest route and traffic information.

[0981] By processing and calculating input data at each step and generating the required output, the system embodying this invention can operate efficiently.

[0982] 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.

[0983] 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.

[0984] 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.

[0985] [Third embodiment]

[0986] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0987] 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.

[0988] 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).

[0989] 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.

[0990] 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.

[0991] 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).

[0992] 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.

[0993] 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.

[0994] 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.

[0995] 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.

[0996] 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.

[0997] 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."

[0998] The present invention is a system for collecting, processing, and analyzing oceanographic data and providing it to users in real time. An embodiment of this system will be described in detail below.

[0999] System Overview

[1000] The system consists of the following main components:

[1001] 1. Marine Antenna Means

[1002] 2. Pretreatment Methods

[1003] 3. Means of communication

[1004] 4. Database Means

[1005] 5. Multimodal AI Methods

[1006] 6. Means of providing data

[1007] Marine Antenna Means

[1008] The marine antenna means is a device for collecting marine data such as ocean currents, wave height, and wave direction in real time. For example, data is acquired using various sensors installed on the sea surface. This data is first stored in temporary memory within the antenna.

[1009] Pretreatment means

[1010] Pre-processing involves processes to remove noise from the collected raw data and convert it into a standard format, which is suitable for subsequent analysis and interpretation.

[1011] communication means

[1012] The communication means includes a communication module for uploading pre-processed data to a cloud server. Communication is mainly via LTE or satellite communications, which enables real-time data transmission even in remote locations.

[1013] Database Means

[1014] The database means is a database placed on a cloud server, which stores and manages data uploaded in real time. The data is recorded in an orderly format and is designed to be immediately accessible when needed.

[1015] Multimodal AI methods

[1016] Multimodal AI tools are artificial intelligence systems that integrate multiple sensor information and perform highly accurate analysis to generate detailed models and predictions of the marine environment. These AI systems also integrate data from sonar and drones to provide richer information.

[1017] Data provision method

[1018] The data provision means is an interface for providing analyzed data to users, who can view the analysis results in real time via their PCs or smartphones.

[1019] A natural language description of what the program does

[1020] The processing flow of the system program and its specific operations will be explained below.

[1021] 1. Ocean data collection (terminal)

[1022] The terminal acquires oceanographic data in real time through an ocean antenna. For example, the terminal reads ocean current data in the South Pacific Ocean from a sensor.

[1023] 2. Initial processing of data (terminal)

[1024] The device removes noise from the data it acquires and standardizes the format. For example, the device filters wave height data and converts it into a standard format.

[1025] 3. Real-time upload (terminal)

[1026] The device uploads the data to the cloud server. For example, the device transmits the pre-processed data to the server using an LTE connection.

[1027] 4. Data storage (server)

[1028] The server stores the received data in a database. For example, the server records ocean current data for the South Pacific Ocean in an SQL database.

[1029] 5. Data analysis (server)

[1030] The server uses multimodal AI to integrate information from multiple sensors and perform detailed analysis. For example, the server integrates sonar and drone data to generate a highly accurate ocean current model.

[1031] 6. Data Provision (Server & User)

[1032] The server provides the analysis results to the user, who then views the data on their smartphone or PC. For example, the user can check recommended navigation route information in real time on their smartphone.

[1033] Specific examples

[1034] Example 1: Navigation support system

[1035] The device collects ocean current data in the Pacific Ocean and uploads it to a cloud server, where it is analyzed and provided to the user's e-navigator app as an optimized route for navigation.

[1036] Example 2: Tsunami observation system

[1037] When the device detects abnormal wave height, it reports it to the cloud in real time. The server analyzes this data and generates a tsunami warning, allowing users to immediately begin evacuation.

[1038] Example 3: Fisheries Management System

[1039] The device acquires fish detection data from the coastal waters and uploads it to the cloud, where the server analyzes the data and provides information on optimal fishing spots to the user's smartphone.

[1040] As described above, the system of the present invention can provide users with highly accurate, real-time information by consistently performing the collection, analysis, and provision of marine data.

[1041] The processing flow will be explained below.

[1042] Step 1:

[1043] The device uses an ocean antenna to collect ocean data such as ocean currents, wave height, and wave direction. The device acquires the data via sensors installed in the ocean and stores it in temporary memory. Specifically, the sensors measure the wave height on the ocean surface and transmit the data to the device.

[1044] Step 2:

[1045] This performs initial processing of raw data collected by the device. First, noise is removed through filtering. Then, a conversion process is performed to standardize the data format. For example, data collected at irregular time intervals is standardized to a timestamp with a regular interval.

[1046] Step 3:

[1047] The device uploads the preprocessed data to a cloud server. The device establishes a secure connection using LTE or satellite communications and transmits the data via HTTPS. For example, ocean current data for the South Pacific Ocean is uploaded to the cloud.

[1048] Step 4:

[1049] The server receives the uploaded data, checks the integrity of the data, and checks for any abnormalities. If there are no problems, the server records the data in a database.

[1050] Step 5:

[1051] The server stores the received data in a database. The stored data is used for subsequent analysis. The server also backs up the stored data in parallel to prevent data loss.

[1052] Step 6:

[1053] The server launches the multimodal AI and begins analyzing the data. The server integrates information from multiple sensors, such as sonar and drones, to generate a detailed model of the marine environment. For example, it analyzes flow patterns using multiple ocean current data.

[1054] Step 7:

[1055] The server then stores the analysis results in a database, which includes detailed information on the marine environment and forecast data. This data is then managed so that users can access it in real time.

[1056] Step 8:

[1057] The server receives requests from users, who then use their smartphones or PCs to send requests for data and analysis results for specific ocean areas.

[1058] Step 9:

[1059] The server retrieves the appropriate data from a database based on the request, and returns it immediately to the user.

[1060] Step 10:

[1061] Users can view the provided data on their smartphones or PCs. For example, they can open a smartphone app and check current ocean current information and the optimal navigation route for their vessel. Based on the analysis results, users can navigate safely and conduct efficient fishing activities.

[1062] Example 1

[1063] 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."

[1064] There is a need for a means to quickly and accurately grasp changes in the marine environment, but existing technologies have difficulty in collecting and analyzing data in real time. Furthermore, there is a lack of a consistent system for effectively processing, storing, analyzing, and providing collected data, which means that users cannot obtain the information they need in a timely manner.

[1065] 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.

[1066] In this invention, the server includes a collection device means for collecting oceanographic data in real time, a preprocessing device means for initially processing the collected oceanographic data, a communication device means for uploading the preprocessed data to a centralized processing device on the cloud, a recording device means for saving the uploaded data, a multi-mode compatible artificial intelligence device means for analyzing the saved data, and an information providing device means for providing the analysis results to users, thereby enabling efficient collection, processing, analysis, and provision of real-time observation data of the marine environment.

[1067] The "collection device means" is a device for collecting oceanographic data such as ocean currents, wave heights, and wave directions in real time.

[1068] The "preprocessing device means" is a device for initially processing collected oceanographic data, removing noise, and standardizing the data format.

[1069] The "communication device means" is a device for uploading pre-processed data to a centralized processing device on the cloud.

[1070] "Recording device means" refers to a device for storing uploaded data.

[1071] The "multi-modal artificial intelligence device means" is an artificial intelligence device that can integrate multiple sensor information to analyze stored data.

[1072] The "information providing device means" is a device for providing the analysis results to the user.

[1073] This invention is a system for collecting, processing, and analyzing oceanographic data and providing it to users in real time. An embodiment of this system will be described in detail below.

[1074] System Overview

[1075] The system consists of the following main components:

[1076] 1. Collection Device Means

[1077] 2. Pretreatment Device Means

[1078] 3. Communication Device Means

[1079] 4. Recording Device Means

[1080] 5. Multimodal Artificial Intelligence Device Means

[1081] 6. Information provision device means

[1082] Collection Device Means

[1083] The collection device means is a device for collecting oceanographic data such as ocean currents, wave height, and wave direction in real time. For example, data is acquired using various sensors (ocean current sensors, wave height meters, etc.) installed on the sea surface. This data is first stored in temporary memory within the collection device.

[1084] Pretreatment device means

[1085] The preprocessing means involves processes to remove noise from the collected raw data and convert it into a standard format. The preprocessed data is then standardized into a form suitable for subsequent analysis. Software for noise filtering algorithms and formatting is used.

[1086] communication device means

[1087] The communication device means includes a communication module for uploading pre-processed data to a centralized processing unit on the cloud. Communication is mainly performed via LTE or satellite communication, which enables real-time data transmission even in remote locations.

[1088] Recording device means

[1089] The recording device is a database stored on a cloud server, which stores and manages the data uploaded in real time. The data is recorded in an orderly format and is designed to be immediately accessible when needed.

[1090] Multimodal artificial intelligence device means

[1091] The multi-modal AI system is an AI system that integrates multiple sensor information and performs highly accurate analysis to generate detailed models and predictions of the marine environment. This AI system also integrates data from sonar and drones to provide richer information. For example, deep learning frameworks such as TensorFlow and PyTorch are used.

[1092] Information providing device means

[1093] The information providing device means is an interface for providing analyzed data to users. Users can view the analysis results in real time via a PC or smartphone. Data is provided via a web interface or mobile application.

[1094] Specific examples

[1095] Example 1: Navigation support system

[1096] The device collects ocean current data in the Pacific Ocean and uploads it to a cloud server, where it is analyzed and provided to the user's navigation app as an optimal route.

[1097] Example 2: Tsunami observation system

[1098] When the device detects abnormal wave height, it reports it to the cloud in real time. The server analyzes this data and generates a tsunami warning, allowing users to immediately begin evacuation.

[1099] Example 3: Fisheries Management System

[1100] The device acquires fish detection data from the coastal waters and uploads it to the cloud, where the server analyzes the data and provides information on optimal fishing spots to the user's smartphone.

[1101] Prompt Sentence Examples

[1102] TXT

[1103] We have developed a system that collects, processes, analyzes, and provides real-time oceanographic data to users. This system consists of six main components: collection device means, pre-processing device means, communication device means, recording device means, multi-mode AI device means, and information provision device means. Please explain in detail.

[1104] 1. Oceanographic data collection

[1105] 2. Initial data processing

[1106] 3. Real-time uploading

[1107] 4. Data storage

[1108] 5. Data Analysis

[1109] 6. Data provision

[1110] As described above, the system of the present invention can provide users with highly accurate, real-time information by consistently performing the collection, analysis, and provision of oceanographic data.

[1111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1112] The flow of this system's program processing

[1113] Step 1: Collecting oceanographic data (terminal)

[1114] Specific description:

[1115] The terminal uses an ocean antenna to collect ocean data in real time. Specifically, data is acquired through various sensors (e.g., ocean current sensors, wave height meters, etc.) and the collected data is stored in temporary memory.

[1116] Input and Output:

[1117] Input: Oceanographic data acquired by sensors (e.g., ocean currents, wave height, wave direction)

[1118] Output: Raw data stored in temporary memory

[1119] Specific behavior:

[1120] The device retrieves the latest ocean current data at a specified point in the South Pacific.

[1121] The sensor measures the seawater temperature and salinity at regular intervals (such as every second) and transmits the data to the terminal.

