system

Abstract

We provide the system. [Solution] A sensor device for acquiring oceanographic information in real time, A communication method for transmitting acquired marine data to cloud storage, An analysis method using artificial intelligence to perform data analysis on the cloud, An output means that provides the processed analysis results to the user's terminal, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional methods for collecting ocean information used visual observation or satellites, but these have low resolution and lack real-time performance, making it difficult to ensure data accuracy and timely utilization. Also, the commercialization of ocean data and prompt response for anomaly detection are issues. As a result, effective utilization in navigation safety, fisheries, resource development, etc. has not been achieved.

Means for Solving the Problems

[0005] This invention provides a system that combines ocean radar, which enables real-time, high-precision acquisition of ocean information, with AI to perform data analysis and anomaly detection on the cloud. Specifically, ocean data acquired by sensor devices is processed using beamforming technology and transmitted to cloud storage. Furthermore, advanced data analysis is performed using AI on the cloud, and the analysis results are provided to the user's terminal, thereby contributing to the development of various ocean-related businesses.

[0006] A "sensor device" is a device used to acquire information from the marine environment, such as wave height, ocean current speed, and wind speed.

[0007] "Communication means" refers to the technology or device used to transmit acquired data to cloud storage or other data processing systems.

[0008] Artificial intelligence is a computer technology that analyzes large amounts of data and automatically detects patterns and anomalies.

[0009] "Analysis means" refers to a process or system for analyzing oceanographic information based on acquired data and generating anomaly detection and prediction models.

[0010] "Output means" refers to a technology or device for providing the analyzed results to the user's terminal or other system.

[0011] "Beamforming technology" is a signal processing technique that enhances signals obtained from a specific direction and suppresses noise.

[0012] An "anomaly detection method" is a process or system for identifying data points that deviate from normal patterns and detecting anomalies in real time. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. ]> [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. " [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, a numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a system for collecting and analyzing marine information in real time, and its embodiments are described in detail below.

[0035] First, sensor devices installed in the ocean continuously acquire data such as wave height, ocean current speed, and wind speed. These sensor devices can collect highly accurate data using beamforming technology.

[0036] Next, the server receives the data transmitted from the sensor device. The received data is converted to an appropriate format and securely sent to cloud storage. Secure communication protocols are used to maintain the confidentiality and integrity of the data.

[0037] The cloud-based server performs advanced AI-powered analysis on the received data. In this analysis process, the AI ​​evaluates the dataset and identifies anomalies and patterns. If necessary, the server immediately issues an alert if an anomaly is detected.

[0038] The analysis results are further processed and sent to the user's device. Users can review the provided data through an application or interface and make decisions based on the information obtained. For example, those involved in the fishing industry can understand the real-time changing ocean conditions and use this information to select fishing grounds and optimize navigation routes.

[0039] This format allows various maritime-related business sectors to utilize highly accurate data in real time and respond quickly. Through integration with other systems, the aim is to promote the commercial use of data and contribute to enhancing Japan's position as a maritime nation.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server receives raw data from the sensor device. The sensor device continuously collects oceanographic information and transmits it to the server. The server then formats the signal in preparation for further processing.

[0043] Step 2:

[0044] The server applies beamforming technology to enhance signals in specific directions and suppress noise. This process improves data accuracy and enhances the quality of analysis.

[0045] Step 3:

[0046] The server converts the processed data into a standard format such as JSON and uploads it to cloud storage. During this process, a secure communication protocol is used to ensure data security.

[0047] Step 4:

[0048] A server in the cloud uses AI to analyze the data. The AI ​​analyzes the data and applies anomaly detection models to identify data points that deviate from normal patterns.

[0049] Step 5:

[0050] The server compiles the analysis results and immediately generates an alert if an anomaly is detected. This alert is sent to the relevant users to prompt a quick response.

[0051] Step 6:

[0052] The analyzed data is sent to the user's device. Through the application, the user can view this data and monitor changes in the marine environment in real time.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] Modern ocean observation systems require real-time data acquisition and analysis, but conventional systems often suffer from insufficient data accuracy and analysis speed, resulting in delayed anomaly detection. Furthermore, data transmission and storage may not guarantee confidentiality or integrity. These issues hinder efficient decision-making in ocean-related industries.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] In this invention, the server includes a measuring device for acquiring marine information in real time, a data transmission means for transmitting the acquired data to a remote storage device, and an analysis means using a machine learning model for performing information analysis on the remote storage device. This improves the accuracy of data collection and the speed of analysis, enabling rapid and accurate anomaly detection, and thus facilitating efficient decision-making in marine-related industrial fields.

[0058] "Oceanographic information" refers to data that indicates the state of the ocean, including information such as wave height, ocean current speed, and wind speed.

[0059] A "measuring device" is a sensor device installed to acquire physical information under specific environmental conditions, enabling high-precision data collection.

[0060] A "remote storage device" is a system for storing and managing data located on the internet, such as cloud storage.

[0061] "Data transmission means" refers to communication technology used to transmit acquired information to another system or storage.

[0062] A "machine learning model" is an algorithm or set of algorithms that allows a computer to recognize patterns based on experience and automatically learn to improve.

[0063] "Analysis means" refers to methods and techniques for analyzing collected data and extracting useful information.

[0064] "Spatial filtering technology" refers to methods for extracting specific signals or data with high precision, and mainly includes technologies such as beamforming.

[0065] An "abnormal information provision means" is a system or method for identifying patterns or conditions that are different from the normal ones from analyzed data and notifying the user.

[0066] This invention outlines an embodiment of a system for acquiring and analyzing marine information in real time and providing it to users. This system consists of a measuring device, a server, and a terminal.

[0067] First, the measuring device is installed in the ocean to continuously acquire oceanographic information such as wave height, ocean current speed, and wind speed. This sensor can collect data with high precision using spatial filtering technology, enabling accurate measurements.

[0068] Next, the server receives the data transmitted from the measuring device. The received data is stored in a remote storage device via a data transmission means. A secure communication protocol is used, and it is designed to maintain the confidentiality and integrity of the data. This remote storage device consists of cloud storage, which can securely store and manage large amounts of data.

[0069] The server performs analysis on data stored in the remote storage device using machine learning models. This analysis allows for evaluation of the dataset and identification of anomalies and specific patterns. Based on the analysis results, an anomaly information provision system generates timely information that users need. This analysis process is highly automated and processed quickly and efficiently.

[0070] Ultimately, the user receives the analysis results sent from the server on their device. The data is displayed through the device's application or interface, allowing the user to check the information in real time. For example, those involved in the fishing industry can use the analyzed oceanographic information to select fishing grounds and optimize navigation routes.

[0071] A concrete example of a prompt message would be something like, "Please explain the methods for analyzing data from marine sensors to detect anomalies." This allows users to efficiently obtain information and be supported in making appropriate decisions.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The server receives oceanographic information transmitted from the measuring devices. At this stage, various data such as wave height and wind speed are input. Since the received data is in its raw state, it needs to be formatted. Specifically, the server converts the data into a standardized format and temporarily stores it in memory. To maintain data consistency, data validation checks are also performed simultaneously.

[0075] Step 2:

[0076] The server transfers the received data to a remote storage device. Standardized data is used as input. This transfer process utilizes a secure communication protocol (e.g., TLS) to ensure data confidentiality and integrity. The data is stored in cloud storage and accessible in a database for extended periods. Specifically, the server checks for communication errors and retransmits the data as needed.

[0077] Step 3:

[0078] The server analyzes data stored in the cloud using machine learning models. Data from cloud storage is used as input. In this analysis, the AI ​​processes the dataset to detect anomalies and patterns. A generative AI model is used to clean the data and extract features, resulting in the generation of insights. Specifically, an anomaly detection algorithm is configured to issue an alert when a set threshold is exceeded.

[0079] Step 4:

[0080] The server sends the analyzed results to the user's device. The output is visualized data obtained as a result of the analysis. The data is processed into a format that is easy for the user to understand (e.g., graphs and charts). The server sends data to the device in real time and works to return a response quickly. Specifically, the server periodically pushes data notifications and provides an interactive dashboard.

[0081] Step 5:

[0082] Users make decisions based on the information displayed on their devices. Here, the visualized results become the input data, and the user's judgment becomes the output. For example, users can understand changes in ocean conditions and use this information to optimize fishing grounds or adjust navigation routes. In this way, users can develop effective strategies based on real-time data. Specifically, users analyze the provided data and decide on their next action.

[0083] (Application Example 1)

[0084] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0085] The present invention aims to improve the safety and efficiency of maritime vehicles by capturing environmental changes in the ocean in real time and providing optimal route guidance based on this information. Conventional systems have limited use of ocean data, making it difficult to make rapid, real-time decisions, and this invention seeks to solve that problem.

[0086] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0087] In this invention, the server includes a detection device for acquiring oceanographic information in real time, a transmission means for transmitting the acquired information to a large-scale data storage device, and an analysis means using machine learning technology to perform information analysis on the large-scale data storage device. This makes it possible to optimize the route of a moving object based on oceanographic change information.

[0088] A "detection device" is a device installed to acquire oceanographic information in real time.

[0089] A "large-scale data storage system" is a system that stores acquired information and makes it accessible when needed.

[0090] A "transmission means" is a means for transmitting information acquired from a detection device to a large-scale data storage device.

[0091] "Analysis methods using machine learning techniques" refer to methods that use machine learning algorithms to process and analyze acquired information.

[0092] A "display means" is an interface used to provide the analyzed results to the user.

[0093] A "route guidance system" is a means of suggesting the optimal route for a moving object based on analyzed information.

[0094] A signal conversion method using "focusing technology" is a technique that precisely processes information from a detection device and is a means for appropriately controlling the direction and intensity of the signal.

[0095] An "abnormal state detection means" is a means of detecting a state that is different from the normal state from the analyzed information and issuing a warning.

[0096] In implementing this system, the server transmits oceanographic information acquired in real time from detection devices installed in the ocean to a large-scale data storage device, where it performs analysis using machine learning techniques. Focusing technology is applied to the detection devices, enabling precise information acquisition. As a result, the server can quickly detect abnormal conditions based on ocean fluctuations and provide the results to the user's terminal as optimal navigation guidance.

[0097] The terminal presents the received analysis results to the user and supports real-time decision-making through the display mechanism. Based on the presented information, the user can select an efficient and safe route. This entire process utilizes analysis algorithms written in programming languages ​​such as Python.

[0098] For example, when operating a marine drone, if the server detects a sudden change in waves, that information is immediately notified to the terminal, and a new navigation route is suggested, allowing the user to respond quickly.

[0099] An example of a prompt message would be presented to the user as follows: "A sudden change in wave height has been detected. We will suggest an alternative route based on the current navigation conditions. Do you want to see the next steps?"

[0100] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0101] Step 1:

[0102] The server acquires real-time oceanographic information (wave height, current speed, wind speed, etc.) from sensing devices installed in the ocean. The input is raw data from the sensors. The data is sent to the server and processed using focusing technology to obtain accurate information.

[0103] Step 2:

[0104] The server transmits the acquired data to a large-scale data storage device. It receives processed ocean data as input and securely stores the data in cloud storage space as output. The data is organized and made quickly accessible when needed.

[0105] Step 3:

[0106] The server uses machine learning techniques to analyze data stored in the cloud. The input is organized ocean data in the cloud, and the output is the analysis results from anomaly detection and pattern recognition. Here, an AI model identifies outliers to detect anomalies.

[0107] Step 4:

[0108] The server detects abnormal conditions based on the analysis results and immediately generates a warning if an abnormality occurs. It uses the analysis results as input and generates a warning message as output. An example of a prompt message would be, "A rapid change in wave height has been detected. We will suggest an alternative route based on the current navigation conditions."

