system

Abstract

We provide the system. [Solution] Multiple mobile devices that collect ocean data, A communication means for receiving data obtained from the aforementioned mobile body in real time, A data analysis means for analyzing the received data, Information provision means for presenting the results of the aforementioned analysis, A marine management system including

Description

Technical Field

[0001] The technology of this 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] In conventional ocean management systems, the collection and analysis of ocean data were inefficient and lacked real-time capabilities. As a result, it was difficult to quickly detect abnormal situations and accurately predict environmental changes. Also, in data visualization, there was a lack of a form that users could intuitively understand. For this reason, the sustainable management of the ocean environment has not been fully achieved.

Means for Solving the Problems

[0005] This invention provides an ocean management system comprising multiple mobile units for collecting ocean data, communication means for receiving the data obtained from these units in real time, and data analysis means for analyzing the received data. By including an information provision means that predicts environmental changes using a generated AI model based on the collected data and intuitively presents the analysis results, the invention improves the efficiency of anomaly detection and resource exploration, and promotes the sustainable management of the marine environment.

[0006] "Oceanic data" refers to information related to the marine environment, including physical and chemical parameters such as temperature, salinity, and ocean currents.

[0007] A "mobile device" is a device that collects data while moving through the ocean and on the seabed, and includes autonomous drones and sensor-equipped devices.

[0008] "Communication methods" refer to technologies for receiving data from mobile devices and transmitting it to processing systems, including acoustic communication and 5G networks.

[0009] "Data analysis methods" refer to technologies for processing received oceanographic data and extracting valuable information, utilizing generative AI models.

[0010] "Information provision methods" refer to technologies for presenting analysis results to users in an easy-to-understand manner, and include visualization dashboards and notification systems.

[0011] A "generative AI model" is an artificial intelligence technology that creates new information based on collected data and performs predictions and analyses.

[0012] "Environmental change" refers to temporal changes in the physical and chemical properties of the ocean, including extreme weather events and long-term changes in ocean patterns. [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] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

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

[0017] In the following embodiments, the labeled 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, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the labeled 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] The ocean management system of the present invention consists of numerous mobile units and servers and users that interact with these mobile units. The main objective of the system is to achieve sustainable ocean management by efficiently collecting and analyzing ocean data.

[0035] First, the mobile terminals are deployed in and on the seabed, collecting various marine data using sensors. This data includes seawater temperature, salinity, and current speed. The collected data is transmitted to a server via a base station. Acoustic communication and the latest 5G communication technologies are used for this process, enabling real-time data transmission.

[0036] The server stores the received ocean data in a database and performs analysis using generative AI. This analysis process includes automatic detection of anomalies, prediction of environmental changes, and proposal of optimal resource exploration routes. The generative AI learns from vast amounts of historical data to perform highly accurate analysis.

[0037] The analysis results are provided to the user. Users can view the analysis results and predictive information on a dedicated dashboard. This dashboard visualizes the collected data in graphs and maps for intuitive understanding. In addition, if an anomaly is detected, the user will be notified promptly and provided with information to consider necessary countermeasures.

[0038] For example, if an oil field development company uses this system, the optimal oil field exploration route will be proposed based on oceanographic data, and anomalies in the water temperature and salinity at the site will be detected quickly. This will enable efficient resource development while minimizing the impact on the environment.

[0039] Thus, the marine management system according to the present invention realizes sustainable management of the marine environment through real-time analysis of collected data and efficient information provision.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] When the device reaches a designated ocean area, it activates its built-in sensors to measure surrounding ocean data. This data includes temperature, salinity, and ocean current speed.

[0043] Step 2:

[0044] The terminal processes the measured data in real time and transmits it to the underwater base station via acoustic communication or a 5G network. The base station aggregates this data and transfers it to a server.

[0045] Step 3:

[0046] The server first stores the data received from the base station in a database. It checks the integrity of the data and filters out inaccurate or missing data.

[0047] Step 4:

[0048] The server feeds the verified data into the AI ​​module. The AI ​​analyzes the data to detect anomalies, predict environmental changes, and optimize resource exploration routes.