[1122] Step 2: Initial processing of data (terminal)

[1123] Specific description:

[1124] The terminal removes noise from the collected data and standardizes the format. Using noise filtering algorithms and formatting standardization software, the raw data is converted into a standard format.

[1125] Input and Output:

[1126] Input: raw data collected

[1127] Output: Denoised and uniformly formatted data

[1128] Specific behavior:

[1129] The terminal uses a moving average filter to remove outliers from the collected wave height data.

[1130] Convert seawater temperature data into CSV format.

[1131] Step 3: Real-time upload (terminal)

[1132] Specific description:

[1133] The device uploads the preprocessed data to the cloud server. The data is sent to the cloud server via wireless communication (LTE or satellite communication).

[1134] Input and Output:

[1135] Input: Preprocessed data

[1136] Output: Data uploaded to the cloud server

[1137] Specific behavior:

[1138] The terminal uses an LTE line to send preprocessed wave height data to a cloud server.

[1139] The device uploads new data to the server in bulk every minute.

[1140] Step 4: Storing data (server)

[1141] Specific description:

[1142] The server stores the received data in a database, and the cloud server immediately records and indexes the data received for quick access.

[1143] Input and Output:

[1144] Input: Data uploaded to the cloud server

[1145] Output: Data stored in the database

[1146] Specific behavior:

[1147] The server receives new ocean current data and records it in the SQL database using INSERT statements.

[1148] Add a timestamp when saving data.

[1149] Step 5: Analyzing the data (server)

[1150] Specific description:

[1151] The server uses multi-modal artificial intelligence to integrate and perform detailed analysis of the multiple sensor data, generating detailed models and predictions of the marine environment.

[1152] Input and Output:

[1153] Input: Saved data

[1154] Output: Analyzed data and predictive models

[1155] Specific behavior:

[1156] The server uses TensorFlow and PyTorch to integrate sonar and drone data to generate a highly accurate ocean current model.

[1157] The AI ​​model makes future predictions about the marine environment and stores the results on a server.

[1158] Step 6: Providing Data (Server & User)

[1159] Specific description:

[1160] The server provides the analysis results to the user, who then views the data, which is provided through a web interface or mobile application.

[1161] Input and Output:

[1162] Input: Parsed data

[1163] Output: Data presented in a user-viewable format

[1164] Specific behavior:

[1165] The server provides the analysis results through web applications and mobile applications.

[1166] A user checks recommended navigation route information on their smartphone.

[1167] (Application example 1)

[1168] 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."

[1169] One of the challenges facing modern food delivery systems is a lack of technology to efficiently and quickly deliver fresh seafood. While it is possible to collect oceanographic data and use it to identify optimal fishing grounds, there is no system in place to use that information to quickly deliver fresh seafood to consumers. In particular, there is a need for technology that optimizes the supply chain from fisheries to consumers by calculating efficient delivery routes using real-time oceanographic data and controlling delivery methods such as drones.

[1170] 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.

[1171] In this invention, the server includes marine antenna means for collecting marine data in real time, preprocessing means for initially processing the collected marine data, communication means for uploading the preprocessed data to a server on the cloud, database means for storing the uploaded data, multimodal AI means for analyzing the stored data, data providing means for providing the analysis results to a user, delivery means for efficiently delivering fresh seafood using the data providing means, and route calculation means for controlling the delivery means and calculating an optimal delivery route. This enables efficient and prompt delivery of fresh seafood based on the marine data collected in real time.

[1172] The "marine antenna means" is a device for collecting marine data such as ocean currents, wave heights, and wave directions in real time.

[1173] A "pre-processing means" is a device that includes a process for removing noise from collected raw data and converting it into a standard format.

[1174] The "communication means" is a communication module for uploading preprocessed data to a server on the cloud.

[1175] "Database means" refers to a database placed on a cloud server, and is a system that stores and manages data uploaded in real time.

[1176] "Multimodal AI means" is an artificial intelligence system that integrates information from multiple sensors and performs highly accurate analysis.

[1177] The "data providing means" is an interface for providing analyzed data to the user.

[1178] "Delivery methods" refer to logistics methods for efficiently delivering fresh seafood, including drones and vehicles.

[1179] The "route calculation means" is a system for controlling delivery means and calculating the optimal delivery route.

[1180] This invention is a marine data collection, analysis and provision system for efficiently providing fresh seafood. The system consists of the following main components:

[1181] Hardware Configuration

[1182] 1. Marine Antenna Means

[1183] The various sensors include sensors that measure ocean currents, wave height, wave direction, etc. These sensor data are acquired in real time and stored in temporary memory.

[1184] 2. Terminal

[1185] The devices include smartphones and drones, which collect, pre-process and upload real-time data.

[1186] 3. Cloud Server

[1187] It is equipped with a database means and a multimodal AI means, and has an interface for providing analysis results to users.

[1188] Software Configuration

[1189] 1. Pretreatment methods

[1190] Software that includes processes for denoising raw data and converting it into a standard format.

[1191] 2. Means of communication

[1192] A communication module for uploading pre-processed data to a cloud server using LTE or satellite communications.

[1193] 3. Database Means

[1194] Software for storing and managing data uploaded in real time in a database on a cloud server.

[1195] 4. Multimodal AI Methods

[1196] An AI system that integrates information from multiple sensors and performs highly accurate analysis. For example, it can integrate data from sonar and drones to generate detailed models and predictions of the marine environment.

[1197] 5. Means of providing data

[1198] Software with an interface for providing analysis results to users.

[1199] 6. Delivery method

[1200] Logistics methods such as drones and vehicles to efficiently deliver fresh seafood.

[1201] 7. Route Calculation Method

[1202] A system that controls delivery means and calculates the optimal delivery route.

[1203] Successful examples

[1204] 1. Oceanographic data collection

[1205] Data such as ocean currents, wave height, and wave direction is collected in real time and stored in temporary memory.

[1206] 2. Pretreatment

[1207] The collected data is denoised and converted into a standard format, e.g., wave height data is filtered.

[1208] 3. Upload your data

[1209] The preprocessed data is uploaded to a cloud server via an LTE line or other means.

[1210] 4. Data Analysis

[1211] Multimodal AI is used to integrate data and generate highly accurate ocean models, for example, using sonar and drone data to generate detailed ocean current models.

[1212] 5. Data provision

[1213] The analysis results are provided to users, who can view real-time data via their smartphones and determine the optimal route for delivering fresh seafood.

[1214] Food delivery as a concrete example

[1215] 1. Optimal fishing ground identification system

[1216] Ocean data is collected and information on optimal fishing spots is uploaded to a cloud server. This information is analyzed and provided to users as optimal fishing spots.

[1217] 2. Real-time fresh seafood delivery system

[1218] Based on oceanographic data collected by drones, fresh seafood is delivered via the optimal route, and users can check the delivery status in real time via their smartphones.

[1219] Prompt Sentence Examples

[1220] Fisheries management system using ocean data. Please implement a function to collect and analyze fishing ground information in real time and provide users with the best fishing ground information.

[1221] As described above, the present invention is a system that can efficiently deliver fresh seafood to users by consistently performing everything from collecting marine data to analyzing it, providing the data, and delivering it along the optimal route.

[1222] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1223] Step 1:

[1224] The terminal collects oceanographic data

[1225] Input: Various sensor data (ocean currents, wave height, wave direction, etc.)

[1226] Specific operation: Acquire ocean data in real time using ocean antenna means. For example, the terminal collects data from a wave direction sensor and stores it in temporary memory.

[1227] Output: Raw data

[1228] Step 2:

[1229] The device preprocesses the data

[1230] Input: Raw data

[1231] Specific operation: Performs noise removal and standard format conversion. For example, it filters the wave height data acquired by the device and converts it into a unified format.

[1232] Output: Preprocessed, consistent data

[1233] Step 3:

[1234] The device uploads the preprocessed data to the cloud server.

[1235] Input: Preprocessed data

[1236] Specific operation: Data is transmitted to a cloud server using LTE or satellite communication. For example, the terminal uploads preprocessed ocean current data to the cloud server.

[1237] Output: Data stored on the cloud server

[1238] Step 4:

[1239] The server stores the data

[1240] Input: Data uploaded to the cloud server

[1241] Specific operation: Data is stored and managed using database means. For example, a cloud server records ocean current data in an SQL database.

[1242] Output: Real-time data stored in a database

[1243] Step 5:

[1244] The server analyzes the data

[1245] Input: Saved data

[1246] How it works: Multimodal AI methods are used to analyze data and generate predictive models. For example, a server integrates data from sonar and drones to generate a highly accurate ocean current model.

[1247] Output: Analysis results and predicted data

[1248] Step 6:

[1249] The server provides the analysis results to the user

[1250] Input: Model and predicted data as analysis results

[1251] Specific operation: The analysis results are provided to the user using the data provision means. For example, the user can view optimal fishing ground information in real time via their smartphone.

[1252] Output: Analysis result data provided to the user

[1253] Step 7:

[1254] The server controls the delivery method

[1255] Input: Parsed oceanographic data and shipping routes

[1256] Specific operation: The route calculation means is used to analyze data and calculate the optimal delivery route. For example, the server calculates and instructs a drone's delivery route to efficiently deliver fresh seafood.

[1257] Output: Optimal delivery route and control instructions

[1258] Step 8:

[1259] Delivery method provides fresh seafood

[1260] Input: Optimal delivery route and control instructions

[1261] Specific operation: Drones and vehicles efficiently deliver fresh seafood. For example, drones deliver fresh seafood to consumers by following optimal routes.

[1262] Output: Fresh seafood delivered to the consumer

[1263] 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.

[1264] The present invention is a system that collects, processes, and analyzes oceanographic data and provides it to users in real time, while also recognizing the user's emotions and dynamically adjusting the content of the data provided. An embodiment of this system will be described in detail below.

[1265] System Overview

[1266] The system consists of the following main components:

[1267] 1. Marine Antenna Means

[1268] 2. Pretreatment Methods

[1269] 3. Means of communication

[1270] 4. Database Means

[1271] 5. Multimodal AI Methods

[1272] 6. Means of providing data

[1273] 7. Emotion Engine

[1274] Marine Antenna Means

[1275] The marine antenna means is a device for collecting marine data such as ocean currents, wave height, and wave direction in real time. For example, data is acquired using various sensors installed on the sea surface. This data is first stored in temporary memory within the antenna.

[1276] Pretreatment means

[1277] Pre-processing involves processes to remove noise from the collected raw data and convert it into a standard format, which is suitable for subsequent analysis and interpretation.

[1278] communication means

[1279] The communication means includes a communication module for uploading pre-processed data to a cloud server. Communication is mainly via LTE or satellite communications, which enables real-time data transmission even in remote locations.

[1280] Database Means

[1281] The database means is a database placed on a cloud server, which stores and manages data uploaded in real time. The data is recorded in an orderly format and is designed to be immediately accessible when needed.

[1282] Multimodal AI methods

[1283] Multimodal AI tools are artificial intelligence systems that integrate multiple sensor information and perform highly accurate analysis to generate detailed models and predictions of the marine environment. These AI systems also integrate data from sonar and drones to provide richer information.

[1284] Data provision method

[1285] The data provision means is an interface for providing analyzed data to users, who can view the analysis results in real time via their PCs or smartphones.

[1286] Emotion Engine

[1287] The emotion engine is a system that recognizes the user's emotions, analyzes the data, and adjusts the display and content of the oceanographic data provided. For example, if the user is stressed, the data will be displayed more simply and easily understandable.