[0109] Step 5:

[0110] The terminal displays warning messages and analysis results received from the server to the user. It receives warning messages as input and presents them on the screen in a user-friendly format as output. Based on this, the user can immediately change their route.

[0111] Step 6:

[0112] The user selects the optimal route based on the information presented on the terminal and operates the mobile vehicle as needed. The terminal's display serves as input, and commands for the mobile vehicle are generated as output. Specifically, it allows for immediate adaptation to new navigation routes.

[0113] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0114] This invention combines a system for collecting and analyzing marine information in real time with an emotion engine that recognizes user emotions. The embodiments are described in detail below.

[0115] First, sensor devices installed in the ocean continuously collect data such as ocean current speed, wave height, and wind speed. This data is then processed using beamforming technology to improve its accuracy. A server receives the data transmitted from the sensor devices, processes it, and then securely sends it to cloud storage.

[0116] On the cloud, the server uses AI to analyze the data. If an anomaly is detected in the analyzed data, the server immediately generates an alert. The analysis results are then sent to the user's device. The user can receive the information via a smartphone, tablet, or other device and check the ocean conditions.

[0117] Furthermore, this invention incorporates an emotion engine. The emotion engine recognizes emotions on the user's device based on the user's voice, facial expressions, and input feedback. For example, if the user is feeling anxious, the interface can be modified to adjust the details of the anomaly detection alert and the suggested actions, thereby providing a sense of security.

[0118] As a concrete example, consider a scenario where a fisherman receives an alert about an approaching storm. The emotion engine analyzes the user's reaction to understand their emotions and then calmly suggests a safe evacuation route. It also provides an option to view more detailed weather information. This allows the user to make a more confident and appropriate decision.

[0119] Thus, the present invention provides a system that improves the user experience and supports faster and more effective decision-making by introducing emotion recognition into the provision of marine information.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The server receives data in real time from sensor devices installed in the ocean. The data includes ocean current speed, wave height, and wind speed, and beamforming technology is used to improve accuracy.

[0123] Step 2:

[0124] The server formats the received data appropriately and sends it to cloud storage. During this process, the data is transmitted via a secure communication protocol, ensuring confidentiality.

[0125] Step 3:

[0126] A server in the cloud uses AI to analyze the data. The AI ​​model has already learned normal data patterns and uses them to detect anomalies in the data.

[0127] Step 4:

[0128] Based on the analysis results, the server immediately generates an alert if an anomaly is detected. The generated alert is immediately sent to the user's terminal.

[0129] Step 5:

[0130] The device receives an alert and displays a warning to the user. More detailed analysis and suggested countermeasures are also displayed on the device.

[0131] Step 6:

[0132] The device's built-in emotion engine captures the user's voice and facial expressions and analyzes their current emotional state. The interface dynamically adjusts based on the analysis results, providing information that matches the user's emotions.

[0133] Step 7:

[0134] The user makes decisions based on information and suggestions received through their device. The choices presented by the emotion engine are designed to enhance the user's sense of security and UX, and change dynamically.

[0135] (Example 2)

[0136] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0137] Conventional marine information gathering systems provide users with only simple data analysis results, failing to consider the user's emotional state. Therefore, improving the user experience in information utilization is a challenge. In particular, to avoid situations where users feel anxious or confused during emergencies, it is necessary to adjust the content and method of information provision according to their emotions.

[0138] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0139] In this invention, the server includes a sensing device for acquiring ocean information in real time, a communication means for transmitting the acquired ocean data to a storage device, and an analysis means using a computational model for performing data analysis on an information infrastructure. This makes it possible to recognize the user's emotions and provide effective information by adjusting the interface according to those emotions.

[0140] A "sensing device" is a device installed to acquire oceanographic information in real time, and its role is to collect data such as ocean current speed, wave height, and wind speed.

[0141] "Collective storage" refers to a storage method for securely storing acquired marine data, and usually refers to a cloud environment such as a data center.

[0142] "Communication means" refers to means for transmitting data collected by a sensing device to a storage device, and includes technologies for transferring data via a network.

[0143] "Information infrastructure" refers to a set of infrastructure for processing and storing data, including cloud platforms that enable data analysis.

[0144] A "computational model" is an artificial intelligence model or algorithm used for data analysis, enabling the analysis of acquired data to gain useful insights.

[0145] "Analysis means" refers to a series of processing means for analyzing acquired data using a computational model, and is used to achieve anomaly detection and information extraction.

[0146] "Emotion recognition means" refers to means for recognizing the user's emotional state and adjusting the interface based on the information obtained, and includes technologies for analyzing the user's voice, facial expressions, and feedback.

[0147] This invention is a system that acquires and analyzes marine information in real time and provides information while taking into account the user's emotions. Specific embodiments are shown below.

[0148] The server receives information from sensing devices installed in the ocean. The sensing devices acquire data such as ocean current speed, wave height, and wind speed, and transmit the data to the server using wireless communication technology. The server precisely processes the received data using directional control technology, formats the data, and transmits it to integrated storage. Integrated storage uses a data center in the cloud, and the data is stored securely using the SSL / TLS protocol.

[0149] On the cloud, the server uses computational models to perform data analysis. Generative AI models function as analytical tools, for example, to detect and predict anomalies in the ocean. These models are based on deep learning algorithms and can be continuously learned and improved. When an anomaly is detected, the server immediately generates an alert and sends that information to the user's terminal.

[0150] The user's device is equipped with emotion recognition capabilities. The device analyzes the user's emotions based on their voice, facial expressions, and input data, and provides an optimal interface. For example, if a fisherman receives an alert about an approaching storm, the emotion engine detects the user's anxiety and calmly suggests a safe evacuation route, offering the option to view detailed weather information. In this way, the user can receive the necessary information while feeling reassured.

[0151] An example of a prompt message could be: "A storm is approaching. Please use the emotion engine to suggest measures to alleviate the user's anxiety." This invention prioritizes user experience and aims to enhance the effectiveness of information delivery.

[0152] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0153] Step 1:

[0154] The server receives information from sensing devices installed in the ocean. Input data includes ocean current speed, wave height, and wind speed. The data is processed using directional control technology to output precise data with minimal noise. This processing improves the accuracy and reliability of the data.

[0155] Step 2:

[0156] The server transmits the formatted data to the integrated storage. The input is precise data processed using directional control technology. The data is securely stored in a cloud data center, with security ensured by the SSL / TLS protocol. This ensures the data is securely protected and ready for subsequent analysis.

[0157] Step 3:

[0158] The server retrieves data stored in integrated memory and performs analysis using a generative AI model. The input is high-resolution data stored in the cloud. Based on pre-trained algorithms, the generative AI model performs anomaly detection and predicts ocean conditions, outputting analysis results. This analysis extracts important ocean information.

[0159] Step 4:

[0160] The server generates alerts as needed based on the analysis results and sends them to the user's device. The input is the analysis results of the generating AI model. The specific alert content is formatted as a text message and pushed to the user's device via the network. This allows the user to immediately understand the situation.

[0161] Step 5:

[0162] The device analyzes the user's emotions using emotion recognition based on received alerts. Inputs include the user's voice, facial expressions, and input feedback. The device compares this data, optimizes the interface according to the emotional state, and outputs a new interface. For example, a user experiencing anxiety will be shown calming suggestions and detailed information. This improves the user experience and enables faster decision-making.

[0163] (Application Example 2)

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

[0165] In today's transportation environment, there is a demand for timely and precise acquisition and analysis of marine information. However, there is a challenge in providing optimal feedback while also considering the user's emotions. As a result, users may be unable to take appropriate measures, and their psychological burden may increase. This invention aims to solve these problems by providing feedback that is tailored to the user's emotions, in addition to real-time data acquisition and analysis.

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

[0167] In this invention, the server includes a detection device for acquiring marine information in real time, a communication means for transmitting the acquired marine data to a large-scale data service, an analysis means using artificial intelligence to perform data analysis in a large-scale data processing environment, and an emotion analysis means for recognizing the user's emotions and providing optimal feedback. This makes it possible to provide appropriate information based on the user's emotions while analyzing marine environment data.

[0168] A "detection device" is a device installed to measure changes in the marine environment in real time, and it acquires data such as ocean current speed, wave height, and wind speed.

[0169] A "large-scale data service" is a cloud-based information system used to centrally manage and analyze collected marine data.

[0170] "Communication means" refers to digital communication technology used to transmit data acquired from detection devices to large-scale data services.

[0171] A "large-scale data processing environment" is a platform that provides computing resources for rapidly and efficiently analyzing large amounts of data on the cloud.

[0172] "Analysis methods using artificial intelligence" refers to machine learning techniques that automatically analyze data and use the results to evaluate the state of the ocean.

[0173] "Output means" refers to a function for displaying or notifying users of the analyzed data on their information terminals.

[0174] "Emotional analysis means" refers to technology that analyzes the user's voice and facial expressions, recognizes their emotional state, and generates optimal feedback.

[0175] "Adaptive measures" refer to control technologies that dynamically adjust the information and feedback provided based on analyzed data and user emotions.

[0176] The system for realizing this invention has a configuration that acquires ocean information in real time and provides information tailored to the user's emotions. A specific embodiment of this system is shown below.

[0177] The server first receives data from detection devices installed in the ocean. This data includes information such as ocean current speed, wave height, and wind speed, and is collected through large-scale data services. Real-time information acquisition is possible because the data is reliably transmitted via communication means.

[0178] This data is processed using artificial intelligence-based analysis methods in a large-scale data processing environment. Specifically, machine learning algorithms are used, and when an anomaly is detected, the user's terminal is quickly notified. This allows the user to immediately understand the situation and take appropriate action.

[0179] Furthermore, the device is equipped with an emotion analysis system. Using the user's voice and facial expression data, an AI model is used to perform emotion analysis. Based on this analysis, an adaptive system adjusts the feedback content to provide information best suited to the user's emotions.

[0180] As a concrete example, consider a scenario where information about a sudden weather change is received during a long-distance voyage. If the system determines through emotion analysis that the user is feeling anxious, the voice assistant will deliver safety information in a calm tone and, if necessary, play soothing music to reassure the user.

[0181] An example of a prompt message would be, "I would like to feel more at ease during this long voyage. Please provide safety information regarding weather changes and relaxation methods." In this way, it is possible to provide services that take the user's feelings into consideration.

[0182] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0183] Step 1:

[0184] The server receives data from detection devices installed in the ocean. The input consists of real-time data such as ocean current speed, wave height, and wind speed. This data is transmitted to a large-scale data service via communication channels and stored in cloud storage. The output is data sent to the cloud in a structured format.

[0185] Step 2:

[0186] The server analyzes data stored in the cloud using machine learning algorithms in a large-scale data processing environment. The input is enterprise-level marine data. Anomaly detection algorithms are applied as data processing to analyze patterns that deviate from the norm. The output is the analysis results, including information on detected anomalies.

[0187] Step 3:

[0188] The terminal receives analysis results sent from the server and notifies the user. Input consists of analyzed ocean data and anomaly information sent from the server. Output from the terminal includes notification messages and information provided through a visual interface for the user.

[0189] Step 4:

[0190] The device analyzes the user's emotions using emotion analysis tools. Input consists of the user's voice and facial expression data. A generative AI model is used to process the data and determine the user's emotional state. Output is information regarding the user's emotional state.

[0191] Step 5:

[0192] Based on the user's emotion analysis results, the device adjusts the feedback content through adaptive mechanisms. Input consists of the emotion analysis results and notification information based on abnormal data. Specifically, it generates feedback appropriate to the received data and presents content and suggestions tailored to the user's psychological state. The output is the adjusted feedback, providing the user with a sense of relaxation and security.