[0049] Step 5:

[0050] The server uses visualization tools to generate reports based on the analysis results. The reports are optimized for dashboards, and the data is visually represented using charts and maps.

[0051] Step 6:

[0052] Users log in to the provided dashboard to view the latest analysis results and forecast information. Based on this, users can quickly make necessary business decisions and implement environmental measures.

[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] Conventional marine management systems suffer from insufficient collection of environmental data and difficulties in real-time data transmission and analysis, making it challenging to predict environmental changes quickly and accurately or detect anomalies. Furthermore, there is a lack of information provided that allows users to intuitively understand the analysis results and take prompt countermeasures.

[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 means for collecting environmental information using a plurality of mobile devices, communication means for transmitting the information acquired from the mobile devices in real time, preprocessing means for storing and organizing the acquired information, data analysis means for analyzing the information using a generation AI model, detecting anomalies, predicting environmental changes, and proposing optimal resource exploration, and information provision means for displaying the analyzed results and transmitting warnings. This enables highly accurate prediction of environmental changes and detection of anomalies, and realizes the provision of efficient and intuitive information to the user.

[0058] A "mobile device" refers to equipment that is placed in the ocean or on the seabed to collect information in the marine environment, and is equipped with sensors to acquire data.

[0059] "Communication methods" refer to technologies and devices for transmitting environmental information from mobile devices to servers in real time, and include acoustic communication and 5G communication.

[0060] "Preprocessing means" refers to the process of data storage, organization, and imputation of missing values ​​performed on the server to prepare the received information into a format that is easy to analyze.

[0061] A "generative AI model" refers to an artificial intelligence algorithm that learns from a large amount of historical data and is capable of accurately predicting environmental changes and detecting anomalies.

[0062] "Data analysis means" refers to the process of analyzing data using an AI model based on acquired environmental information, detecting anomalies, predicting environmental changes, and proposing the optimal resource exploration route.

[0063] "Information provision means" refers to systems and devices that display the analyzed results to the user and promptly notify them of warnings as needed.

[0064] The program in this marine management system has three main components: servers, terminals, and users, each playing a specific role to make the entire system function.

[0065] The mobile terminal is deployed in a marine environment and is equipped with temperature sensors, salinity sensors, and current velocity sensors. These sensors acquire data on seawater temperature, salinity, and current velocity, and transmit this data to a server in real time. Communication uses acoustic and 5G technologies, and this combination enables stable communication with minimal data interruptions.

[0066] The server receives data sent from the terminal and stores it in a database. ETL (Extract, Transform, Load) tools are used to preprocess the received raw data, organizing it into a format suitable for analysis. Next, the server performs data analysis using a generative AI model. This generative AI model is trained on a large amount of historical ocean data, enabling highly accurate anomaly detection, environmental change prediction, and optimal resource exploration route proposals. Specifically, prompts are used to input data into the AI ​​model, generating output based on the analysis results. For example, a user could use the prompt, "Please tell me the data analysis patterns necessary to predict environmental change."

[0067] Users can view analysis results through a dedicated dashboard. The dashboard visualizes acquired data and analysis results as graphs and maps, allowing users to intuitively understand the information. Furthermore, if an anomaly is detected, the system sends a rapid notification, providing users with quick access to information to consider appropriate countermeasures.

[0068] As a concrete example, this system may be used by oil field development companies. In this case, the system proposes the optimal exploration route based on seawater environmental data and supports the rapid detection of environmental changes in the field. In this way, the present invention seamlessly handles everything from data collection and analysis to the provision of results in the marine environment, thereby realizing sustainable marine management.

[0069] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0070] Step 1:

[0071] The terminals are placed in and on the seabed and collect marine environmental data using temperature sensors, salinity sensors, and current velocity sensors. Inputs include physical characteristics (temperature, salinity, current velocity) acquired by the sensors, which are then converted into digital data. Specifically, each sensor collects data at regular intervals and stores it in a built-in data logger. The output is environmental data in digital format, which is transmitted to a communication module.