[1288] A natural language description of what the program does

[1289] The processing flow of the system program and its specific operations will be explained below.

[1290] 1. Ocean data collection (terminal)

[1291] The terminal acquires oceanographic data in real time through an ocean antenna. For example, the terminal reads ocean current data in the South Pacific Ocean from a sensor.

[1292] 2. Initial processing of data (terminal)

[1293] The device removes noise from the data it acquires and standardizes the format. For example, the device filters wave height data and converts it into a standard format.

[1294] 3. Real-time upload (terminal)

[1295] The device uploads the data to the cloud server. For example, the device transmits the pre-processed data to the server using an LTE connection.

[1296] 4. Data storage (server)

[1297] The server receives the uploaded data, checks the integrity of the data, and checks for any abnormalities. If there are no problems, the server records the data in a database.

[1298] 5. Data analysis (server)

[1299] The server uses multimodal AI to integrate information from multiple sensors and generate a detailed model of the marine environment, for example by analyzing flow patterns using multiple ocean current data.

[1300] 6. User Emotion Recognition (Server)

[1301] The server runs an emotion engine to collect and analyze the user's emotion data, for example, to determine whether the user is feeling stressed.

[1302] 7. Coordination of data provision (server)

[1303] The server adjusts how it presents data based on the user's emotions, for example, if the user is feeling stressed, it will present a concise summary of the data.

[1304] 8. Data Provision (Server & User)

[1305] The server provides the analysis results to the user, who then views the data on their smartphone or PC. For example, the user can check real-time oceanographic data and analysis results on their smartphone.

[1306] Specific examples

[1307] Example 1: Navigation support system

[1308] The device collects ocean current data in the Pacific Ocean and uploads it to a cloud server. This data is then analyzed and provided to the user's e-navigator app as an optimized route. Furthermore, an emotion engine recognizes the user's stress level and adjusts the displayed data accordingly.

[1309] Example 2: Tsunami observation system

[1310] When the device detects abnormal wave height, it reports it to the cloud in real time. The server analyzes this data and generates a tsunami warning. The emotion engine detects the user's emotions and displays evacuation instructions more intuitively, allowing the user to immediately begin evacuation.

[1311] Example 3: Fisheries Management System

[1312] The device collects fish detection data from the coastal waters and uploads it to the cloud. The server analyzes the data and provides optimal fishing spot information to the user's smartphone. The emotion engine analyzes the user's emotions and adjusts the priority and presentation of the information.

[1313] As described above, the system of the present invention provides optimal information to users by consistently performing all steps from collecting marine data to analyzing and providing it, as well as recognizing and adjusting user emotions.

[1314] The processing flow will be explained below.

[1315] Step 1:

[1316] The device uses an ocean antenna to collect ocean data such as ocean currents, wave height, and wave direction. The device acquires the data via sensors installed in the ocean and stores it in temporary memory. Specifically, the sensors measure the wave height on the ocean surface and transmit the data to the device.

[1317] Step 2:

[1318] This performs initial processing of raw data collected by the device. First, noise is removed through filtering. Then, a conversion process is performed to standardize the data format. For example, data collected at irregular time intervals is standardized to a timestamp with a regular interval.

[1319] Step 3:

[1320] The device uploads the preprocessed data to a cloud server. The device establishes a secure connection using LTE or satellite communications and transmits the data via HTTPS. For example, ocean current data for the South Pacific Ocean is uploaded to the cloud.

[1321] Step 4:

[1322] The server receives the uploaded data, checks the integrity of the data, and checks for any abnormalities. If there are no problems, the server records the data in a database.

[1323] Step 5:

[1324] The server stores the received data in a database. The stored data is used for subsequent analysis. The server also backs up the stored data in parallel to prevent data loss.

[1325] Step 6:

[1326] The server launches the multimodal AI and begins analyzing the data. The server integrates information from multiple sensors, such as sonar and drones, to generate a detailed model of the marine environment. For example, it analyzes flow patterns using multiple ocean current data.

[1327] Step 7:

[1328] The server then stores the analysis results in a database, which includes detailed information on the marine environment and forecast data. This data is then managed so that users can access it in real time.

[1329] Step 8:

[1330] The server starts the emotion engine, collects and analyzes the user's emotion data, and evaluates the user's state based on the emotion data obtained from the user's smartphone or PC. For example, it determines whether the user is feeling stressed.

[1331] Step 9:

[1332] The server dynamically adjusts the way data is presented based on the user's emotions. For example, if the user is feeling stressed, the server will avoid displaying complex data and provide simple, visually easy-to-understand information.

[1333] Step 10:

[1334] The server receives requests from users. Users use their smartphones or PCs to send requests for data and analysis results for specific ocean areas. For example, a user requests detailed data on a particular fishing ground.

[1335] Step 11:

[1336] The server retrieves the appropriate data from the database based on the request, and immediately returns the retrieved data to the user. For example, it retrieves the latest ocean current data for the South Pacific and provides it to the user.

[1337] Step 12:

[1338] Users can view the provided data in real time on their smartphones or PCs. For example, a user can open a smartphone app and check data for optimizing navigation routes or managing fisheries. The app takes into account the user's emotional state and displays the necessary information in an appropriate format.

[1339] Example 2

[1340] 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."

[1341] Conventional oceanographic data collection systems have the problem that the analysis and provision of acquired data is one-way, and they are unable to respond flexibly to the user's emotions or circumstances. Furthermore, when collecting and analyzing data in real time, managing huge amounts of data and ensuring the speed of analysis becomes an issue. This makes it difficult to provide users with the information they need, and there has been a demand for improved usability.

[1342] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1343] In this invention, the server includes marine antenna means for collecting marine data in real time, preprocessing means for initially processing the collected marine data, communication means for uploading the preprocessed data to a server on the cloud, database means for saving the uploaded data, multimodal AI means for analyzing the saved data, data providing means for providing the analysis results to the user, and emotion engine means for analyzing the user's emotions and dynamically adjusting the content to be provided. This makes it possible to effectively collect and analyze data in real time and to appropriately provide necessary information based on the user's emotions.

[1344] The "marine antenna means" is a device for collecting marine data such as ocean currents, wave heights, and wave directions in real time.

[1345] A "pre-processing means" is a device that includes a process for removing noise from collected raw data and converting it into a standard format.

[1346] A "communication means" is a device that includes a communication module for uploading preprocessed data to a server on the cloud.

[1347] "Database means" refers to a database placed on a cloud server, and is a device for storing and managing data uploaded in real time.

[1348] A "multimodal AI means" is a device that includes an artificial intelligence system for integrating information from multiple sensors and performing highly accurate analysis.

[1349] A "data providing means" is a device that includes an interface for providing analyzed data to a user.

[1350] The "emotion engine means" is a device that includes a system that recognizes the user's emotions, analyzes the data, and dynamically adjusts the display method and content of the oceanographic data provided.

[1351] The system of this invention collects, processes, and analyzes oceanographic data, and provides it to users in real time, while also recognizing the user's emotions and dynamically adjusting the content provided. The system consists of the following main components:

[1352] Components

[1353] 1. Marine Antenna Means

[1354] The marine antenna means is a device for collecting marine data such as ocean currents, wave height, and wave direction in real time. For example, it uses various sensors installed on the sea surface. This data is first stored in a temporary memory within the antenna.

[1355] 2. Pretreatment Methods

[1356] Pre-processing involves processes to remove noise from the collected raw data and convert it into a standard format, which is suitable for subsequent analysis and interpretation.

[1357] 3. Means of communication

[1358] The communication means includes a communication module for uploading pre-processed data to a cloud server. Communication is mainly via LTE or satellite communications, which enables real-time data transmission even in remote locations.

[1359] 4. Database Means

[1360] The database means is a database placed on a cloud server, which stores and manages data uploaded in real time. The data is recorded in an orderly format and is designed to be immediately accessible when needed.

[1361] 5. Multimodal AI Methods

[1362] Multimodal AI methods are artificial intelligence systems that integrate multiple sensor information and perform highly accurate analysis to generate detailed models and predictions of the marine environment. These AI systems are built using TensorFlow and PyTorch, for example. They also integrate data from sonar and drones to provide richer information.

[1363] 6. Means of providing data

[1364] The data provision means is an interface for providing analyzed data to users, who can view the analysis results in real time via their PCs or smartphones.

[1365] 7. Emotional Engine Means

[1366] The emotion engine is a system that recognizes the user's emotions, analyzes the data, and dynamically adjusts the display method and content of the oceanographic data provided. For example, if the user is under stress, the data will be displayed in a more concise and easy-to-understand manner. The emotion recognition algorithm uses IBM Watson's emotion analysis.

[1367] Specific examples

[1368] Example 1: Navigation support system

[1369] The device collects ocean current data in the Pacific Ocean and uploads it to a cloud server. This data is analyzed by the server and provided to the user's navigation support app as an optimal route. In addition, an emotion engine recognizes the user's stress level and briefly adjusts the data presented.

[1370] Example prompt: "Based on ocean current data collected in the Pacific Ocean, provide the best sailing route. Also, assess the user's stress level and present the data in a concise format."

[1371] Example 2: Tsunami observation system

[1372] When the device detects abnormal wave height, it reports it to the cloud in real time. The server analyzes this data and generates a tsunami warning. The emotion engine analyzes the user's emotions and displays evacuation instructions more intuitively.

[1373] Example prompt: "If abnormal wave heights are detected, generate a real-time tsunami warning and provide evacuation instructions to the user in an intuitive manner."

[1374] Example 3: Fisheries Management System

[1375] The device collects fish detection data from the coastal waters and uploads it to the cloud. The server analyzes the data and provides optimal fishing spot information to the user's smartphone. The emotion engine analyzes the user's emotions and adjusts the priority and presentation of the information.

[1376] Example prompt: "Based on inshore fish detection data, provide information on optimal fishing spots. Also, assess user sentiment and adjust how the information is presented."

[1377] As described above, the system of the present invention provides optimal information to users by consistently performing all steps from collecting marine data to analyzing and providing it, as well as recognizing and adjusting user emotions.

[1378] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1379] Step 1:

[1380] Ocean data collection (terminal)

[1381] The terminal acquires oceanographic data in real time through an ocean antenna. Specifically, the terminal acquires data such as ocean currents, wave height, and wave direction from sensors installed in the Pacific Ocean at one-second intervals and records it in temporary memory. The input at this time is raw data from each sensor, and the output is raw oceanographic data stored in temporary memory.

[1382] Step 2:

[1383] Initial data processing (terminal)

[1384] The terminal removes noise from the acquired data and standardizes the format. In operation, the terminal uses a filtering algorithm to remove noise from the raw data and then converts the data into a standard format (e.g., CSV format). The input is the raw oceanographic data saved in step 1, and the output is the data that has been removed and converted into a standard format.

[1385] Step 3:

[1386] Real-time upload (terminal)

[1387] The device uploads the preprocessed data to a cloud server. Specifically, the device uses an LTE or satellite communication module to transmit the filtered data to the cloud server in packets every minute. The input is the preprocessed data, and the output is the data transmitted to the cloud server.