[0193] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0194] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0195] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0196] [Second Embodiment]

[0197] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0198] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0199] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0200] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0201] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0202] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0203] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0204] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0205] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0206] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0207] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0208] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0209] This invention is a system for collecting and analyzing marine information in real time, and its embodiments are described in detail below.

[0210] First, sensor devices installed in the ocean continuously acquire data such as wave height, ocean current speed, and wind speed. These sensor devices can collect highly accurate data using beamforming technology.

[0211] Next, the server receives the data transmitted from the sensor device. The received data is converted to an appropriate format and securely sent to cloud storage. Secure communication protocols are used to maintain the confidentiality and integrity of the data.

[0212] The cloud-based server performs advanced AI-powered analysis on the received data. In this analysis process, the AI ​​evaluates the dataset and identifies anomalies and patterns. If necessary, the server immediately issues an alert if an anomaly is detected.

[0213] The analysis results are further processed and sent to the user's device. Users can review the provided data through an application or interface and make decisions based on the information obtained. For example, those involved in the fishing industry can understand the real-time changing ocean conditions and use this information to select fishing grounds and optimize navigation routes.

[0214] This format allows various maritime-related business sectors to utilize highly accurate data in real time and respond quickly. Through integration with other systems, the aim is to promote the commercial use of data and contribute to enhancing Japan's position as a maritime nation.

[0215] The following describes the processing flow.

[0216] Step 1:

[0217] The server receives raw data from the sensor device. The sensor device continuously collects oceanographic information and transmits it to the server. The server then formats the signal in preparation for further processing.

[0218] Step 2:

[0219] The server applies beamforming technology to enhance signals in specific directions and suppress noise. This process improves data accuracy and enhances the quality of analysis.

[0220] Step 3:

[0221] The server converts the processed data into a standard format such as JSON and uploads it to cloud storage. During this process, a secure communication protocol is used to ensure data security.

[0222] Step 4:

[0223] A server in the cloud uses AI to analyze the data. The AI ​​analyzes the data and applies anomaly detection models to identify data points that deviate from normal patterns.

[0224] Step 5:

[0225] The server compiles the analysis results and immediately generates an alert if an anomaly is detected. This alert is sent to the relevant users to prompt a quick response.

[0226] Step 6:

[0227] The analyzed data is sent to the user's device. Through the application, the user can view this data and monitor changes in the marine environment in real time.

[0228] (Example 1)

[0229] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0230] Modern ocean observation systems require real-time data acquisition and analysis, but conventional systems often suffer from insufficient data accuracy and analysis speed, resulting in delayed anomaly detection. Furthermore, data transmission and storage may not guarantee confidentiality or integrity. These issues hinder efficient decision-making in ocean-related industries.

[0231] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0232] In this invention, the server includes a measuring device for acquiring marine information in real time, a data transmission means for transmitting the acquired data to a remote storage device, and an analysis means using a machine learning model for performing information analysis on the remote storage device. This improves the accuracy of data collection and the speed of analysis, enabling rapid and accurate anomaly detection, and thus facilitating efficient decision-making in marine-related industrial fields.

[0233] "Oceanographic information" refers to data that indicates the state of the ocean, including information such as wave height, ocean current speed, and wind speed.

[0234] A "measuring device" is a sensor device installed to acquire physical information under specific environmental conditions, enabling high-precision data collection.

[0235] A "remote storage device" is a system for storing and managing data located on the internet, such as cloud storage.

[0236] "Data transmission means" refers to communication technology used to transmit acquired information to another system or storage.

[0237] A "machine learning model" is an algorithm or set of algorithms that allows a computer to recognize patterns based on experience and automatically learn to improve.

[0238] "Analysis means" refers to methods and techniques for analyzing collected data and extracting useful information.

[0239] "Spatial filtering technology" refers to methods for extracting specific signals or data with high precision, and mainly includes technologies such as beamforming.

[0240] An "abnormal information provision means" is a system or method for identifying patterns or conditions that are different from the normal ones from analyzed data and notifying the user.

[0241] This invention outlines an embodiment of a system for acquiring and analyzing marine information in real time and providing it to users. This system consists of a measuring device, a server, and a terminal.

[0242] First, the measuring device is installed in the ocean to continuously acquire oceanographic information such as wave height, ocean current speed, and wind speed. This sensor can collect data with high precision using spatial filtering technology, enabling accurate measurements.

[0243] Next, the server receives the data transmitted from the measuring device. The received data is stored in a remote storage device via a data transmission means. A secure communication protocol is used, and it is designed to maintain the confidentiality and integrity of the data. This remote storage device consists of cloud storage, which can securely store and manage large amounts of data.

[0244] The server performs analysis on data stored in the remote storage device using machine learning models. This analysis allows for evaluation of the dataset and identification of anomalies and specific patterns. Based on the analysis results, an anomaly information provision system generates timely information that users need. This analysis process is highly automated and processed quickly and efficiently.

[0245] Ultimately, the user receives the analysis results sent from the server on their device. The data is displayed through the device's application or interface, allowing the user to check the information in real time. For example, those involved in the fishing industry can use the analyzed oceanographic information to select fishing grounds and optimize navigation routes.

[0246] A concrete example of a prompt message would be something like, "Please explain the methods for analyzing data from marine sensors to detect anomalies." This allows users to efficiently obtain information and be supported in making appropriate decisions.

[0247] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0248] Step 1:

[0249] The server receives oceanographic information transmitted from the measuring devices. At this stage, various data such as wave height and wind speed are input. Since the received data is in its raw state, it needs to be formatted. Specifically, the server converts the data into a standardized format and temporarily stores it in memory. To maintain data consistency, data validation checks are also performed simultaneously.

[0250] Step 2:

[0251] The server transfers the received data to a remote storage device. Standardized data is used as input. This transfer process utilizes a secure communication protocol (e.g., TLS) to ensure data confidentiality and integrity. The data is stored in cloud storage and accessible in a database for extended periods. Specifically, the server checks for communication errors and retransmits the data as needed.

[0252] Step 3:

[0253] The server analyzes data stored in the cloud using machine learning models. Data from cloud storage is used as input. In this analysis, the AI ​​processes the dataset to detect anomalies and patterns. A generative AI model is used to clean the data and extract features, resulting in the generation of insights. Specifically, an anomaly detection algorithm is configured to issue an alert when a set threshold is exceeded.

[0254] Step 4:

[0255] The server sends the analyzed results to the user's device. The output is visualized data obtained as a result of the analysis. The data is processed into a format that is easy for the user to understand (e.g., graphs and charts). The server sends data to the device in real time and works to return a response quickly. Specifically, the server periodically pushes data notifications and provides an interactive dashboard.

[0256] Step 5:

[0257] Users make decisions based on the information displayed on their devices. Here, the visualized results become the input data, and the user's judgment becomes the output. For example, users can understand changes in ocean conditions and use this information to optimize fishing grounds or adjust navigation routes. In this way, users can develop effective strategies based on real-time data. Specifically, users analyze the provided data and decide on their next action.

[0258] (Application Example 1)

[0259] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0260] The present invention aims to improve the safety and efficiency of maritime vehicles by capturing environmental changes in the ocean in real time and providing optimal route guidance based on this information. Conventional systems have limited use of ocean data, making it difficult to make rapid, real-time decisions, and this invention seeks to solve that problem.

[0261] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0262] In this invention, the server includes a detection device for acquiring oceanographic information in real time, a transmission means for transmitting the acquired information to a large-scale data storage device, and an analysis means using machine learning technology to perform information analysis on the large-scale data storage device. This makes it possible to optimize the route of a moving object based on oceanographic change information.

[0263] A "detection device" is a device installed to acquire oceanographic information in real time.

[0264] A "large-scale data storage system" is a system that stores acquired information and makes it accessible when needed.

[0265] A "transmission means" is a means for transmitting information acquired from a detection device to a large-scale data storage device.

[0266] "Analysis methods using machine learning techniques" refer to methods that use machine learning algorithms to process and analyze acquired information.

[0267] A "display means" is an interface used to provide the analyzed results to the user.

[0268] A "route guidance system" is a means of suggesting the optimal route for a moving object based on analyzed information.

[0269] A signal conversion method using "focusing technology" is a technique that precisely processes information from a detection device and is a means for appropriately controlling the direction and intensity of the signal.

[0270] An "abnormal state detection means" is a means of detecting a state that is different from the normal state from the analyzed information and issuing a warning.

[0271] In implementing this system, the server transmits oceanographic information acquired in real time from detection devices installed in the ocean to a large-scale data storage device, where it performs analysis using machine learning techniques. Focusing technology is applied to the detection devices, enabling precise information acquisition. As a result, the server can quickly detect abnormal conditions based on ocean fluctuations and provide the results to the user's terminal as optimal navigation guidance.

[0272] The terminal presents the received analysis results to the user and supports real-time decision-making through the display mechanism. Based on the presented information, the user can select an efficient and safe route. This entire process utilizes analysis algorithms written in programming languages ​​such as Python.

[0273] For example, when operating a marine drone, if the server detects a sudden change in waves, that information is immediately notified to the terminal, and a new navigation route is suggested, allowing the user to respond quickly.

[0274] An example of a prompt message would be presented to the user as follows: "A rapid change in wave height has been detected. We will suggest an alternative route based on the current navigation conditions. Do you want to see the next steps?"

[0275] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0276] Step 1:

[0277] The server acquires real-time oceanographic information (wave height, current speed, wind speed, etc.) from sensing devices installed in the ocean. The input is raw data from the sensors. The data is sent to the server and processed using focusing technology to obtain accurate information.

[0278] Step 2:

[0279] The server sends the acquired data to the large-scale data storage device. It receives the processed ocean data as input and securely stores the data in the storage space on the cloud as output. The data is organized so that it can be quickly accessed when needed.

[0280] Step 3:

[0281] The server analyzes the data stored in the cloud using machine learning techniques. The input is the organized ocean data in the cloud, and the output is the analysis results by anomaly detection and pattern recognition. Here, the AI model identifies abnormal values to detect anomalies.

[0282] Step 4:

[0283] The server detects an abnormal state based on the analysis results and immediately generates a warning if an anomaly occurs. It utilizes the analysis results as input and generates a warning message as output. The warning is in the form of, for example, "A sudden change in wave height has been detected. An alternative route is proposed based on the current navigation conditions."

[0284] Step 5:

[0285] The terminal displays the warning message and analysis results received from the server to the user. It receives the warning message as input and presents it on the screen in a user-friendly format as output. The user can immediately change the route based on this.

[0286] Step 6:

[0287] The user selects the optimal route based on the information presented by the terminal and operates the moving object as needed. It refers to the display content of the terminal as input and creates commands for the moving object as output. As a specific operation, immediate adaptation to a new navigation route is possible.

[0288] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0289] This invention combines a system for collecting and analyzing marine information in real time with an emotion engine that recognizes user emotions. The embodiments are described in detail below.

[0290] First, sensor devices installed in the ocean continuously collect data such as ocean current speed, wave height, and wind speed. This data is then processed using beamforming technology to improve its accuracy. A server receives the data transmitted from the sensor devices, processes it, and then securely sends it to cloud storage.

[0291] On the cloud, the server uses AI to analyze the data. If an anomaly is detected in the analyzed data, the server immediately generates an alert. The analysis results are then sent to the user's device. The user can receive the information via a smartphone, tablet, or other device and check the ocean conditions.

[0292] Furthermore, this invention incorporates an emotion engine. The emotion engine recognizes emotions on the user's device based on the user's voice, facial expressions, and input feedback. For example, if the user is feeling anxious, the interface can be modified to adjust the details of the anomaly detection alert and the suggested actions, thereby providing a sense of security.