[1388] Step 4:

[1389] Data storage (server)

[1390] The server receives the data uploaded to the cloud, verifies it, and then records it in a database. The server then checks the integrity of the data it receives to ensure there are no outliers or missing data. It then stores the data in a database (e.g., Amazon RDS). The input at this time is the data uploaded to the cloud server, and the output is the data stored in the database, the integrity of which has been confirmed.

[1391] Step 5:

[1392] Data analysis (server)

[1393] The server uses multimodal AI to integrate multiple sensor information and generate a detailed marine environment model. Specifically, the server integrates collected ocean current and wave data and runs an AI model using TensorFlow and PyTorch, thereby generating ocean current patterns and predictions. The input is sensor data whose consistency has been confirmed, and the output is an analyzed marine environment model.

[1394] Step 6:

[1395] User emotion recognition (server)

[1396] The server launches an emotion engine to collect and analyze the user's emotional data. In operation, the server uses an emotion recognition algorithm (for example, IBM Watson's emotion analysis) to analyze the user's voice input and text messages and determine their stress level and relaxation level. The input is the user's emotional data, and the output is analyzed emotional information.

[1397] Step 7:

[1398] Coordination of data provision (server)

[1399] The server adjusts how it presents data based on the user's emotions. Specifically, it adjusts the amount and format of data presented based on the results obtained from the emotion engine. If the user is feeling stressed, it uses simple, visually easy-to-understand graphs and infographics. The inputs are the analyzed emotion information and the marine environment model, and the output is the adjusted display format of the data.

[1400] Step 8:

[1401] Data provision (server & user)

[1402] The server provides the analysis results to the user, who then views the data on their smartphone or PC. Specifically, the server sends the analysis results to the user's application via a Web API, and the user can view real-time oceanographic data and analysis results through a smartphone app or PC web browser. The input is data in an adjusted display format, and the output is the analysis results displayed on the user's device.

[1403] (Application example 2)

[1404] 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."

[1405] Conventional seafood delivery systems have difficulty effectively utilizing oceanographic data, making it impossible to grasp the status of fishing grounds and environmental changes in real time. This makes it difficult to efficiently supply fresh seafood and to optimize deliveries. Furthermore, the system is unable to adjust information provision based on the user's emotions, often resulting in a poor user experience. These issues need to be resolved.

[1406] 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.

[1407] In this invention, the server includes a marine antenna means for collecting marine data in real time, a preprocessing means for initially processing the collected marine data, a communication means for uploading the preprocessed data to a server on the cloud, a database means for saving the uploaded data, a multimodal AI means for analyzing the saved data, a data provision means for providing the analysis results to the user, an emotion engine that recognizes the user's emotions and dynamically adjusts the data provision method, and a route optimization means for using the analyzed marine data to support food delivery via the optimal route. This enables the server to analyze the marine data and provide information on optimal fishing grounds, as well as provide information based on the user's emotions, thereby realizing efficient delivery of fresh seafood that satisfies the user.

[1408] The "marine antenna means" is a device for collecting marine data such as ocean currents, wave heights, and wave directions in real time.

[1409] A "pre-processing means" is a device that includes a process for removing noise from collected raw data and converting it into a standard format.

[1410] A "communication means" is a device that includes a communication module for uploading preprocessed data to a server on the cloud.

[1411] "Database means" refers to a database placed on a cloud server, and is a device that serves to store and manage data uploaded in real time.

[1412] "Multimodal AI means" is an artificial intelligence system that integrates information from multiple sensors and performs highly accurate analysis.

[1413] The "data providing means" is an interface for providing analyzed data to the user.

[1414] An "emotion engine" is a system that recognizes a user's emotions, analyzes that data, and dynamically adjusts the display method and content of the information provided.

[1415] The "route optimization tool" is a device that has the function of supporting the procurement and delivery of fresh seafood along the optimal route based on analyzed oceanographic data.

[1416] The system for realizing the present invention comprises the following main components:

[1417] Oceanographic data collection

[1418] The server collects oceanographic data in real time from various sensors installed on the ocean surface, such as data on ocean currents, wave height, wave direction, etc. This data is first collected by an ocean antenna means and stored in temporary memory.

[1419] Initial processing of data

[1420] The server uses pre-processing means to remove noise from the collected data and convert it into a standard format, specifically filtering the wave height data and standardizing it into a format suitable for analysis.

[1421] Uploading data to the cloud

[1422] The preprocessed data is uploaded to a cloud server using a communication method, mainly via LTE or satellite communication, which enables real-time data transmission even from remote locations.

[1423] Data storage

[1424] The cloud server stores the uploaded data using a database system, which is designed to record the data in an organized format and make it instantly accessible when needed.

[1425] Data analysis

[1426] The server analyzes the stored data using multimodal AI methods, integrating information from multiple sensors to generate highly accurate marine environment models and predictions, enabling the provision of richer information.

[1427] User Emotion Recognition

[1428] The server uses an emotion engine to collect and analyze the user's emotional data, for example to determine whether the user is feeling stressed, and dynamically adjusts the display and content of the information provided to the user based on this data.

[1429] Customization of information provided

[1430] When a user views the analysis results on a PC or smartphone, the server takes into account the user's emotions and adjusts the display to display information concisely and intuitively, improving the user experience.

[1431] Fresh seafood delivery assistance

[1432] Based on the analysis results, route optimization methods are used to assist food delivery, providing information on the best fishing spots and enabling the fastest delivery of fresh seafood via the most optimal route.

[1433] Specific examples

[1434] As a concrete example, consider a fresh seafood delivery support system. The user's smartphone app displays information on optimal fishing grounds based on oceanographic data collected in real time. Based on this information, delivery personnel can purchase and deliver seafood along the optimal route. In addition, an emotion engine analyzes the user's emotions and adjusts the way information is displayed, allowing the user to obtain information without feeling stressed.

[1435] Prompt Sentence Examples

[1436] Here is an example of a prompt for generating code using a generative AI model:

[1437] I am implementing a seafood delivery support app. Please generate code for the following functions:

[1438] 1. Ability to collect and process oceanographic data in real time

[1439] 2. Ability to upload data to the cloud

[1440] 3. Function to analyze data and provide optimal fishing ground information

[1441] 4. Ability to customize information display based on user emotional data

[1442] 5. A feature that provides optimal routes for delivering fresh seafood.

[1443] Please provide the specific code and explanation for each function.

[1444] In this way, the system of the present invention consistently collects, analyzes, and provides marine data, as well as recognizes and adjusts user emotions, thereby providing optimal information to users and enabling the efficient and effective delivery of fresh seafood.

[1445] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1446] Step 1:

[1447] The terminal collects oceanographic data in real time through an ocean antenna means. This oceanographic data includes ocean currents, wave height, wave direction, etc. The input is data from sensors installed on the sea surface, and the output is raw oceanographic data stored in temporary memory. Specifically, the terminal reads data from the sensors and stores it in real time.

[1448] Step 2:

[1449] The terminal uses pre-processing means to remove noise from the collected oceanographic data and convert it into a standard format. The input is raw oceanographic data stored in temporary memory, and the output is data that has been filtered and converted into a standard format. Specifically, the terminal removes noise from the data and converts wave height and ocean current data into a format that is easy to analyze.

[1450] Step 3:

[1451] The device uploads the preprocessed data to a server on the cloud using a communication means. The input is the preprocessed data, and the output is the data sent to the cloud server. Specifically, the device transfers the data to the server in real time using an LTE line or satellite communication.

[1452] Step 4:

[1453] The server uses a database on the cloud to store and manage the uploaded data. The input is the data sent to the cloud server, and the output is the organized data recorded in the database. Specifically, the server checks the integrity of the data and checks for any abnormal values ​​before saving it to the database.

[1454] Step 5:

[1455] The server uses multimodal AI methods to analyze the stored data. The input is oceanographic data stored in the database, and the output is an analyzed ocean environmental model and prediction information. Specifically, the server integrates information from multiple sensors and performs analysis to generate a highly accurate ocean environmental model.

[1456] Step 6:

[1457] The server uses an emotion engine to collect and analyze the user's emotional data. The input is data indicating the user's emotional state, and the output is the analyzed user's emotional information. Specifically, the server uses an emotion recognition algorithm to determine whether the user is feeling stressed.

[1458] Step 7:

[1459] The server adjusts the data presentation method based on the user's emotions. The input is the analyzed marine environment model, forecast information, and the user's emotional information, and the output is the adjusted data presentation method. Specifically, the server dynamically adjusts the user interface to present information concisely and intuitively.

[1460] Step 8:

[1461] The server uses a route optimization method to support the procurement and delivery of fresh seafood along the optimal route. The input is the analyzed oceanographic data and the user's delivery request, and the output is the optimal delivery route. Specifically, the server provides the delivery person with the optimized route, taking into account the shortest route and traffic information.

[1462] By processing and calculating input data at each step and generating the required output, the system embodying this invention can operate efficiently.

[1463] 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.

[1464] 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.

[1465] 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.

[1466] [Fourth embodiment]

[1467] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1468] 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.

[1469] 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).

[1470] 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.

[1471] 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.

[1472] 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).

[1473] 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.

[1474] 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.

[1475] 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.

[1476] 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.

[1477] 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.

[1478] 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.

[1479] 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."

[1480] The present invention is a system for collecting, processing, and analyzing oceanographic data and providing it to users in real time. An embodiment of this system will be described in detail below.

[1481] System Overview

[1482] The system consists of the following main components:

[1483] 1. Marine Antenna Means

[1484] 2. Pretreatment Methods

[1485] 3. Means of communication

[1486] 4. Database Means

[1487] 5. Multimodal AI Methods

[1488] 6. Means of providing data

[1489] Marine Antenna Means

[1490] The marine antenna means is a device for collecting marine data such as ocean currents, wave height, and wave direction in real time. For example, data is acquired using various sensors installed on the sea surface. This data is first stored in temporary memory within the antenna.

[1491] Pretreatment means

[1492] Pre-processing involves processes to remove noise from the collected raw data and convert it into a standard format, which is suitable for subsequent analysis and interpretation.

[1493] communication means

[1494] The communication means includes a communication module for uploading pre-processed data to a cloud server. Communication is mainly via LTE or satellite communications, which enables real-time data transmission even in remote locations.

[1495] Database Means

[1496] The database means is a database placed on a cloud server, which stores and manages data uploaded in real time. The data is recorded in an orderly format and is designed to be immediately accessible when needed.

[1497] Multimodal AI methods

[1498] Multimodal AI tools are artificial intelligence systems that integrate multiple sensor information and perform highly accurate analysis to generate detailed models and predictions of the marine environment. These AI systems also integrate data from sonar and drones to provide richer information.

[1499] Data provision method

[1500] The data provision means is an interface for providing analyzed data to users, who can view the analysis results in real time via their PCs or smartphones.

[1501] A natural language description of what the program does

[1502] The processing flow of the system program and its specific operations will be explained below.

[1503] 1. Ocean data collection (terminal)

[1504] The terminal acquires oceanographic data in real time through an ocean antenna. For example, the terminal reads ocean current data in the South Pacific Ocean from a sensor.

[1505] 2. Initial processing of data (terminal)

[1506] The device removes noise from the data it acquires and standardizes the format. For example, the device filters wave height data and converts it into a standard format.

[1507] 3. Real-time upload (terminal)

[1508] The device uploads the data to the cloud server. For example, the device transmits the pre-processed data to the server using an LTE connection.

[1509] 4. Data storage (server)

[1510] The server stores the received data in a database. For example, the server records ocean current data for the South Pacific Ocean in an SQL database.

[1511] 5. Data analysis (server)

[1512] The server uses multimodal AI to integrate information from multiple sensors and perform detailed analysis. For example, the server integrates sonar and drone data to generate a highly accurate ocean current model.

[1513] 6. Data Provision (Server & User)

[1514] The server provides the analysis results to the user, who then views the data on their smartphone or PC. For example, the user can check recommended navigation route information in real time on their smartphone.

[1515] Specific examples

[1516] Example 1: Navigation support system

[1517] The device collects ocean current data in the Pacific Ocean and uploads it to a cloud server, where it is analyzed and provided to the user's e-navigator app as an optimized route for navigation.

[1518] Example 2: Tsunami observation system

[1519] When the device detects abnormal wave height, it reports it to the cloud in real time. The server analyzes this data and generates a tsunami warning, allowing users to immediately begin evacuation.

[1520] Example 3: Fisheries Management System

[1521] The device acquires fish detection data from the coastal waters and uploads it to the cloud, where the server analyzes the data and provides information on optimal fishing spots to the user's smartphone.

[1522] As described above, the system of the present invention can provide users with highly accurate, real-time information by consistently performing the collection, analysis, and provision of marine data.

[1523] The processing flow will be explained below.

[1524] Step 1:

[1525] The device uses an ocean antenna to collect ocean data such as ocean currents, wave height, and wave direction. The device acquires the data via sensors installed in the ocean and stores it in temporary memory. Specifically, the sensors measure the wave height on the ocean surface and transmit the data to the device.

[1526] Step 2:

[1527] This performs initial processing of raw data collected by the device. First, noise is removed through filtering. Then, a conversion process is performed to standardize the data format. For example, data collected at irregular time intervals is standardized to a timestamp with a regular interval.

[1528] Step 3:

[1529] The device uploads the preprocessed data to a cloud server. The device establishes a secure connection using LTE or satellite communications and transmits the data via HTTPS. For example, ocean current data for the South Pacific Ocean is uploaded to the cloud.

[1530] Step 4:

[1531] The server receives the uploaded data, checks the integrity of the data, and checks for any abnormalities. If there are no problems, the server records the data in a database.

[1532] Step 5:

[1533] The server stores the received data in a database. The stored data is used for subsequent analysis. The server also backs up the stored data in parallel to prevent data loss.

[1534] Step 6:

[1535] The server launches the multimodal AI and begins analyzing the data. The server integrates information from multiple sensors, such as sonar and drones, to generate a detailed model of the marine environment. For example, it analyzes flow patterns using multiple ocean current data.

[1536] Step 7:

[1537] The server then stores the analysis results in a database, which includes detailed information on the marine environment and forecast data. This data is then managed so that users can access it in real time.

[1538] Step 8:

[1539] The server receives requests from users, who then use their smartphones or PCs to send requests for data and analysis results for specific ocean areas.

[1540] Step 9:

[1541] The server retrieves the appropriate data from a database based on the request, and returns it immediately to the user.

[1542] Step 10:

[1543] Users can view the provided data on their smartphones or PCs. For example, they can open a smartphone app and check current ocean current information and the optimal navigation route for their vessel. Based on the analysis results, users can navigate safely and conduct efficient fishing activities.

[1544] Example 1

[1545] 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."

[1546] There is a need for a means to quickly and accurately grasp changes in the marine environment, but existing technologies have difficulty in collecting and analyzing data in real time. Furthermore, there is a lack of a consistent system for effectively processing, storing, analyzing, and providing collected data, which means that users cannot obtain the information they need in a timely manner.

[1547] 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.

[1548] In this invention, the server includes a collection device means for collecting oceanographic data in real time, a preprocessing device means for initially processing the collected oceanographic data, a communication device means for uploading the preprocessed data to a centralized processing device on the cloud, a recording device means for saving the uploaded data, a multi-mode compatible artificial intelligence device means for analyzing the saved data, and an information providing device means for providing the analysis results to users, thereby enabling efficient collection, processing, analysis, and provision of real-time observation data of the marine environment.

[1549] The "collection device means" is a device for collecting oceanographic data such as ocean currents, wave heights, and wave directions in real time.

[1550] The "preprocessing device means" is a device for initially processing collected oceanographic data, removing noise, and standardizing the data format.

[1551] The "communication device means" is a device for uploading pre-processed data to a centralized processing device on the cloud.

[1552] "Recording device means" refers to a device for storing uploaded data.

[1553] The "multi-modal artificial intelligence device means" is an artificial intelligence device that can integrate multiple sensor information to analyze stored data.

[1554] The "information providing device means" is a device for providing the analysis results to the user.

[1555] This invention is a system for collecting, processing, and analyzing oceanographic data and providing it to users in real time. An embodiment of this system will be described in detail below.

[1556] System Overview

[1557] The system consists of the following main components:

[1558] 1. Collection Device Means

[1559] 2. Pretreatment Device Means

[1560] 3. Communication Device Means

[1561] 4. Recording Device Means

[1562] 5. Multimodal Artificial Intelligence Device Means

[1563] 6. Information provision device means

[1564] Collection Device Means

[1565] The collection device means is a device for collecting oceanographic data such as ocean currents, wave height, and wave direction in real time. For example, data is acquired using various sensors (ocean current sensors, wave height meters, etc.) installed on the sea surface. This data is first stored in temporary memory within the collection device.

[1566] Pretreatment device means

[1567] The preprocessing means involves processes to remove noise from the collected raw data and convert it into a standard format. The preprocessed data is then standardized into a form suitable for subsequent analysis. Software for noise filtering algorithms and formatting is used.

[1568] communication device means

[1569] The communication device means includes a communication module for uploading pre-processed data to a centralized processing unit on the cloud. Communication is mainly performed via LTE or satellite communication, which enables real-time data transmission even in remote locations.

[1570] Recording device means

[1571] The recording device is a database stored on a cloud server, which stores and manages the data uploaded in real time. The data is recorded in an orderly format and is designed to be immediately accessible when needed.

[1572] Multimodal artificial intelligence device means

[1573] The multi-modal AI system is an AI system that integrates multiple sensor information and performs highly accurate analysis to generate detailed models and predictions of the marine environment. This AI system also integrates data from sonar and drones to provide richer information. For example, deep learning frameworks such as TensorFlow and PyTorch are used.

[1574] Information providing device means

[1575] The information providing device means is an interface for providing analyzed data to users. Users can view the analysis results in real time via a PC or smartphone. Data is provided via a web interface or mobile application.

[1576] Specific examples

[1577] Example 1: Navigation support system

[1578] The device collects ocean current data in the Pacific Ocean and uploads it to a cloud server, where it is analyzed and provided to the user's navigation app as an optimal route.

[1579] Example 2: Tsunami observation system

[1580] When the device detects abnormal wave height, it reports it to the cloud in real time. The server analyzes this data and generates a tsunami warning, allowing users to immediately begin evacuation.

[1581] Example 3: Fisheries Management System

[1582] The device acquires fish detection data from the coastal waters and uploads it to the cloud, where the server analyzes the data and provides information on optimal fishing spots to the user's smartphone.

[1583] Prompt Sentence Examples

[1584] TXT

[1585] We have developed a system that collects, processes, analyzes, and provides real-time oceanographic data to users. This system consists of six main components: collection device means, pre-processing device means, communication device means, recording device means, multi-mode AI device means, and information provision device means. Please explain in detail.

[1586] 1. Oceanographic data collection

[1587] 2. Initial data processing

[1588] 3. Real-time uploading

[1589] 4. Data storage

[1590] 5. Data Analysis

[1591] 6. Data provision

[1592] As described above, the system of the present invention can provide users with highly accurate, real-time information by consistently performing the collection, analysis, and provision of oceanographic data.

[1593] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1594] The flow of this system's program processing

[1595] Step 1: Collecting oceanographic data (terminal)

[1596] Specific description:

[1597] The terminal uses an ocean antenna to collect ocean data in real time. Specifically, data is acquired through various sensors (e.g., ocean current sensors, wave height meters, etc.) and the collected data is stored in temporary memory.

[1598] Input and Output:

[1599] Input: Oceanographic data acquired by sensors (e.g., ocean currents, wave height, wave direction)

[1600] Output: Raw data stored in temporary memory

[1601] Specific behavior:

[1602] The device retrieves the latest ocean current data at a specified point in the South Pacific.

[1603] The sensor measures the seawater temperature and salinity at regular intervals (such as every second) and transmits the data to the terminal.

[1604] Step 2: Initial processing of data (terminal)

[1605] Specific description:

[1606] The terminal removes noise from the collected data and standardizes the format. Using noise filtering algorithms and formatting standardization software, the raw data is converted into a standard format.

[1607] Input and Output:

[1608] Input: raw data collected

[1609] Output: Denoised and uniformly formatted data

[1610] Specific behavior:

[1611] The terminal uses a moving average filter to remove outliers from the collected wave height data.

[1612] Convert seawater temperature data into CSV format.

[1613] Step 3: Real-time upload (terminal)

[1614] Specific description:

[1615] The device uploads the preprocessed data to the cloud server. The data is sent to the cloud server via wireless communication (LTE or satellite communication).

[1616] Input and Output:

[1617] Input: Preprocessed data

[1618] Output: Data uploaded to the cloud server

[1619] Specific behavior:

[1620] The terminal uses an LTE line to send preprocessed wave height data to a cloud server.

[1621] The device uploads new data to the server in bulk every minute.

[1622] Step 4: Storing data (server)

[1623] Specific description:

[1624] The server stores the received data in a database, and the cloud server immediately records and indexes the data received for quick access.

[1625] Input and Output:

[1626] Input: Data uploaded to the cloud server

[1627] Output: Data stored in the database

[1628] Specific behavior:

[1629] The server receives new ocean current data and records it in the SQL database using INSERT statements.

[1630] Add a timestamp when saving data.

[1631] Step 5: Analyzing the data (server)

[1632] Specific description:

[1633] The server uses multi-modal artificial intelligence to integrate and perform detailed analysis of the multiple sensor data, generating detailed models and predictions of the marine environment.

[1634] Input and Output:

[1635] Input: Saved data

[1636] Output: Analyzed data and predictive models

[1637] Specific behavior:

[1638] The server uses TensorFlow and PyTorch to integrate sonar and drone data to generate a highly accurate ocean current model.

[1639] The AI ​​model makes future predictions about the marine environment and stores the results on a server.

[1640] Step 6: Providing Data (Server & User)

[1641] Specific description:

[1642] The server provides the analysis results to the user, who then views the data, which is provided through a web interface or mobile application.

[1643] Input and Output:

[1644] Input: Parsed data

[1645] Output: Data presented in a user-viewable format

[1646] Specific behavior:

[1647] The server provides the analysis results through web applications and mobile applications.

[1648] A user checks recommended navigation route information on their smartphone.

[1649] (Application example 1)

[1650] 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."

[1651] One of the challenges facing modern food delivery systems is a lack of technology to efficiently and quickly deliver fresh seafood. While it is possible to collect oceanographic data and use it to identify optimal fishing grounds, there is no system in place to use that information to quickly deliver fresh seafood to consumers. In particular, there is a need for technology that optimizes the supply chain from fisheries to consumers by calculating efficient delivery routes using real-time oceanographic data and controlling delivery methods such as drones.