[0293] As a concrete example, consider a scenario where a fisherman receives an alert about an approaching storm. The emotion engine analyzes the user's reaction to understand their emotions and then calmly suggests a safe evacuation route. It also provides an option to view more detailed weather information. This allows the user to make a more confident and appropriate decision.

[0294] Thus, the present invention provides a system that improves the user experience and supports faster and more effective decision-making by introducing emotion recognition into the provision of marine information.

[0295] The following describes the processing flow.

[0296] Step 1:

[0297] The server receives data in real time from sensor devices installed in the ocean. The data includes ocean current speed, wave height, wind speed, etc., and beamforming technology is used to improve accuracy.

[0298] Step 2:

[0299] The server formats the received data appropriately and sends it to cloud storage. During this process, the data is transmitted via a secure communication protocol, ensuring confidentiality.

[0300] Step 3:

[0301] A server in the cloud uses AI to analyze the data. The AI ​​model has already learned normal data patterns and uses them to detect anomalies in the data.

[0302] Step 4:

[0303] If the server detects an anomaly based on the analysis results, it immediately generates an alert. The generated alert is immediately sent to the user's terminal.

[0304] Step 5:

[0305] The device receives an alert and displays a warning to the user. More detailed analysis and suggested countermeasures are also displayed on the device.

[0306] Step 6:

[0307] The emotion engine in the terminal captures the user's voice and expression and analyzes the current emotional state. According to the analysis result, the interface is dynamically adjusted to provide information that matches the user's emotion.

[0308] Step 7:

[0309] The user makes a decision based on the information and suggestions received via the terminal. The options presented by the emotion engine are considered in terms of the user's sense of security and the improvement of the UX, and they change dynamically.

[0310] (Example 2)

[0311] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0312] In the conventional marine information collection system, the information provided to the user remains at the level of simple data analysis results, and information is not provided considering the user's emotional state. Therefore, improving the user experience in information utilization is an issue. In particular, in order to avoid situations where the user feels anxious or confused during an emergency, it is necessary to adjust the content and method of information provision according to the emotion.

[0313] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0314] In this invention, the server includes a sensing device for acquiring marine information in real time, a communication means for transmitting the acquired marine data to integrated storage, and an analysis means using an arithmetic model for performing data analysis on an information infrastructure. As a result, it is possible to recognize the user's emotion and provide effective information with the interface adjusted according to the emotion.

[0315] <00​​​ "Collective storage" refers to a storage method for securely storing acquired marine data, and usually refers to a cloud environment such as a data center.

[0317] "Communication means" refers to means for transmitting data collected by a sensing device to a storage device, and includes technologies for transferring data via a network.

[0318] "Information infrastructure" refers to a set of infrastructure for processing and storing data, including cloud platforms that enable data analysis.

[0319] A "computational model" is an artificial intelligence model or algorithm used for data analysis, enabling the analysis of acquired data to gain useful insights.

[0320] "Analysis means" refers to a series of processing means for analyzing acquired data using a computational model, and is used to achieve anomaly detection and information extraction.

[0321] "Emotion recognition means" refers to means for recognizing the user's emotional state and adjusting the interface based on the information obtained, and includes technologies for analyzing the user's voice, facial expressions, and feedback.

[0322] This invention is a system that acquires and analyzes marine information in real time and provides information while taking into account the user's emotions. Specific embodiments are shown below.

[0323] The server receives information from sensing devices installed in the ocean. The sensing devices acquire data such as ocean current speed, wave height, and wind speed, and transmit the data to the server using wireless communication technology. The server precisely processes the received data using directional control technology, formats the data, and transmits it to integrated storage. Integrated storage uses a data center in the cloud, and the data is stored securely using the SSL / TLS protocol.

[0324] On the cloud, the server uses computational models to perform data analysis. Generative AI models function as analytical tools, for example, to detect and predict anomalies in the ocean. These models are based on deep learning algorithms and can be continuously learned and improved. When an anomaly is detected, the server immediately generates an alert and sends that information to the user's terminal.

[0325] The user's device is equipped with emotion recognition capabilities. The device analyzes the user's emotions based on their voice, facial expressions, and input data, and provides an optimal interface. For example, if a fisherman receives an alert about an approaching storm, the emotion engine detects the user's anxiety and calmly suggests a safe evacuation route, offering the option to view detailed weather information. In this way, the user can receive the necessary information while feeling reassured.

[0326] An example of a prompt message could be: "A storm is approaching. Please use the emotion engine to suggest measures to alleviate the user's anxiety." This invention prioritizes user experience and aims to enhance the effectiveness of information delivery.

[0327] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0328] Step 1:

[0329] The server receives information from sensing devices installed in the ocean. Input data includes ocean current speed, wave height, and wind speed. The data is processed using directional control technology to output precise data with minimal noise. This processing improves the accuracy and reliability of the data.

[0330] Step 2:

[0331] The server transmits the formatted data to the integrated storage. The input is precise data processed using directional control technology. The data is securely stored in a cloud data center, with security ensured by the SSL / TLS protocol. This ensures the data is securely protected and ready for subsequent analysis.

[0332] Step 3:

[0333] The server retrieves data stored in integrated memory and performs analysis using a generative AI model. The input is high-resolution data stored in the cloud. Based on pre-trained algorithms, the generative AI model performs anomaly detection and predicts ocean conditions, outputting analysis results. This analysis extracts important ocean information.

[0334] Step 4:

[0335] The server generates alerts as needed based on the analysis results and sends them to the user's device. The input is the analysis results of the generating AI model. The specific alert content is formatted as a text message and pushed to the user's device via the network. This allows the user to immediately understand the situation.

[0336] Step 5:

[0337] The device analyzes the user's emotions using emotion recognition based on received alerts. Inputs include the user's voice, facial expressions, and input feedback. The device compares this data, optimizes the interface according to the emotional state, and outputs a new interface. For example, a user experiencing anxiety will be shown calming suggestions and detailed information. This improves the user experience and enables faster decision-making.

[0338] (Application Example 2)

[0339] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0340] In today's transportation environment, there is a demand for timely and precise acquisition and analysis of marine information. However, there is a challenge in providing optimal feedback while also considering the user's emotions. As a result, users may be unable to take appropriate measures, and their psychological burden may increase. This invention aims to solve these problems by providing feedback that is tailored to the user's emotions, in addition to real-time data acquisition and analysis.

[0341] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0342] In this invention, the server includes a detection device for acquiring marine information in real time, a communication means for transmitting the acquired marine data to a large-scale data service, an analysis means using artificial intelligence to perform data analysis in a large-scale data processing environment, and an emotion analysis means for recognizing the user's emotions and providing optimal feedback. This makes it possible to provide appropriate information based on the user's emotions while analyzing marine environment data.

[0343] A "detection device" is a device installed to measure changes in the marine environment in real time, and it acquires data such as ocean current speed, wave height, and wind speed.

[0344] A "large-scale data service" is a cloud-based information system used to centrally manage and analyze collected marine data.

[0345] "Communication means" refers to digital communication technology used to transmit data acquired from detection devices to large-scale data services.

[0346] A "large-scale data processing environment" is a platform that provides computing resources for rapidly and efficiently analyzing large amounts of data on the cloud.

[0347] "Analysis methods using artificial intelligence" refers to machine learning techniques that automatically analyze data and use the results to evaluate the state of the ocean.

[0348] "Output means" refers to a function for displaying or notifying users of the analyzed data on their information terminals.

[0349] "Emotional analysis means" refers to technology that analyzes the user's voice and facial expressions, recognizes their emotional state, and generates optimal feedback.

[0350] "Adaptive measures" refer to control technologies that dynamically adjust the information and feedback provided based on analyzed data and user emotions.

[0351] The system for realizing this invention has a configuration that acquires ocean information in real time and provides information tailored to the user's emotions. A specific embodiment of this system is shown below.

[0352] The server first receives data from detection devices installed in the ocean. This data includes information such as ocean current speed, wave height, and wind speed, and is collected through large-scale data services. Real-time information acquisition is possible because the data is reliably transmitted via communication means.

[0353] This data is processed using artificial intelligence-based analysis methods in a large-scale data processing environment. Specifically, machine learning algorithms are used, and when an anomaly is detected, the user's terminal is quickly notified. This allows the user to immediately understand the situation and take appropriate action.

[0354] Furthermore, the device is equipped with an emotion analysis system. Using the user's voice and facial expression data, an AI model is used to perform emotion analysis. Based on this analysis, an adaptive system adjusts the feedback content to provide information best suited to the user's emotions.

[0355] As a concrete example, consider a scenario where information about a sudden weather change is received during a long-distance voyage. If the system determines, through emotion analysis, that the user is feeling anxious, the voice assistant will deliver safety information in a calm tone and, if necessary, play soothing music to reassure the user.

[0356] An example of a prompt message would be, "I would like to feel more at ease during this long voyage. Please provide safety information regarding weather changes and relaxation methods." In this way, it is possible to provide services that take the user's feelings into consideration.

[0357] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0358] Step 1:

[0359] The server receives data from detection devices installed in the ocean. The input consists of real-time data such as ocean current speed, wave height, and wind speed. This data is transmitted to a large-scale data service via communication channels and stored in cloud storage. The output is data sent to the cloud in a structured format.

[0360] Step 2:

[0361] The server analyzes data stored in the cloud using machine learning algorithms in a large-scale data processing environment. The input is enterprise-level marine data. Anomaly detection algorithms are applied as data processing to analyze patterns that deviate from the norm. The output is the analysis results, including information on detected anomalies.

[0362] Step 3:

[0363] The terminal receives analysis results sent from the server and notifies the user. Input consists of analyzed ocean data and anomaly information sent from the server. Output from the terminal includes notification messages and information provided through a visual interface for the user.

[0364] Step 4:

[0365] The device analyzes the user's emotions using emotion analysis tools. Input consists of the user's voice and facial expression data. A generative AI model is used to process the data and determine the user's emotional state. Output is information regarding the user's emotional state.

[0366] Step 5:

[0367] Based on the user's emotion analysis results, the device adjusts the feedback content through adaptive mechanisms. Input consists of the emotion analysis results and notification information based on abnormal data. Specifically, it generates feedback appropriate to the received data and presents content and suggestions tailored to the user's psychological state. The output is the adjusted feedback, providing the user with a sense of relaxation and security.

[0368] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0369] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0370] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0371] [Third Embodiment]

[0372] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0373] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0374] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0375] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0376] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0377] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0378] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0379] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0380] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0381] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0382] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0383] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0384] This invention is a system for collecting and analyzing marine information in real time, and its embodiments are described in detail below.

[0385] First, sensor devices installed in the ocean continuously acquire data such as wave height, ocean current speed, and wind speed. These sensor devices can collect highly accurate data using beamforming technology.

[0386] Next, the server receives the data transmitted from the sensor device. The received data is converted to an appropriate format and securely sent to cloud storage. Secure communication protocols are used to maintain the confidentiality and integrity of the data.

[0387] The cloud-based server performs advanced AI-powered analysis on the received data. In this analysis process, the AI ​​evaluates the dataset and identifies anomalies and patterns. If necessary, the server immediately issues an alert if an anomaly is detected.

[0388] The analysis results are further processed and sent to the user's device. Users can review the provided data through an application or interface and make decisions based on the information obtained. For example, those involved in the fishing industry can understand the real-time changing ocean conditions and use this information to select fishing grounds and optimize navigation routes.

[0389] This format allows various maritime-related business sectors to utilize highly accurate data in real time and respond quickly. Through integration with other systems, the aim is to promote the commercial use of data and contribute to enhancing Japan's position as a maritime nation.

[0390] The following describes the processing flow.