[1652] 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.

[1653] In this invention, the server includes marine antenna means for collecting marine data in real time, preprocessing means for initially processing the collected marine data, communication means for uploading the preprocessed data to a server on the cloud, database means for storing the uploaded data, multimodal AI means for analyzing the stored data, data providing means for providing the analysis results to a user, delivery means for efficiently delivering fresh seafood using the data providing means, and route calculation means for controlling the delivery means and calculating an optimal delivery route. This enables efficient and prompt delivery of fresh seafood based on the marine data collected in real time.

[1654] The "marine antenna means" is a device for collecting marine data such as ocean currents, wave heights, and wave directions in real time.

[1655] A "pre-processing means" is a device that includes a process for removing noise from collected raw data and converting it into a standard format.

[1656] The "communication means" is a communication module for uploading preprocessed data to a server on the cloud.

[1657] "Database means" refers to a database placed on a cloud server, and is a system that stores and manages data uploaded in real time.

[1658] "Multimodal AI means" is an artificial intelligence system that integrates information from multiple sensors and performs highly accurate analysis.

[1659] The "data providing means" is an interface for providing analyzed data to the user.

[1660] "Delivery methods" refer to logistics methods for efficiently delivering fresh seafood, including drones and vehicles.

[1661] The "route calculation means" is a system for controlling delivery means and calculating the optimal delivery route.

[1662] This invention is a marine data collection, analysis and provision system for efficiently providing fresh seafood. The system consists of the following main components:

[1663] Hardware Configuration

[1664] 1. Marine Antenna Means

[1665] The various sensors include sensors that measure ocean currents, wave height, wave direction, etc. These sensor data are acquired in real time and stored in temporary memory.

[1666] 2. Terminal

[1667] The devices include smartphones and drones, which collect, pre-process and upload real-time data.

[1668] 3. Cloud Server

[1669] It is equipped with a database means and a multimodal AI means, and has an interface for providing analysis results to users.

[1670] Software Configuration

[1671] 1. Pretreatment methods

[1672] Software that includes processes for denoising raw data and converting it into a standard format.

[1673] 2. Means of communication

[1674] A communication module for uploading pre-processed data to a cloud server using LTE or satellite communications.

[1675] 3. Database Means

[1676] Software for storing and managing data uploaded in real time in a database on a cloud server.

[1677] 4. Multimodal AI Methods

[1678] An AI system that integrates information from multiple sensors and performs highly accurate analysis. For example, it can integrate data from sonar and drones to generate detailed models and predictions of the marine environment.

[1679] 5. Means of providing data

[1680] Software with an interface for providing analysis results to users.

[1681] 6. Delivery method

[1682] Logistics methods such as drones and vehicles to efficiently deliver fresh seafood.

[1683] 7. Route Calculation Method

[1684] A system that controls delivery means and calculates the optimal delivery route.

[1685] Successful examples

[1686] 1. Oceanographic data collection

[1687] Data such as ocean currents, wave height, and wave direction is collected in real time and stored in temporary memory.

[1688] 2. Pretreatment

[1689] The collected data is denoised and converted into a standard format, e.g., wave height data is filtered.

[1690] 3. Upload your data

[1691] The preprocessed data is uploaded to a cloud server via an LTE line or other means.

[1692] 4. Data Analysis

[1693] Multimodal AI is used to integrate data and generate highly accurate ocean models, for example, using sonar and drone data to generate detailed ocean current models.

[1694] 5. Data provision

[1695] The analysis results are provided to users, who can view real-time data via their smartphones and determine the optimal route for delivering fresh seafood.

[1696] Food delivery as a concrete example

[1697] 1. Optimal fishing ground identification system

[1698] Ocean data is collected and information on optimal fishing spots is uploaded to a cloud server. This information is analyzed and provided to users as optimal fishing spots.

[1699] 2. Real-time fresh seafood delivery system

[1700] Based on oceanographic data collected by drones, fresh seafood is delivered via the optimal route, and users can check the delivery status in real time via their smartphones.

[1701] Prompt Sentence Examples

[1702] Fisheries management system using ocean data. Please implement a function to collect and analyze fishing ground information in real time and provide users with the best fishing ground information.

[1703] As described above, the present invention is a system that can efficiently deliver fresh seafood to users by consistently performing everything from collecting marine data to analyzing it, providing the data, and delivering it along the optimal route.

[1704] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1705] Step 1:

[1706] The terminal collects oceanographic data

[1707] Input: Various sensor data (ocean currents, wave height, wave direction, etc.)

[1708] Specific operation: Acquire ocean data in real time using ocean antenna means. For example, the terminal collects data from a wave direction sensor and stores it in temporary memory.

[1709] Output: Raw data

[1710] Step 2:

[1711] The device preprocesses the data

[1712] Input: Raw data

[1713] Specific operation: Performs noise removal and standard format conversion. For example, it filters the wave height data acquired by the device and converts it into a unified format.

[1714] Output: Preprocessed, consistent data

[1715] Step 3:

[1716] The device uploads the preprocessed data to the cloud server.

[1717] Input: Preprocessed data

[1718] Specific operation: Data is transmitted to a cloud server using LTE or satellite communication. For example, the terminal uploads preprocessed ocean current data to the cloud server.

[1719] Output: Data stored on the cloud server

[1720] Step 4:

[1721] The server stores the data

[1722] Input: Data uploaded to the cloud server

[1723] Specific operation: Data is stored and managed using database means. For example, a cloud server records ocean current data in an SQL database.

[1724] Output: Real-time data stored in a database

[1725] Step 5:

[1726] The server analyzes the data

[1727] Input: Saved data

[1728] How it works: Multimodal AI methods are used to analyze data and generate predictive models. For example, a server integrates data from sonar and drones to generate a highly accurate ocean current model.

[1729] Output: Analysis results and predicted data

[1730] Step 6:

[1731] The server provides the analysis results to the user

[1732] Input: Model and predicted data as analysis results

[1733] Specific operation: The analysis results are provided to the user using the data provision means. For example, the user can view optimal fishing ground information in real time via their smartphone.

[1734] Output: Analysis result data provided to the user

[1735] Step 7:

[1736] The server controls the delivery method

[1737] Input: Parsed oceanographic data and shipping routes

[1738] Specific operation: The route calculation means is used to analyze data and calculate the optimal delivery route. For example, the server calculates and instructs a drone's delivery route to efficiently deliver fresh seafood.

[1739] Output: Optimal delivery route and control instructions

[1740] Step 8:

[1741] Delivery method provides fresh seafood

[1742] Input: Optimal delivery route and control instructions

[1743] Specific operation: Drones and vehicles efficiently deliver fresh seafood. For example, drones deliver fresh seafood to consumers by following optimal routes.

[1744] Output: Fresh seafood delivered to the consumer

[1745] 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.

[1746] The present invention is a system that collects, processes, and analyzes oceanographic data and provides it to users in real time, while also recognizing the user's emotions and dynamically adjusting the content of the data provided. An embodiment of this system will be described in detail below.

[1747] System Overview

[1748] The system consists of the following main components:

[1749] 1. Marine Antenna Means

[1750] 2. Pretreatment Methods

[1751] 3. Means of communication

[1752] 4. Database Means

[1753] 5. Multimodal AI Methods

[1754] 6. Means of providing data

[1755] 7. Emotion Engine

[1756] Marine Antenna Means

[1757] The marine antenna means is a device for collecting marine data such as ocean currents, wave height, and wave direction in real time. For example, data is acquired using various sensors installed on the sea surface. This data is first stored in temporary memory within the antenna.

[1758] Pretreatment means

[1759] Pre-processing involves processes to remove noise from the collected raw data and convert it into a standard format, which is suitable for subsequent analysis and interpretation.

[1760] communication means

[1761] The communication means includes a communication module for uploading pre-processed data to a cloud server. Communication is mainly via LTE or satellite communications, which enables real-time data transmission even in remote locations.

[1762] Database Means

[1763] The database means is a database placed on a cloud server, which stores and manages data uploaded in real time. The data is recorded in an orderly format and is designed to be immediately accessible when needed.

[1764] Multimodal AI methods

[1765] Multimodal AI tools are artificial intelligence systems that integrate multiple sensor information and perform highly accurate analysis to generate detailed models and predictions of the marine environment. These AI systems also integrate data from sonar and drones to provide richer information.

[1766] Data provision method

[1767] The data provision means is an interface for providing analyzed data to users, who can view the analysis results in real time via their PCs or smartphones.

[1768] Emotion Engine

[1769] The emotion engine is a system that recognizes the user's emotions, analyzes the data, and adjusts the display and content of the oceanographic data provided. For example, if the user is stressed, the data will be displayed more simply and easily understandable.

[1770] A natural language description of what the program does

[1771] The processing flow of the system program and its specific operations will be explained below.

[1772] 1. Ocean data collection (terminal)

[1773] The terminal acquires oceanographic data in real time through an ocean antenna. For example, the terminal reads ocean current data in the South Pacific Ocean from a sensor.

[1774] 2. Initial processing of data (terminal)

[1775] The device removes noise from the data it acquires and standardizes the format. For example, the device filters wave height data and converts it into a standard format.

[1776] 3. Real-time upload (terminal)

[1777] The device uploads the data to the cloud server. For example, the device transmits the pre-processed data to the server using an LTE connection.

[1778] 4. Data storage (server)

[1779] The server receives the uploaded data, checks the integrity of the data, and checks for any abnormalities. If there are no problems, the server records the data in a database.

[1780] 5. Data analysis (server)

[1781] The server uses multimodal AI to integrate information from multiple sensors and generate a detailed model of the marine environment, for example by analyzing flow patterns using multiple ocean current data.

[1782] 6. User Emotion Recognition (Server)

[1783] The server runs an emotion engine to collect and analyze the user's emotion data, for example, to determine whether the user is feeling stressed.

[1784] 7. Coordination of data provision (server)

[1785] The server adjusts how it presents data based on the user's emotions, for example, if the user is feeling stressed, it will present a concise summary of the data.

[1786] 8. Data Provision (Server & User)

[1787] The server provides the analysis results to the user, who then views the data on their smartphone or PC. For example, the user can check real-time oceanographic data and analysis results on their smartphone.

[1788] Specific examples

[1789] Example 1: Navigation support system

[1790] The device collects ocean current data in the Pacific Ocean and uploads it to a cloud server. This data is then analyzed and provided to the user's e-navigator app as an optimized route. Furthermore, an emotion engine recognizes the user's stress level and adjusts the displayed data accordingly.

[1791] Example 2: Tsunami observation system

[1792] When the device detects abnormal wave height, it reports it to the cloud in real time. The server analyzes this data and generates a tsunami warning. The emotion engine detects the user's emotions and displays evacuation instructions more intuitively, allowing the user to immediately begin evacuation.

[1793] Example 3: Fisheries Management System

[1794] The device collects fish detection data from the coastal waters and uploads it to the cloud. The server analyzes the data and provides optimal fishing spot information to the user's smartphone. The emotion engine analyzes the user's emotions and adjusts the priority and presentation of the information.

[1795] As described above, the system of the present invention provides optimal information to users by consistently performing all steps from collecting marine data to analyzing and providing it, as well as recognizing and adjusting user emotions.

[1796] The processing flow will be explained below.

[1797] Step 1:

[1798] The device uses an ocean antenna to collect ocean data such as ocean currents, wave height, and wave direction. The device acquires the data via sensors installed in the ocean and stores it in temporary memory. Specifically, the sensors measure the wave height on the ocean surface and transmit the data to the device.

[1799] Step 2:

[1800] This performs initial processing of raw data collected by the device. First, noise is removed through filtering. Then, a conversion process is performed to standardize the data format. For example, data collected at irregular time intervals is standardized to a timestamp with a regular interval.

[1801] Step 3:

[1802] The device uploads the preprocessed data to a cloud server. The device establishes a secure connection using LTE or satellite communications and transmits the data via HTTPS. For example, ocean current data for the South Pacific Ocean is uploaded to the cloud.

[1803] Step 4:

[1804] The server receives the uploaded data, checks the integrity of the data, and checks for any abnormalities. If there are no problems, the server records the data in a database.

[1805] Step 5:

[1806] The server stores the received data in a database. The stored data is used for subsequent analysis. The server also backs up the stored data in parallel to prevent data loss.

[1807] Step 6:

[1808] The server launches the multimodal AI and begins analyzing the data. The server integrates information from multiple sensors, such as sonar and drones, to generate a detailed model of the marine environment. For example, it analyzes flow patterns using multiple ocean current data.

[1809] Step 7:

[1810] The server then stores the analysis results in a database, which includes detailed information on the marine environment and forecast data. This data is then managed so that users can access it in real time.

[1811] Step 8:

[1812] The server starts the emotion engine, collects and analyzes the user's emotion data, and evaluates the user's state based on the emotion data obtained from the user's smartphone or PC. For example, it determines whether the user is feeling stressed.

[1813] Step 9:

[1814] The server dynamically adjusts the way data is presented based on the user's emotions. For example, if the user is feeling stressed, the server will avoid displaying complex data and provide simple, visually easy-to-understand information.

[1815] Step 10:

[1816] The server receives requests from users. Users use their smartphones or PCs to send requests for data and analysis results for specific ocean areas. For example, a user requests detailed data on a particular fishing ground.

[1817] Step 11:

[1818] The server retrieves the appropriate data from the database based on the request, and immediately returns the retrieved data to the user. For example, it retrieves the latest ocean current data for the South Pacific and provides it to the user.

[1819] Step 12:

[1820] Users can view the provided data in real time on their smartphones or PCs. For example, a user can open a smartphone app and check data for optimizing navigation routes or managing fisheries. The app takes into account the user's emotional state and displays the necessary information in an appropriate format.

[1821] Example 2

[1822] 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."

[1823] Conventional oceanographic data collection systems have the problem that the analysis and provision of acquired data is one-way, and they are unable to respond flexibly to the user's emotions or circumstances. Furthermore, when collecting and analyzing data in real time, managing huge amounts of data and ensuring the speed of analysis becomes an issue. This makes it difficult to provide users with the information they need, and there has been a demand for improved usability.

[1824] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1825] In this invention, the server includes marine antenna means for collecting marine data in real time, preprocessing means for initially processing the collected marine data, communication means for uploading the preprocessed data to a server on the cloud, database means for saving the uploaded data, multimodal AI means for analyzing the saved data, data providing means for providing the analysis results to the user, and emotion engine means for analyzing the user's emotions and dynamically adjusting the content to be provided. This makes it possible to effectively collect and analyze data in real time and to appropriately provide necessary information based on the user's emotions.

[1826] The "marine antenna means" is a device for collecting marine data such as ocean currents, wave heights, and wave directions in real time.

[1827] A "pre-processing means" is a device that includes a process for removing noise from collected raw data and converting it into a standard format.

[1828] A "communication means" is a device that includes a communication module for uploading preprocessed data to a server on the cloud.

[1829] "Database means" refers to a database placed on a cloud server, and is a device for storing and managing data uploaded in real time.

[1830] A "multimodal AI means" is a device that includes an artificial intelligence system for integrating information from multiple sensors and performing highly accurate analysis.

[1831] A "data providing means" is a device that includes an interface for providing analyzed data to a user.

[1832] The "emotion engine means" is a device that includes a system that recognizes the user's emotions, analyzes the data, and dynamically adjusts the display method and content of the oceanographic data provided.

[1833] The system of this invention collects, processes, and analyzes oceanographic data, and provides it to users in real time, while also recognizing the user's emotions and dynamically adjusting the content provided. The system consists of the following main components:

[1834] Components

[1835] 1. Marine Antenna Means

[1836] The marine antenna means is a device for collecting marine data such as ocean currents, wave height, and wave direction in real time. For example, it uses various sensors installed on the sea surface. This data is first stored in a temporary memory within the antenna.

[1837] 2. Pretreatment Methods

[1838] Pre-processing involves processes to remove noise from the collected raw data and convert it into a standard format, which is suitable for subsequent analysis and interpretation.

[1839] 3. Means of communication

[1840] The communication means includes a communication module for uploading pre-processed data to a cloud server. Communication is mainly via LTE or satellite communications, which enables real-time data transmission even in remote locations.

[1841] 4. Database Means

[1842] The database means is a database placed on a cloud server, which stores and manages data uploaded in real time. The data is recorded in an orderly format and is designed to be immediately accessible when needed.

[1843] 5. Multimodal AI Methods

[1844] Multimodal AI methods are artificial intelligence systems that integrate multiple sensor information and perform highly accurate analysis to generate detailed models and predictions of the marine environment. These AI systems are built using TensorFlow and PyTorch, for example. They also integrate data from sonar and drones to provide richer information.

[1845] 6. Means of providing data

[1846] The data provision means is an interface for providing analyzed data to users, who can view the analysis results in real time via their PCs or smartphones.

[1847] 7. Emotional Engine Means

[1848] The emotion engine is a system that recognizes the user's emotions, analyzes the data, and dynamically adjusts the display method and content of the oceanographic data provided. For example, if the user is under stress, the data will be displayed in a more concise and easy-to-understand manner. The emotion recognition algorithm uses IBM Watson's emotion analysis.

[1849] Specific examples

[1850] Example 1: Navigation support system

[1851] The device collects ocean current data in the Pacific Ocean and uploads it to a cloud server. This data is analyzed by the server and provided to the user's navigation support app as an optimal route. In addition, an emotion engine recognizes the user's stress level and briefly adjusts the data presented.

[1852] Example prompt: "Based on ocean current data collected in the Pacific Ocean, provide the best sailing route. Also, assess the user's stress level and present the data in a concise format."

[1853] Example 2: Tsunami observation system

[1854] When the device detects abnormal wave height, it reports it to the cloud in real time. The server analyzes this data and generates a tsunami warning. The emotion engine analyzes the user's emotions and displays evacuation instructions more intuitively.

[1855] Example prompt: "If abnormal wave heights are detected, generate a real-time tsunami warning and provide evacuation instructions to the user in an intuitive manner."

[1856] Example 3: Fisheries Management System

[1857] The device collects fish detection data from the coastal waters and uploads it to the cloud. The server analyzes the data and provides optimal fishing spot information to the user's smartphone. The emotion engine analyzes the user's emotions and adjusts the priority and presentation of the information.

[1858] Example prompt: "Based on inshore fish detection data, provide information on optimal fishing spots. Also, assess user sentiment and adjust how the information is presented."

[1859] As described above, the system of the present invention provides optimal information to users by consistently performing all steps from collecting marine data to analyzing and providing it, as well as recognizing and adjusting user emotions.

[1860] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1861] Step 1:

[1862] Ocean data collection (terminal)

[1863] The terminal acquires oceanographic data in real time through an ocean antenna. Specifically, the terminal acquires data such as ocean currents, wave height, and wave direction from sensors installed in the Pacific Ocean at one-second intervals and records it in temporary memory. The input at this time is raw data from each sensor, and the output is raw oceanographic data stored in temporary memory.

[1864] Step 2:

[1865] Initial data processing (terminal)

[1866] The terminal removes noise from the acquired data and standardizes the format. In operation, the terminal uses a filtering algorithm to remove noise from the raw data and then converts the data into a standard format (e.g., CSV format). The input is the raw oceanographic data saved in step 1, and the output is the data that has been removed and converted into a standard format.

[1867] Step 3:

[1868] Real-time upload (terminal)

[1869] The device uploads the preprocessed data to a cloud server. Specifically, the device uses an LTE or satellite communication module to transmit the filtered data to the cloud server in packets every minute. The input is the preprocessed data, and the output is the data transmitted to the cloud server.

[1870] Step 4:

[1871] Data storage (server)

[1872] The server receives the data uploaded to the cloud, verifies it, and then records it in a database. The server then checks the integrity of the data it receives to ensure there are no outliers or missing data. It then stores the data in a database (e.g., Amazon RDS). The input at this time is the data uploaded to the cloud server, and the output is the data stored in the database, the integrity of which has been confirmed.

[1873] Step 5:

[1874] Data analysis (server)

[1875] The server uses multimodal AI to integrate multiple sensor information and generate a detailed marine environment model. Specifically, the server integrates collected ocean current and wave data and runs an AI model using TensorFlow and PyTorch, thereby generating ocean current patterns and predictions. The input is sensor data whose consistency has been confirmed, and the output is an analyzed marine environment model.

[1876] Step 6:

[1877] User emotion recognition (server)

[1878] The server launches an emotion engine to collect and analyze the user's emotional data. In operation, the server uses an emotion recognition algorithm (for example, IBM Watson's emotion analysis) to analyze the user's voice input and text messages and determine their stress level and relaxation level. The input is the user's emotional data, and the output is analyzed emotional information.

[1879] Step 7:

[1880] Coordination of data provision (server)

[1881] The server adjusts how it presents data based on the user's emotions. Specifically, it adjusts the amount and format of data presented based on the results obtained from the emotion engine. If the user is feeling stressed, it uses simple, visually easy-to-understand graphs and infographics. The inputs are the analyzed emotion information and the marine environment model, and the output is the adjusted display format of the data.

[1882] Step 8:

[1883] Data provision (server & user)

[1884] The server provides the analysis results to the user, who then views the data on their smartphone or PC. Specifically, the server sends the analysis results to the user's application via a Web API, and the user can view real-time oceanographic data and analysis results through a smartphone app or PC web browser. The input is data in an adjusted display format, and the output is the analysis results displayed on the user's device.

[1885] (Application example 2)

[1886] 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."

[1887] Conventional seafood delivery systems have difficulty effectively utilizing oceanographic data, making it impossible to grasp the status of fishing grounds and environmental changes in real time. This makes it difficult to efficiently supply fresh seafood and to optimize deliveries. Furthermore, the system is unable to adjust information provision based on the user's emotions, often resulting in a poor user experience. These issues need to be resolved.

[1888] 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.

[1889] In this invention, the server includes a marine antenna means for collecting marine data in real time, a preprocessing means for initially processing the collected marine data, a communication means for uploading the preprocessed data to a server on the cloud, a database means for saving the uploaded data, a multimodal AI means for analyzing the saved data, a data provision means for providing the analysis results to the user, an emotion engine that recognizes the user's emotions and dynamically adjusts the data provision method, and a route optimization means for using the analyzed marine data to support food delivery via the optimal route. This enables the server to analyze the marine data and provide information on optimal fishing grounds, as well as provide information based on the user's emotions, thereby realizing efficient delivery of fresh seafood that satisfies the user.