[0391] Step 1:

[0392] The server receives raw data from the sensor device. The sensor device continuously collects oceanographic information and transmits it to the server. The server then formats the signal in preparation for further processing.

[0393] Step 2:

[0394] The server applies beamforming technology to enhance signals in specific directions and suppress noise. This process improves data accuracy and enhances the quality of analysis.

[0395] Step 3:

[0396] The server converts the processed data into a standard format such as JSON and uploads it to cloud storage. During this process, a secure communication protocol is used to ensure data security.

[0397] Step 4:

[0398] A server in the cloud uses AI to analyze the data. The AI ​​analyzes the data and applies anomaly detection models to identify data points that deviate from normal patterns.

[0399] Step 5:

[0400] The server compiles the analysis results and immediately generates an alert if an anomaly is detected. This alert is sent to the relevant users to prompt a quick response.

[0401] Step 6:

[0402] The analyzed data is sent to the user's device. Through the application, the user can view this data and monitor changes in the marine environment in real time.

[0403] (Example 1)

[0404] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0405] Modern ocean observation systems require real-time data acquisition and analysis, but conventional systems often suffer from insufficient data accuracy and analysis speed, resulting in delayed anomaly detection. Furthermore, data transmission and storage may not guarantee confidentiality or integrity. These issues hinder efficient decision-making in ocean-related industries.

[0406] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0407] In this invention, the server includes a measuring device for acquiring marine information in real time, a data transmission means for transmitting the acquired data to a remote storage device, and an analysis means using a machine learning model for performing information analysis on the remote storage device. This improves the accuracy of data collection and the speed of analysis, enabling rapid and accurate anomaly detection, and thus facilitating efficient decision-making in marine-related industrial fields.

[0408] "Oceanographic information" refers to data that indicates the state of the ocean, including information such as wave height, ocean current speed, and wind speed.

[0409] A "measuring device" is a sensor device installed to acquire physical information under specific environmental conditions, enabling high-precision data collection.

[0410] A "remote storage device" is a system for storing and managing data located on the internet, such as cloud storage.

[0411] "Data transmission means" refers to communication technology used to transmit acquired information to another system or storage.

[0412] A "machine learning model" is an algorithm or set of algorithms that allows a computer to recognize patterns based on experience and automatically learn to improve.

[0413] "Analysis means" refers to methods and techniques for analyzing collected data and extracting useful information.

[0414] "Spatial filtering technology" refers to methods for extracting specific signals or data with high precision, and mainly includes technologies such as beamforming.

[0415] An "abnormal information provision means" is a system or method for identifying patterns or conditions that are different from the normal ones from analyzed data and notifying the user.

[0416] This invention outlines an embodiment of a system for acquiring and analyzing marine information in real time and providing it to users. This system consists of a measuring device, a server, and a terminal.

[0417] First, the measuring device is installed in the ocean to continuously acquire oceanographic information such as wave height, ocean current speed, and wind speed. This sensor can collect data with high precision using spatial filtering technology, enabling accurate measurements.

[0418] Next, the server receives the data transmitted from the measuring device. The received data is stored in a remote storage device via a data transmission means. A secure communication protocol is used, and it is designed to maintain the confidentiality and integrity of the data. This remote storage device consists of cloud storage, which can securely store and manage large amounts of data.

[0419] The server performs analysis on data stored in the remote storage device using machine learning models. This analysis allows for evaluation of the dataset and identification of anomalies and specific patterns. Based on the analysis results, an anomaly information provision system generates timely information that users need. This analysis process is highly automated and processed quickly and efficiently.

[0420] Ultimately, the user receives the analysis results sent from the server on their device. The data is displayed through the device's application or interface, allowing the user to check the information in real time. For example, those involved in the fishing industry can use the analyzed oceanographic information to select fishing grounds and optimize navigation routes.

[0421] A concrete example of a prompt message would be something like, "Please explain the methods for analyzing data from marine sensors to detect anomalies." This allows users to efficiently obtain information and be supported in making appropriate decisions.

[0422] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0423] Step 1:

[0424] The server receives oceanographic information transmitted from the measuring devices. At this stage, various data such as wave height and wind speed are input. Since the received data is in its raw state, it needs to be formatted. Specifically, the server converts the data into a standardized format and temporarily stores it in memory. To maintain data consistency, data validation checks are also performed simultaneously.

[0425] Step 2:

[0426] The server transfers the received data to a remote storage device. Standardized data is used as input. This transfer process utilizes a secure communication protocol (e.g., TLS) to ensure data confidentiality and integrity. The data is stored in cloud storage and accessible in a database for extended periods. Specifically, the server checks for communication errors and retransmits the data as needed.

[0427] Step 3:

[0428] The server analyzes data stored in the cloud using machine learning models. Data from cloud storage is used as input. In this analysis, the AI ​​processes the dataset to detect anomalies and patterns. A generative AI model is used to clean the data and extract features, resulting in the generation of insights. Specifically, an anomaly detection algorithm is configured to issue an alert when a set threshold is exceeded.

[0429] Step 4:

[0430] The server sends the analyzed results to the user's device. The output is visualized data obtained as a result of the analysis. The data is processed into a format that is easy for the user to understand (e.g., graphs and charts). The server sends data to the device in real time and works to return a response quickly. Specifically, the server periodically pushes data notifications and provides an interactive dashboard.

[0431] Step 5:

[0432] Users make decisions based on the information displayed on their devices. Here, the visualized results become the input data, and the user's judgment becomes the output. For example, users can understand changes in ocean conditions and use this information to optimize fishing grounds or adjust navigation routes. In this way, users can develop effective strategies based on real-time data. Specifically, users analyze the provided data and decide on their next action.

[0433] (Application Example 1)

[0434] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0435] The present invention aims to improve the safety and efficiency of maritime vehicles by capturing environmental changes in the ocean in real time and providing optimal route guidance based on this information. Conventional systems have limited use of ocean data, making it difficult to make rapid, real-time decisions, and this invention seeks to solve that problem.

[0436] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0437] In this invention, the server includes a detection device for acquiring oceanographic information in real time, a transmission means for transmitting the acquired information to a large-scale data storage device, and an analysis means using machine learning technology to perform information analysis on the large-scale data storage device. This makes it possible to optimize the route of a moving object based on oceanographic change information.

[0438] A "detection device" is a device installed to acquire oceanographic information in real time.

[0439] A "large-scale data storage system" is a system that stores acquired information and makes it accessible when needed.

[0440] A "transmission means" is a means for transmitting information acquired from a detection device to a large-scale data storage device.

[0441] "Analysis methods using machine learning techniques" refer to methods that use machine learning algorithms to process and analyze acquired information.

[0442] A "display means" is an interface used to provide the analyzed results to the user.

[0443] A "route guidance system" is a means of suggesting the optimal route for a moving object based on analyzed information.

[0444] A signal conversion method using "focusing technology" is a technique that precisely processes information from a detection device and is a means for appropriately controlling the direction and intensity of the signal.

[0445] An "abnormal state detection means" is a means of detecting a state that is different from the normal state from the analyzed information and issuing a warning.

[0446] In implementing this system, the server transmits oceanographic information acquired in real time from detection devices installed in the ocean to a large-scale data storage device, where it performs analysis using machine learning techniques. Focusing technology is applied to the detection devices, enabling precise information acquisition. As a result, the server can quickly detect abnormal conditions based on ocean fluctuations and provide the results to the user's terminal as optimal navigation guidance.

[0447] The terminal presents the received analysis results to the user and supports real-time decision-making through the display mechanism. Based on the presented information, the user can select an efficient and safe route. This entire process utilizes analysis algorithms written in programming languages ​​such as Python.

[0448] For example, when operating a marine drone, if the server detects a sudden change in waves, that information is immediately notified to the terminal, and a new navigation route is suggested, allowing the user to respond quickly.

[0449] An example of a prompt message would be presented to the user as follows: "A rapid change in wave height has been detected. We will suggest an alternative route based on the current navigation conditions. Do you want to see the next steps?"

[0450] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0451] Step 1:

[0452] The server acquires real-time oceanographic information (wave height, current speed, wind speed, etc.) from sensing devices installed in the ocean. The input is raw data from the sensors. The data is sent to the server and processed using focusing technology to obtain accurate information.

[0453] Step 2:

[0454] The server transmits the acquired data to a large-scale data storage device. It receives processed ocean data as input and securely stores the data in cloud storage space as output. The data is organized and made quickly accessible when needed.

[0455] Step 3:

[0456] The server uses machine learning techniques to analyze data stored in the cloud. The input is organized ocean data in the cloud, and the output is the analysis results from anomaly detection and pattern recognition. Here, an AI model identifies outliers to detect anomalies.

[0457] Step 4:

[0458] The server detects abnormal conditions based on the analysis results and immediately generates a warning if an abnormality occurs. It uses the analysis results as input and generates a warning message as output. An example of a prompt message would be, "A rapid change in wave height has been detected. We will suggest an alternative route based on the current navigation conditions."

[0459] Step 5:

[0460] The terminal displays warning messages and analysis results received from the server to the user. It receives warning messages as input and presents them on the screen in a user-friendly format as output. Based on this, the user can immediately change their route.

[0461] Step 6:

[0462] The user selects the optimal route based on the information presented on the terminal and operates the mobile vehicle as needed. The terminal's display serves as input, and commands for the mobile vehicle are generated as output. Specifically, it allows for immediate adaptation to new navigation routes.

[0463] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0464] This invention combines a system for collecting and analyzing marine information in real time with an emotion engine that recognizes user emotions. The embodiments are described in detail below.

[0465] First, sensor devices installed in the ocean continuously collect data such as ocean current speed, wave height, and wind speed. This data is then processed using beamforming technology to improve its accuracy. A server receives the data transmitted from the sensor devices, processes it, and then securely sends it to cloud storage.

[0466] On the cloud, the server uses AI to analyze the data. If an anomaly is detected in the analyzed data, the server immediately generates an alert. The analysis results are then sent to the user's device. The user can receive the information via a smartphone, tablet, or other device and check the ocean conditions.

[0467] Furthermore, this invention incorporates an emotion engine. The emotion engine recognizes emotions on the user's device based on the user's voice, facial expressions, and input feedback. For example, if the user is feeling anxious, the interface can be modified to adjust the details of the anomaly detection alert and the suggested actions, thereby providing a sense of security.

[0468] As a concrete example, consider a scenario where a fisherman receives an alert about an approaching storm. The emotion engine analyzes the user's reaction to understand their emotions and then calmly suggests a safe evacuation route. It also provides an option to view more detailed weather information. This allows the user to make a more confident and appropriate decision.

[0469] Thus, the present invention provides a system that improves the user experience and supports faster and more effective decision-making by introducing emotion recognition into the provision of marine information.

[0470] The following describes the processing flow.

[0471] Step 1:

[0472] The server receives data in real time from sensor devices installed in the ocean. The data includes ocean current speed, wave height, wind speed, etc., and beamforming technology is used to improve accuracy.

[0473] Step 2:

[0474] The server formats the received data appropriately and sends it to cloud storage. During this process, the data is transmitted via a secure communication protocol, ensuring confidentiality.

[0475] Step 3:

[0476] A server in the cloud uses AI to analyze the data. The AI ​​model has already learned normal data patterns and uses them to detect anomalies in the data.

[0477] Step 4:

[0478] If the server detects an anomaly based on the analysis results, it immediately generates an alert. The generated alert is immediately sent to the user's terminal.

[0479] Step 5:

[0480] The device receives an alert and displays a warning to the user. More detailed analysis and suggested countermeasures are also displayed on the device.

[0481] Step 6:

[0482] The device's built-in emotion engine captures the user's voice and facial expressions and analyzes their current emotional state. The interface dynamically adjusts based on the analysis results, providing information that matches the user's emotions.