[1890] The "marine antenna means" is a device for collecting marine data such as ocean currents, wave heights, and wave directions in real time.

[1891] A "pre-processing means" is a device that includes a process for removing noise from collected raw data and converting it into a standard format.

[1892] A "communication means" is a device that includes a communication module for uploading preprocessed data to a server on the cloud.

[1893] "Database means" refers to a database placed on a cloud server, and is a device that serves to store and manage data uploaded in real time.

[1894] "Multimodal AI means" is an artificial intelligence system that integrates information from multiple sensors and performs highly accurate analysis.

[1895] The "data providing means" is an interface for providing analyzed data to the user.

[1896] An "emotion engine" is a system that recognizes a user's emotions, analyzes that data, and dynamically adjusts the display method and content of the information provided.

[1897] The "route optimization tool" is a device that has the function of supporting the procurement and delivery of fresh seafood along the optimal route based on analyzed oceanographic data.

[1898] The system for realizing the present invention comprises the following main components:

[1899] Oceanographic data collection

[1900] The server collects oceanographic data in real time from various sensors installed on the ocean surface, such as data on ocean currents, wave height, wave direction, etc. This data is first collected by an ocean antenna means and stored in temporary memory.

[1901] Initial processing of data

[1902] The server uses pre-processing means to remove noise from the collected data and convert it into a standard format, specifically filtering the wave height data and standardizing it into a format suitable for analysis.

[1903] Uploading data to the cloud

[1904] The preprocessed data is uploaded to a cloud server using a communication method, mainly via LTE or satellite communication, which enables real-time data transmission even from remote locations.

[1905] Data storage

[1906] The cloud server stores the uploaded data using a database system, which is designed to record the data in an organized format and make it instantly accessible when needed.

[1907] Data analysis

[1908] The server analyzes the stored data using multimodal AI methods, integrating information from multiple sensors to generate highly accurate marine environment models and predictions, enabling the provision of richer information.

[1909] User Emotion Recognition

[1910] The server uses an emotion engine to collect and analyze the user's emotional data, for example to determine whether the user is feeling stressed, and dynamically adjusts the display and content of the information provided to the user based on this data.

[1911] Customization of information provided

[1912] When a user views the analysis results on a PC or smartphone, the server takes into account the user's emotions and adjusts the display to display information concisely and intuitively, improving the user experience.

[1913] Fresh seafood delivery assistance

[1914] Based on the analysis results, route optimization methods are used to assist food delivery, providing information on the best fishing spots and enabling the fastest delivery of fresh seafood via the most optimal route.

[1915] Specific examples

[1916] As a concrete example, consider a fresh seafood delivery support system. The user's smartphone app displays information on optimal fishing grounds based on oceanographic data collected in real time. Based on this information, delivery personnel can purchase and deliver seafood along the optimal route. In addition, an emotion engine analyzes the user's emotions and adjusts the way information is displayed, allowing the user to obtain information without feeling stressed.

[1917] Prompt Sentence Examples

[1918] Here is an example of a prompt for generating code using a generative AI model:

[1919] I am implementing a seafood delivery support app. Please generate code for the following functions:

[1920] 1. Ability to collect and process oceanographic data in real time

[1921] 2. Ability to upload data to the cloud

[1922] 3. Function to analyze data and provide optimal fishing ground information

[1923] 4. Ability to customize information display based on user emotional data

[1924] 5. A feature that provides optimal routes for delivering fresh seafood.

[1925] Please provide the specific code and explanation for each function.

[1926] In this way, the system of the present invention consistently collects, analyzes, and provides marine data, as well as recognizes and adjusts user emotions, thereby providing optimal information to users and enabling the efficient and effective delivery of fresh seafood.

[1927] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1928] Step 1:

[1929] The terminal collects oceanographic data in real time through an ocean antenna means. This oceanographic data includes ocean currents, wave height, wave direction, etc. The input is data from sensors installed on the sea surface, and the output is raw oceanographic data stored in temporary memory. Specifically, the terminal reads data from the sensors and stores it in real time.

[1930] Step 2:

[1931] The terminal uses pre-processing means to remove noise from the collected oceanographic data and convert it into a standard format. The input is raw oceanographic data stored in temporary memory, and the output is data that has been filtered and converted into a standard format. Specifically, the terminal removes noise from the data and converts wave height and ocean current data into a format that is easy to analyze.

[1932] Step 3:

[1933] The device uploads the preprocessed data to a server on the cloud using a communication means. The input is the preprocessed data, and the output is the data sent to the cloud server. Specifically, the device transfers the data to the server in real time using an LTE line or satellite communication.

[1934] Step 4:

[1935] The server uses a database on the cloud to store and manage the uploaded data. The input is the data sent to the cloud server, and the output is the organized data recorded in the database. Specifically, the server checks the integrity of the data and checks for any abnormal values ​​before saving it to the database.

[1936] Step 5:

[1937] The server uses multimodal AI methods to analyze the stored data. The input is oceanographic data stored in the database, and the output is an analyzed ocean environmental model and prediction information. Specifically, the server integrates information from multiple sensors and performs analysis to generate a highly accurate ocean environmental model.

[1938] Step 6:

[1939] The server uses an emotion engine to collect and analyze the user's emotional data. The input is data indicating the user's emotional state, and the output is the analyzed user's emotional information. Specifically, the server uses an emotion recognition algorithm to determine whether the user is feeling stressed.

[1940] Step 7:

[1941] The server adjusts the data presentation method based on the user's emotions. The input is the analyzed marine environment model, forecast information, and the user's emotional information, and the output is the adjusted data presentation method. Specifically, the server dynamically adjusts the user interface to present information concisely and intuitively.

[1942] Step 8:

[1943] The server uses a route optimization method to support the procurement and delivery of fresh seafood along the optimal route. The input is the analyzed oceanographic data and the user's delivery request, and the output is the optimal delivery route. Specifically, the server provides the delivery person with the optimized route, taking into account the shortest route and traffic information.

[1944] By processing and calculating input data at each step and generating the required output, the system embodying this invention can operate efficiently.

[1945] 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.

[1946] 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.

[1947] 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.

[1948] 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.

[1949] FIG. 9 illustrates 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 behaviors 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.

[1950] 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.

[1951] 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).

[1952] 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.

[1953] 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."

[1954] 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.

[1955] 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).

[1956] 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.

[1957] 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.

[1958] 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.

[1959] 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.

[1960] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1961] 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.

[1962] 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.

[1963] 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.

[1964] 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.

[1965] 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.

[1966] The following is further disclosed regarding the above embodiment.

[1967] (Claim 1)

[1968] an ocean antenna means for collecting oceanographic data in real time;

[1969] a pre-processing means for initially processing the collected oceanographic data;

[1970] A communication means for uploading the preprocessed data to a server on the cloud;

[1971] database means for storing the uploaded data;

[1972] a multimodal AI means for analyzing the stored data;

[1973] data providing means for providing the analysis results to a user;

[1974] A system including:

[1975] (Claim 2)

[1976] 10. The system of claim 1, wherein sensors are used to acquire oceanographic data such as ocean currents, wave height, and wave direction.

[1977] (Claim 3)

[1978] 10. The system of claim 1, wherein the system removes noise from the acquired oceanographic data and standardizes the data format.

[1979] "Example 1"

[1980] (Claim 1)

[1981] collector means for collecting oceanographic data in real time;

[1982] preprocessing means for initially processing the collected oceanographic data;

[1983] a communication means for uploading the pre-processed data to a centralized processing unit on the cloud;

[1984] recorder means for storing the uploaded data;

[1985] multimodal artificial intelligence device means for analyzing the stored data;

[1986] an information providing device means for providing the analysis result to a user;

[1987] A system including:

[1988] (Claim 2)

[1989] 10. The system of claim 1, wherein sensors are used to acquire oceanographic data such as ocean currents, wave height, and wave direction.

[1990] (Claim 3)

[1991] 10. The system of claim 1, wherein the system removes noise from the acquired oceanographic data and standardizes the data format.

[1992] "Application Example 1"

[1993] (Claim 1)

[1994] an ocean antenna means for collecting oceanographic data in real time;

[1995] a pre-processing means for initially processing the collected oceanographic data;

[1996] A communication means for uploading the preprocessed data to a server on the cloud;

[1997] database means for storing the uploaded data;

[1998] a multimodal AI means for analyzing the stored data;

[1999] data providing means for providing the analysis results to a user;

[2000] a delivery means for efficiently providing fresh seafood using the data provision means;

[2001] a route calculation means for controlling delivery means and calculating an optimal delivery route;

[2002] A system including:

[2003] (Claim 2)

[2004] 10. The system of claim 1, wherein sensors are used to acquire oceanographic data such as ocean currents, wave height, and wave direction.

[2005] (Claim 3)

[2006] 10. The system of claim 1, wherein the system removes noise from the acquired oceanographic data and standardizes the data format.

[2007] "Example 2: Combining Emotion Engines"

[2008] (Claim 1)

[2009] an ocean antenna means for collecting oceanographic data in real time;

[2010] a pre-processing means for initially processing the collected oceanographic data;

[2011] A communication means for uploading the preprocessed data to a server on the cloud;

[2012] database means for storing the uploaded data;

[2013] a multimodal AI means for analyzing the stored data;

[2014] data providing means for providing the analysis results to a user;

[2015] emotion engine means for analyzing user emotions and dynamically adjusting the content provided;

[2016] A system including:

[2017] (Claim 2)

[2018] 10. The system of claim 1, wherein sensors are used to acquire oceanographic data such as ocean currents, wave height, and wave direction.

[2019] (Claim 3)

[2020] 10. The system of claim 1, wherein the system removes noise from the acquired oceanographic data and standardizes the data format.

[2021] "Application example 2 when combining emotion engines"

[2022] (Claim 1)

[2023] an ocean antenna means for collecting oceanographic data in real time;

[2024] a pre-processing means for initially processing the collected oceanographic data;

[2025] A communication means for uploading the preprocessed data to a server on the cloud;

[2026] database means for storing the uploaded data;

[2027] a multimodal AI means for analyzing the stored data;

[2028] data providing means for providing the analysis results to a user;

[2029] an emotion engine that recognizes user emotions and dynamically adjusts how data is provided;

[2030] Route optimization means to support food delivery via optimal routes based on the analyzed oceanographic data;

[2031] A system including:

[2032] (Claim 2)

[2033] 10. The system of claim 1, wherein sensors are used to acquire oceanographic data such as ocean currents, wave height, and wave direction.

[2034] (Claim 3)

[2035] 10. The system of claim 1, wherein the system removes noise from the acquired oceanographic data and standardizes the data format. [Explanation of symbols]

[2036] 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. an ocean antenna means for collecting oceanographic data in real time; a pre-processing means for initially processing the collected oceanographic data; A communication means for uploading the preprocessed data to a server on the cloud; database means for storing the uploaded data; a multimodal AI means for analyzing the stored data; data providing means for providing the analysis results to a user; A system including:

2. The system of claim 1 , wherein sensors are used to acquire oceanographic data such as ocean currents, wave heights, and wave directions.

3. The system according to claim 1, wherein noise is removed from the acquired oceanographic data and the data format is unified.

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