[0483] Step 7:

[0484] The user makes decisions based on information and suggestions received through their device. The choices presented by the emotion engine are designed to enhance the user's sense of security and UX, and change dynamically.

[0485] (Example 2)

[0486] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0487] Conventional marine information gathering systems provide users with only simple data analysis results, failing to consider the user's emotional state. Therefore, improving the user experience in information utilization is a challenge. In particular, to avoid situations where users feel anxious or confused during emergencies, it is necessary to adjust the content and method of information provision according to their emotions.

[0488] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0489] In this invention, the server includes a sensing device for acquiring ocean information in real time, a communication means for transmitting the acquired ocean data to a storage device, and an analysis means using a computational model for performing data analysis on an information infrastructure. This makes it possible to recognize the user's emotions and provide effective information by adjusting the interface according to those emotions.

[0490] A "sensing device" is a device installed to acquire oceanographic information in real time, and its role is to collect data such as ocean current speed, wave height, and wind speed.

[0491] "Collective storage" refers to a storage method for securely storing acquired marine data, and usually refers to a cloud environment such as a data center.

[0492] "Communication means" refers to means for transmitting data collected by a sensing device to a storage device, and includes technologies for transferring data via a network.

[0493] "Information infrastructure" refers to a set of infrastructure for processing and storing data, including cloud platforms that enable data analysis.

[0494] A "computational model" is an artificial intelligence model or algorithm used for data analysis, enabling the analysis of acquired data to gain useful insights.

[0495] "Analysis means" refers to a series of processing means for analyzing acquired data using a computational model, and is used to achieve anomaly detection and information extraction.

[0496] "Emotion recognition means" refers to means for recognizing the user's emotional state and adjusting the interface based on the information obtained, and includes technologies for analyzing the user's voice, facial expressions, and feedback.

[0497] This invention is a system that acquires and analyzes marine information in real time and provides information while taking into account the user's emotions. Specific embodiments are shown below.

[0498] The server receives information from sensing devices installed in the ocean. The sensing devices acquire data such as ocean current speed, wave height, and wind speed, and transmit the data to the server using wireless communication technology. The server precisely processes the received data using directional control technology, formats the data, and transmits it to integrated storage. Integrated storage uses a data center in the cloud, and the data is stored securely using the SSL / TLS protocol.

[0499] On the cloud, the server uses computational models to perform data analysis. Generative AI models function as analytical tools, for example, to detect and predict anomalies in the ocean. These models are based on deep learning algorithms and can be continuously learned and improved. When an anomaly is detected, the server immediately generates an alert and sends that information to the user's terminal.

[0500] The user's device is equipped with emotion recognition capabilities. The device analyzes the user's emotions based on their voice, facial expressions, and input data, and provides an optimal interface. For example, if a fisherman receives an alert about an approaching storm, the emotion engine detects the user's anxiety and calmly suggests a safe evacuation route, offering the option to view detailed weather information. In this way, the user can receive the necessary information while feeling reassured.

[0501] An example of a prompt message could be: "A storm is approaching. Please use the emotion engine to suggest measures to alleviate the user's anxiety." This invention prioritizes user experience and aims to enhance the effectiveness of information delivery.

[0502] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0503] Step 1:

[0504] The server receives information from sensing devices installed in the ocean. Input data includes ocean current speed, wave height, and wind speed. The data is processed using directional control technology to output precise data with minimal noise. This processing improves the accuracy and reliability of the data.

[0505] Step 2:

[0506] The server transmits the formatted data to the integrated storage. The input is precise data processed using directional control technology. The data is securely stored in a cloud data center, with security ensured by the SSL / TLS protocol. This ensures the data is securely protected and ready for subsequent analysis.

[0507] Step 3:

[0508] The server retrieves data stored in integrated memory and performs analysis using a generative AI model. The input is high-resolution data stored in the cloud. Based on pre-trained algorithms, the generative AI model performs anomaly detection and predicts ocean conditions, outputting analysis results. This analysis extracts important ocean information.

[0509] Step 4:

[0510] The server generates alerts as needed based on the analysis results and sends them to the user's device. The input is the analysis results of the generating AI model. The specific alert content is formatted as a text message and pushed to the user's device via the network. This allows the user to immediately understand the situation.

[0511] Step 5:

[0512] The device analyzes the user's emotions using emotion recognition based on received alerts. Inputs include the user's voice, facial expressions, and input feedback. The device compares this data, optimizes the interface according to the emotional state, and outputs a new interface. For example, a user experiencing anxiety will be shown calming suggestions and detailed information. This improves the user experience and enables faster decision-making.

[0513] (Application Example 2)

[0514] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0515] In today's transportation environment, there is a demand for timely and precise acquisition and analysis of marine information. However, there is a challenge in providing optimal feedback while also considering the user's emotions. As a result, users may be unable to take appropriate measures, and their psychological burden may increase. This invention aims to solve these problems by providing feedback that is tailored to the user's emotions, in addition to real-time data acquisition and analysis.

[0516] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0517] In this invention, the server includes a detection device for acquiring marine information in real time, a communication means for transmitting the acquired marine data to a large-scale data service, an analysis means using artificial intelligence to perform data analysis in a large-scale data processing environment, and an emotion analysis means for recognizing the user's emotions and providing optimal feedback. This makes it possible to provide appropriate information based on the user's emotions while analyzing marine environment data.

[0518] A "detection device" is a device installed to measure changes in the marine environment in real time, and it acquires data such as ocean current speed, wave height, and wind speed.

[0519] A "large-scale data service" is a cloud-based information system used to centrally manage and analyze collected marine data.

[0520] "Communication means" refers to digital communication technology used to transmit data acquired from detection devices to large-scale data services.

[0521] A "large-scale data processing environment" is a platform that provides computing resources for rapidly and efficiently analyzing large amounts of data on the cloud.

[0522] "Analysis methods using artificial intelligence" refers to machine learning techniques that automatically analyze data and use the results to evaluate the state of the ocean.

[0523] "Output means" refers to a function for displaying or notifying users of the analyzed data on their information terminals.

[0524] "Emotional analysis means" refers to technology that analyzes the user's voice and facial expressions, recognizes their emotional state, and generates optimal feedback.

[0525] "Adaptive measures" refer to control technologies that dynamically adjust the information and feedback provided based on analyzed data and user emotions.

[0526] The system for realizing this invention has a configuration that acquires ocean information in real time and provides information tailored to the user's emotions. A specific embodiment of this system is shown below.

[0527] The server first receives data from detection devices installed in the ocean. This data includes information such as ocean current speed, wave height, and wind speed, and is collected through large-scale data services. Real-time information acquisition is possible because the data is reliably transmitted via communication means.

[0528] This data is processed using artificial intelligence-based analysis methods in a large-scale data processing environment. Specifically, machine learning algorithms are used, and when an anomaly is detected, the user's terminal is quickly notified. This allows the user to immediately understand the situation and take appropriate action.

[0529] Furthermore, the device is equipped with an emotion analysis system. Using the user's voice and facial expression data, an AI model is used to perform emotion analysis. Based on this analysis, an adaptive system adjusts the feedback content to provide information best suited to the user's emotions.

[0530] As a concrete example, consider a scenario where information about a sudden weather change is received during a long-distance voyage. If the system determines, through emotion analysis, that the user is feeling anxious, the voice assistant will deliver safety information in a calm tone and, if necessary, play soothing music to reassure the user.

[0531] An example of a prompt message would be, "I would like to feel more at ease during this long voyage. Please provide safety information regarding weather changes and relaxation methods." In this way, it is possible to provide services that take the user's feelings into consideration.

[0532] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0533] Step 1:

[0534] The server receives data from detection devices installed in the ocean. The input consists of real-time data such as ocean current speed, wave height, and wind speed. This data is transmitted to a large-scale data service via communication channels and stored in cloud storage. The output is data sent to the cloud in a structured format.

[0535] Step 2:

[0536] The server analyzes data stored in the cloud using machine learning algorithms in a large-scale data processing environment. The input is enterprise-level marine data. Anomaly detection algorithms are applied as data processing to analyze patterns that deviate from the norm. The output is the analysis results, including information on detected anomalies.

[0537] Step 3:

[0538] The terminal receives analysis results sent from the server and notifies the user. Input consists of analyzed ocean data and anomaly information sent from the server. Output from the terminal includes notification messages and information provided through a visual interface for the user.

[0539] Step 4:

[0540] The device analyzes the user's emotions using emotion analysis tools. Input consists of the user's voice and facial expression data. A generative AI model is used to process the data and determine the user's emotional state. Output is information regarding the user's emotional state.

[0541] Step 5:

[0542] Based on the user's emotion analysis results, the device adjusts the feedback content through adaptive mechanisms. Input consists of the emotion analysis results and notification information based on abnormal data. Specifically, it generates feedback appropriate to the received data and presents content and suggestions tailored to the user's psychological state. The output is the adjusted feedback, providing the user with a sense of relaxation and security.

[0543] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0544] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0545] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0546] [Fourth Embodiment]

[0547] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0548] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0549] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0550] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0551] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0552] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0553] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0554] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0555] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0556] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0557] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0558] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0559] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0560] This invention is a system for collecting and analyzing marine information in real time, and its embodiments are described in detail below.

[0561] First, sensor devices installed in the ocean continuously acquire data such as wave height, ocean current speed, and wind speed. These sensor devices can collect highly accurate data using beamforming technology.

[0562] Next, the server receives the data transmitted from the sensor device. The received data is converted to an appropriate format and securely sent to cloud storage. Secure communication protocols are used to maintain the confidentiality and integrity of the data.

[0563] The cloud-based server performs advanced AI-powered analysis on the received data. In this analysis process, the AI ​​evaluates the dataset and identifies anomalies and patterns. If necessary, the server immediately issues an alert if an anomaly is detected.

[0564] The analysis results are further processed and sent to the user's device. Users can review the provided data through an application or interface and make decisions based on the information obtained. For example, those involved in the fishing industry can understand the real-time changing ocean conditions and use this information to select fishing grounds and optimize navigation routes.

[0565] This format allows various maritime-related business sectors to utilize highly accurate data in real time and respond quickly. Through integration with other systems, the aim is to promote the commercial use of data and contribute to enhancing Japan's position as a maritime nation.

[0566] The following describes the processing flow.

[0567] Step 1:

[0568] The server receives raw data from the sensor device. The sensor device continuously collects oceanographic information and transmits it to the server. The server then formats the signal in preparation for further processing.

[0569] Step 2:

[0570] The server applies beamforming technology to enhance signals in specific directions and suppress noise. This process improves data accuracy and enhances the quality of analysis.

[0571] Step 3:

[0572] The server converts the processed data into a standard format such as JSON and uploads it to cloud storage. During this process, a secure communication protocol is used to ensure data security.

[0573] Step 4:

[0574] A server in the cloud uses AI to analyze the data. The AI ​​analyzes the data and applies anomaly detection models to identify data points that deviate from normal patterns.

[0575] Step 5:

[0576] The server compiles the analysis results and immediately generates an alert if an anomaly is detected. This alert is sent to the relevant users to prompt a quick response.

[0577] Step 6:

[0578] The analyzed data is sent to the user's device. Through the application, the user can view this data and monitor changes in the marine environment in real time.

[0579] (Example 1)

[0580] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0581] Modern ocean observation systems require real-time data acquisition and analysis, but conventional systems often suffer from insufficient data accuracy and analysis speed, resulting in delayed anomaly detection. Furthermore, data transmission and storage may not guarantee confidentiality or integrity. These issues hinder efficient decision-making in ocean-related industries.

[0582] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0583] In this invention, the server includes a measuring device for acquiring marine information in real time, a data transmission means for transmitting the acquired data to a remote storage device, and an analysis means using a machine learning model for performing information analysis on the remote storage device. This improves the accuracy of data collection and the speed of analysis, enabling rapid and accurate anomaly detection, and thus facilitating efficient decision-making in marine-related industrial fields.

[0584] "Oceanographic information" refers to data that indicates the state of the ocean, including information such as wave height, ocean current speed, and wind speed.

[0585] A "measuring device" is a sensor device installed to acquire physical information under specific environmental conditions, enabling high-precision data collection.

[0586] A "remote storage device" is a system for storing and managing data located on the internet, such as cloud storage.

[0587] "Data transmission means" refers to communication technology used to transmit acquired information to another system or storage.

[0588] A "machine learning model" is an algorithm or set of algorithms that allows a computer to recognize patterns based on experience and automatically learn to improve.

[0589] "Analysis means" refers to methods and techniques for analyzing collected data and extracting useful information.

[0590] "Spatial filtering technology" refers to methods for extracting specific signals or data with high precision, and mainly includes technologies such as beamforming.

[0591] An "abnormal information provision means" is a system or method for identifying patterns or conditions that are different from the normal ones from analyzed data and notifying the user.

[0592] This invention outlines an embodiment of a system for acquiring and analyzing marine information in real time and providing it to users. This system consists of a measuring device, a server, and a terminal.

[0593] First, the measuring device is installed in the ocean to continuously acquire oceanographic information such as wave height, ocean current speed, and wind speed. This sensor can collect data with high precision using spatial filtering technology, enabling accurate measurements.

[0594] Next, the server receives the data transmitted from the measuring device. The received data is stored in a remote storage device via a data transmission means. A secure communication protocol is used, and it is designed to maintain the confidentiality and integrity of the data. This remote storage device consists of cloud storage, which can securely store and manage large amounts of data.

[0595] The server performs analysis on data stored in the remote storage device using machine learning models. This analysis allows for evaluation of the dataset and identification of anomalies and specific patterns. Based on the analysis results, an anomaly information provision system generates timely information that users need. This analysis process is highly automated and processed quickly and efficiently.

[0596] Ultimately, the user receives the analysis results sent from the server on their device. The data is displayed through the device's application or interface, allowing the user to check the information in real time. For example, those involved in the fishing industry can use the analyzed oceanographic information to select fishing grounds and optimize navigation routes.

[0597] A concrete example of a prompt message would be something like, "Please explain the methods for analyzing data from marine sensors to detect anomalies." This allows users to efficiently obtain information and be supported in making appropriate decisions.

[0598] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0599] Step 1:

[0600] The server receives oceanographic information transmitted from the measuring devices. At this stage, various data such as wave height and wind speed are input. Since the received data is in its raw state, it needs to be formatted. Specifically, the server converts the data into a standardized format and temporarily stores it in memory. To maintain data consistency, data validation checks are also performed simultaneously.

[0601] Step 2:

[0602] The server transfers the received data to a remote storage device. Standardized data is used as input. This transfer process utilizes a secure communication protocol (e.g., TLS) to ensure data confidentiality and integrity. The data is stored in cloud storage and accessible in a database for extended periods. Specifically, the server checks for communication errors and retransmits the data as needed.

[0603] Step 3:

[0604] The server analyzes data stored in the cloud using machine learning models. Data from cloud storage is used as input. In this analysis, the AI ​​processes the dataset to detect anomalies and patterns. A generative AI model is used to clean the data and extract features, resulting in the generation of insights. Specifically, an anomaly detection algorithm is configured to issue an alert when a set threshold is exceeded.

[0605] Step 4:

[0606] The server sends the analyzed results to the user's device. The output is visualized data obtained as a result of the analysis. The data is processed into a format that is easy for the user to understand (e.g., graphs and charts). The server sends data to the device in real time and works to return a response quickly. Specifically, the server periodically pushes data notifications and provides an interactive dashboard.

[0607] Step 5:

[0608] Users make decisions based on the information displayed on their devices. Here, the visualized results become the input data, and the user's judgment becomes the output. For example, users can understand changes in ocean conditions and use this information to optimize fishing grounds or adjust navigation routes. In this way, users can develop effective strategies based on real-time data. Specifically, users analyze the provided data and decide on their next action.

[0609] (Application Example 1)

[0610] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0611] The present invention aims to improve the safety and efficiency of maritime vehicles by capturing environmental changes in the ocean in real time and providing optimal route guidance based on this information. Conventional systems have limited use of ocean data, making it difficult to make rapid, real-time decisions, and this invention seeks to solve that problem.

[0612] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0613] In this invention, the server includes a detection device for acquiring oceanographic information in real time, a transmission means for transmitting the acquired information to a large-scale data storage device, and an analysis means using machine learning technology to perform information analysis on the large-scale data storage device. This makes it possible to optimize the route of a moving object based on oceanographic change information.

[0614] A "detection device" is a device installed to acquire oceanographic information in real time.

[0615] A "large-scale data storage system" is a system that stores acquired information and makes it accessible when needed.

[0616] A "transmission means" is a means for transmitting information acquired from a detection device to a large-scale data storage device.

[0617] "Analysis methods using machine learning techniques" refer to methods that use machine learning algorithms to process and analyze acquired information.

[0618] A "display means" is an interface used to provide the analyzed results to the user.

[0619] A "route guidance system" is a means of suggesting the optimal route for a moving object based on analyzed information.

[0620] A signal conversion method using "focusing technology" is a technique that precisely processes information from a detection device and is a means for appropriately controlling the direction and intensity of the signal.

[0621] An "abnormal state detection means" is a means of detecting a state that is different from the normal state from the analyzed information and issuing a warning.

[0622] In implementing this system, the server transmits oceanographic information acquired in real time from detection devices installed in the ocean to a large-scale data storage device, where it performs analysis using machine learning techniques. Focusing technology is applied to the detection devices, enabling precise information acquisition. As a result, the server can quickly detect abnormal conditions based on ocean fluctuations and provide the results to the user's terminal as optimal navigation guidance.

[0623] The terminal presents the received analysis results to the user and supports real-time decision-making through the display mechanism. Based on the presented information, the user can select an efficient and safe route. This entire process utilizes analysis algorithms written in programming languages ​​such as Python.

[0624] For example, when operating a marine drone, if the server detects a sudden change in waves, that information is immediately notified to the terminal, and a new navigation route is suggested, allowing the user to respond quickly.

[0625] An example of a prompt message would be presented to the user as follows: "A rapid change in wave height has been detected. We will suggest an alternative route based on the current navigation conditions. Do you want to see the next steps?"

[0626] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0627] Step 1:

[0628] The server acquires real-time oceanographic information (wave height, current speed, wind speed, etc.) from sensing devices installed in the ocean. The input is raw data from the sensors. The data is sent to the server and processed using focusing technology to obtain accurate information.

[0629] Step 2:

[0630] The server transmits the acquired data to a large-scale data storage device. It receives processed ocean data as input and securely stores the data in cloud storage space as output. The data is organized and made quickly accessible when needed.

[0631] Step 3:

[0632] The server uses machine learning techniques to analyze data stored in the cloud. The input is organized ocean data in the cloud, and the output is the analysis results from anomaly detection and pattern recognition. Here, an AI model identifies outliers to detect anomalies.

[0633] Step 4:

[0634] The server detects abnormal conditions based on the analysis results and immediately generates a warning if an abnormality occurs. It uses the analysis results as input and generates a warning message as output. An example of a prompt message would be, "A rapid change in wave height has been detected. We will suggest an alternative route based on the current navigation conditions."

[0635] Step 5:

[0636] The terminal displays warning messages and analysis results received from the server to the user. It receives warning messages as input and presents them on the screen in a user-friendly format as output. Based on this, the user can immediately change their route.

[0637] Step 6:

[0638] The user selects the optimal route based on the information presented on the terminal and operates the mobile vehicle as needed. The terminal's display serves as input, and commands for the mobile vehicle are generated as output. Specifically, it allows for immediate adaptation to new navigation routes.

[0639] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0640] This invention combines a system for collecting and analyzing marine information in real time with an emotion engine that recognizes user emotions. The embodiments are described in detail below.

[0641] First, sensor devices installed in the ocean continuously collect data such as ocean current speed, wave height, and wind speed. This data is then processed using beamforming technology to improve its accuracy. A server receives the data transmitted from the sensor devices, processes it, and then securely sends it to cloud storage.

[0642] On the cloud, the server uses AI to analyze the data. If an anomaly is detected in the analyzed data, the server immediately generates an alert. The analysis results are then sent to the user's device. The user can receive the information via a smartphone, tablet, or other device and check the ocean conditions.

[0643] Furthermore, this invention incorporates an emotion engine. The emotion engine recognizes emotions on the user's device based on the user's voice, facial expressions, and input feedback. For example, if the user is feeling anxious, the interface can be modified to adjust the details of the anomaly detection alert and the suggested actions, thereby providing a sense of security.

[0644] As a concrete example, consider a scenario where a fisherman receives an alert about an approaching storm. The emotion engine analyzes the user's reaction to understand their emotions and then calmly suggests a safe evacuation route. It also provides an option to view more detailed weather information. This allows the user to make a more confident and appropriate decision.

[0645] Thus, the present invention provides a system that improves the user experience and supports faster and more effective decision-making by introducing emotion recognition into the provision of marine information.

[0646] The following describes the processing flow.

[0647] Step 1:

[0648] The server receives data in real time from sensor devices installed in the ocean. The data includes ocean current speed, wave height, wind speed, etc., and beamforming technology is used to improve accuracy.

[0649] Step 2:

[0650] The server formats the received data appropriately and sends it to cloud storage. During this process, the data is transmitted via a secure communication protocol, ensuring confidentiality.

[0651] Step 3:

[0652] A server in the cloud uses AI to analyze the data. The AI ​​model has already learned normal data patterns and uses them to detect anomalies in the data.

[0653] Step 4:

[0654] If the server detects an anomaly based on the analysis results, it immediately generates an alert. The generated alert is immediately sent to the user's terminal.

[0655] Step 5:

[0656] The device receives an alert and displays a warning to the user. More detailed analysis and suggested countermeasures are also displayed on the device.

[0657] Step 6:

[0658] The device's built-in emotion engine captures the user's voice and facial expressions and analyzes their current emotional state. The interface dynamically adjusts based on the analysis results, providing information that matches the user's emotions.

[0659] Step 7:

[0660] The user makes decisions based on information and suggestions received through their device. The choices presented by the emotion engine are designed to enhance the user's sense of security and UX, and change dynamically.

[0661] (Example 2)

[0662] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0663] Conventional marine information gathering systems provide users with only simple data analysis results, failing to consider the user's emotional state. Therefore, improving the user experience in information utilization is a challenge. In particular, to avoid situations where users feel anxious or confused during emergencies, it is necessary to adjust the content and method of information provision according to their emotions.

[0664] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0665] In this invention, the server includes a sensing device for acquiring ocean information in real time, a communication means for transmitting the acquired ocean data to a storage device, and an analysis means using a computational model for performing data analysis on an information infrastructure. This makes it possible to recognize the user's emotions and provide effective information by adjusting the interface according to those emotions.

[0666] A "sensing device" is a device installed to acquire oceanographic information in real time, and its role is to collect data such as ocean current speed, wave height, and wind speed.

[0667] "Collective storage" refers to a storage method for securely storing acquired marine data, and usually refers to a cloud environment such as a data center.

[0668] "Communication means" refers to means for transmitting data collected by a sensing device to a storage device, and includes technologies for transferring data via a network.

[0669] "Information infrastructure" refers to a set of infrastructure for processing and storing data, including cloud platforms that enable data analysis.

[0670] A "computational model" is an artificial intelligence model or algorithm used for data analysis, enabling the analysis of acquired data to gain useful insights.

[0671] "Analysis means" refers to a series of processing means for analyzing acquired data using a computational model, and is used to achieve anomaly detection and information extraction.

[0672] "Emotion recognition means" refers to means for recognizing the user's emotional state and adjusting the interface based on the information obtained, and includes technologies for analyzing the user's voice, facial expressions, and feedback.

[0673] This invention is a system that acquires and analyzes marine information in real time and provides information while taking into account the user's emotions. Specific embodiments are shown below.

[0674] The server receives information from sensing devices installed in the ocean. The sensing devices acquire data such as ocean current speed, wave height, and wind speed, and transmit the data to the server using wireless communication technology. The server precisely processes the received data using directional control technology, formats the data, and transmits it to integrated storage. Integrated storage uses a data center in the cloud, and the data is stored securely using the SSL / TLS protocol.

[0675] On the cloud, the server uses computational models to perform data analysis. Generative AI models function as analytical tools, for example, to detect and predict anomalies in the ocean. These models are based on deep learning algorithms and can be continuously learned and improved. When an anomaly is detected, the server immediately generates an alert and sends that information to the user's terminal.

[0676] The user's device is equipped with emotion recognition capabilities. The device analyzes the user's emotions based on their voice, facial expressions, and input data, and provides an optimal interface. For example, if a fisherman receives an alert about an approaching storm, the emotion engine detects the user's anxiety and calmly suggests a safe evacuation route, offering the option to view detailed weather information. In this way, the user can receive the necessary information while feeling reassured.

[0677] An example of a prompt message could be: "A storm is approaching. Please use the emotion engine to suggest measures to alleviate the user's anxiety." This invention prioritizes user experience and aims to enhance the effectiveness of information delivery.

[0678] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0679] Step 1:

[0680] The server receives information from sensing devices installed in the ocean. Input data includes ocean current speed, wave height, and wind speed. The data is processed using directional control technology to output precise data with minimal noise. This processing improves the accuracy and reliability of the data.

[0681] Step 2:

[0682] The server transmits the formatted data to the integrated storage. The input is precise data processed using directional control technology. The data is securely stored in a cloud data center, with security ensured by the SSL / TLS protocol. This ensures the data is securely protected and ready for subsequent analysis.

[0683] Step 3:

[0684] The server retrieves data stored in integrated memory and performs analysis using a generative AI model. The input is high-resolution data stored in the cloud. Based on pre-trained algorithms, the generative AI model performs anomaly detection and predicts ocean conditions, outputting analysis results. This analysis extracts important ocean information.

[0685] Step 4:

[0686] The server generates alerts as needed based on the analysis results and sends them to the user's device. The input is the analysis results of the generating AI model. The specific alert content is formatted as a text message and pushed to the user's device via the network. This allows the user to immediately understand the situation.

[0687] Step 5:

[0688] The device analyzes the user's emotions using emotion recognition based on received alerts. Inputs include the user's voice, facial expressions, and input feedback. The device compares this data, optimizes the interface according to the emotional state, and outputs a new interface. For example, a user experiencing anxiety will be shown calming suggestions and detailed information. This improves the user experience and enables faster decision-making.

[0689] (Application Example 2)

[0690] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0691] In today's transportation environment, there is a demand for timely and precise acquisition and analysis of marine information. However, there is a challenge in providing optimal feedback while also considering the user's emotions. As a result, users may be unable to take appropriate measures, and their psychological burden may increase. This invention aims to solve these problems by providing feedback that is tailored to the user's emotions, in addition to real-time data acquisition and analysis.

[0692] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0693] In this invention, the server includes a detection device for acquiring marine information in real time, a communication means for transmitting the acquired marine data to a large-scale data service, an analysis means using artificial intelligence to perform data analysis in a large-scale data processing environment, and an emotion analysis means for recognizing the user's emotions and providing optimal feedback. This makes it possible to provide appropriate information based on the user's emotions while analyzing marine environment data.

[0694] A "detection device" is a device installed to measure changes in the marine environment in real time, and it acquires data such as ocean current speed, wave height, and wind speed.

[0695] A "large-scale data service" is a cloud-based information system used to centrally manage and analyze collected marine data.

[0696] "Communication means" refers to digital communication technology used to transmit data acquired from detection devices to large-scale data services.

[0697] A "large-scale data processing environment" is a platform that provides computing resources for rapidly and efficiently analyzing large amounts of data on the cloud.

[0698] "Analysis methods using artificial intelligence" refers to machine learning techniques that automatically analyze data and use the results to evaluate the state of the ocean.

[0699] "Output means" refers to a function for displaying or notifying users of the analyzed data on their information terminals.

[0700] "Emotional analysis means" refers to technology that analyzes the user's voice and facial expressions, recognizes their emotional state, and generates optimal feedback.

[0701] "Adaptive measures" refer to control technologies that dynamically adjust the information and feedback provided based on analyzed data and user emotions.

[0702] The system for realizing this invention has a configuration that acquires ocean information in real time and provides information tailored to the user's emotions. A specific embodiment of this system is shown below.

[0703] The server first receives data from detection devices installed in the ocean. This data includes information such as ocean current speed, wave height, and wind speed, and is collected through large-scale data services. Real-time information acquisition is possible because the data is reliably transmitted via communication means.

[0704] This data is processed using artificial intelligence-based analysis methods in a large-scale data processing environment. Specifically, machine learning algorithms are used, and when an anomaly is detected, the user's terminal is quickly notified. This allows the user to immediately understand the situation and take appropriate action.

[0705] Furthermore, the device is equipped with an emotion analysis system. Using the user's voice and facial expression data, an AI model is used to perform emotion analysis. Based on this analysis, an adaptive system adjusts the feedback content to provide information best suited to the user's emotions.

[0706] As a concrete example, consider a scenario where information about a sudden weather change is received during a long-distance voyage. If the system determines, through emotion analysis, that the user is feeling anxious, the voice assistant will deliver safety information in a calm tone and, if necessary, play soothing music to reassure the user.

[0707] An example of a prompt message would be, "I would like to feel more at ease during this long voyage. Please provide safety information regarding weather changes and relaxation methods." In this way, it is possible to provide services that take the user's feelings into consideration.

[0708] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0709] Step 1:

[0710] The server receives data from detection devices installed in the ocean. The input consists of real-time data such as ocean current speed, wave height, and wind speed. This data is transmitted to a large-scale data service via communication channels and stored in cloud storage. The output is data sent to the cloud in a structured format.

[0711] Step 2:

[0712] The server analyzes data stored in the cloud using machine learning algorithms in a large-scale data processing environment. The input is enterprise-level marine data. Anomaly detection algorithms are applied as data processing to analyze patterns that deviate from the norm. The output is the analysis results, including information on detected anomalies.

[0713] Step 3:

[0714] The terminal receives analysis results sent from the server and notifies the user. Input consists of analyzed ocean data and anomaly information sent from the server. Output from the terminal includes notification messages and information provided through a visual interface for the user.

[0715] Step 4:

[0716] The device analyzes the user's emotions using emotion analysis tools. Input consists of the user's voice and facial expression data. A generative AI model is used to process the data and determine the user's emotional state. Output is information regarding the user's emotional state.

[0717] Step 5:

[0718] Based on the user's emotion analysis results, the device adjusts the feedback content through adaptive mechanisms. Input consists of the emotion analysis results and notification information based on abnormal data. Specifically, it generates feedback appropriate to the received data and presents content and suggestions tailored to the user's psychological state. The output is the adjusted feedback, providing the user with a sense of relaxation and security.

[0719] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0720] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0721] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0722] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0723] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0724] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0725] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0726] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0727] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0728] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0729] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0730] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0731] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0732] 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.

[0733] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0734] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0735] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0736] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0737] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0738] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0739] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0740] The following is further disclosed regarding the embodiments described above.

[0741] (Claim 1)

[0742] A sensor device for acquiring oceanographic information in real time,

[0743] A communication method for transmitting acquired marine data to cloud storage,

[0744] An analysis method using artificial intelligence to perform data analysis on the cloud,

[0745] An output means that provides the processed analysis results to the user's terminal,

[0746] A system that includes this.

[0747] (Claim 2)

[0748] The system according to claim 1, comprising signal processing means using beamforming technology for precisely processing data from a sensor device.

[0749] (Claim 3)

[0750] The system according to claim 1, comprising an anomaly detection means for detecting anomalies in the ocean from analyzed data.

[0751] "Example 1"

[0752] (Claim 1)

[0753] A measuring device for acquiring oceanographic information in real time,

[0754] A data transmission means for transmitting acquired data to a remote storage device,

[0755] An analysis method using a machine learning model to perform information analysis on a remote storage device,

[0756] An information output means for providing analysis results to a terminal,

[0757] A system that includes this.

[0758] (Claim 2)

[0759] The system according to claim 1, comprising signal processing means using spatial filtering technology for processing data from a measuring device with high precision.

[0760] (Claim 3)

[0761] The system according to claim 1, further comprising means for providing anomaly information to detect anomalies in the ocean from analyzed data.

[0762] "Application Example 1"

[0763] (Claim 1)

[0764] A detection device for acquiring oceanographic information in real time,

[0765] A means for transmitting acquired information to a large-scale data storage device,

[0766] An analytical method using machine learning technology to perform information analysis on a large-scale data storage device,

[0767] A display means that provides the processed analysis results to the user's computer,

[0768] A route guidance means that optimizes the flight path of a moving object based on the analyzed information,

[0769] A system that includes this.

[0770] (Claim 2)

[0771] The system according to claim 1, comprising signal conversion means using focusing technology for accurately processing information from a detection device.

[0772] (Claim 3)

[0773] The system according to claim 1, comprising an anomaly detection means for detecting anomalies in the ocean from analyzed information.

[0774] "Example 2 of combining an emotion engine"

[0775] (Claim 1)

[0776] A sensing device for acquiring oceanographic information in real time,

[0777] A communication means for transmitting acquired ocean data to a storage device,

[0778] An analysis method using a computational model for performing data analysis on an information infrastructure,

[0779] An output means that provides the processed analysis results to the user's mobile device,

[0780] An emotion recognition means that recognizes emotions and adjusts the interface,

[0781] A system that includes this.

[0782] (Claim 2)

[0783] The system according to claim 1, comprising signal processing means using directional control technology for precisely processing data from a sensing device.

[0784] (Claim 3)

[0785] The system according to claim 1, comprising an anomaly detection means for detecting anomalies in the ocean from analyzed data.

[0786] "Application example 2 when combining with an emotional engine"

[0787] (Claim 1)

[0788] A detection device for acquiring oceanographic information in real time,

[0789] A communication method for transmitting acquired ocean data to a large-scale data service,

[0790] An analysis method using artificial intelligence for data analysis in a large-scale data processing environment,

[0791] An output means for providing the processed analysis results to an information terminal,

[0792] A means of sentiment analysis to recognize the user's emotions and provide optimal feedback,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, comprising signal processing means using directional technology for precisely processing data from a detection device.

[0796] (Claim 3)

[0797] The system according to claim 1, comprising adaptive means for detecting anomalies in the ocean from analyzed data and adjusting the output according to the user's emotions. [Explanation of Symbols]

[0798] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A sensor device for acquiring oceanographic information in real time, A communication method for transmitting acquired marine data to cloud storage, An analysis method using artificial intelligence to perform data analysis on the cloud, An output means that provides the processed analysis results to the user's terminal, A system that includes this.

2. The system according to claim 1, comprising signal processing means using beamforming technology for precisely processing data from a sensor device.

3. The system according to claim 1, further comprising an anomaly detection means for detecting anomalies in the ocean from analyzed data.

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