[0072] Step 2:

[0073] The terminal transmits the collected data to the server using acoustic and 5G communication technologies. The input is digital environmental data within the terminal, which is packetized according to the communication protocol. In specific operation, the data is transmitted to the base station in real time, where it is aggregated. The output is well-formed data packets received at the base station.

[0074] Step 3:

[0075] The server receives data packets transmitted from base stations and stores them in a database. The input is well-formed data packets, which are then converted into a format suitable for the database. Specifically, ETL (Extract, Transform, Load) tools are used to preprocess the data, performing necessary indexing and missing value imputation. The output is a dataset in a format optimized for analysis.

[0076] Step 4:

[0077] The server analyzes pre-processed data using a generative AI model. The input is a dataset stored in a database, which is then used for anomaly detection, environmental change prediction, and optimal resource exploration route suggestions. Specifically, the generative AI model interprets the data based on historical training data and real-time data to generate results. The output includes analysis results such as anomalies, prediction models, and recommended routes.

[0078] Step 5:

[0079] Users view the analysis results provided by the server on a dedicated dashboard. The input is the analysis results from the server, which are displayed as graphs and maps by a visualization engine. Specifically, users access the dashboard and utilize filtering and reporting functions as needed. The output is analysis results in an intuitively easy-to-understand format, including notifications to prompt necessary actions.

[0080] In this way, the system promotes the sustainable management of the marine environment through a series of processes, from data collection at terminals to analysis by servers and provision of information to users.

[0081] (Application Example 1)

[0082] 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."

[0083] There is a need to improve the operational efficiency and safety of autonomous mobile vehicles by collecting and analyzing environmental information in real time. However, existing technologies have not provided sufficient means to quickly reflect the analysis of collected data in the operation of the mobile vehicles. This invention aims to solve this problem and achieve efficient and safe optimization of the operating route.

[0084] 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.

[0085] In this invention, the server includes means for collecting environmental information using sensors mounted on a mobile vehicle, means for analyzing the information in real time and optimizing the route, and means for predicting environmental changes using a generative AI model. This enables optimization of the route and improvement of safety based on the collected data.

[0086] "Oceanographic information" refers to measurement data related to the marine environment, such as temperature, salinity, and current speed.

[0087] A "mobile device" is a machine or device that can be moved to collect data underwater and along the coast.

[0088] A "communication device" is a device that transmits data received from a mobile device to a central server in real time.

[0089] An "information analysis device" is a device that analyzes information received from a communication device to perform anomaly detection and prediction.

[0090] A "results presentation device" is a device that visualizes the results obtained through analysis and provides them to the user.

[0091] A "sensor" is a device used to measure environmental information and collect data.

[0092] A "route optimization device" is a device that optimizes the operating route of an autonomously operating mobile vehicle based on collected data and analysis results.

[0093] A "generative AI model" is an artificial intelligence technology that learns from collected ocean data to predict environmental changes and detect anomalies.

[0094] This invention provides a management system that optimizes the operation of an autonomous mobile vehicle by collecting ocean information in real time. The server collects data via various sensors mounted on the mobile vehicle. These sensors are installed to acquire data related to the marine environment, such as temperature, salinity, and current speed, in real time. The data is transmitted to the server using 5G communication equipment.

[0095] On the server, a data analysis device uses a generated AI model to analyze the received data. Specifically, it detects anomalies, predicts environmental changes, and generates optimized route instructions for moving objects. The generated AI model learns from past data to perform highly accurate predictions and analyses. The results of this analysis are visualized through a results presentation device provided to the user. The results are displayed in a dashboard format, and a graphical user interface is provided for intuitive understanding.

[0096] Furthermore, the route optimization device determines the operating route of the moving object based on the analysis results. For example, it may propose a new route that avoids areas with strong currents, thereby improving safety.

[0097] As a concrete example, when moving through an area with rapidly changing ocean currents, the generating AI detects these data fluctuations in advance and instructs the moving object to take a new route that avoids the surrounding area. This route selection ultimately ensures sustainable and safe travel.

[0098] An example of a prompt to use would be, "Based on current ocean data and vehicle location information, please suggest the safest and most efficient driving route." This prompt is presented to the generating AI model to assist in suggesting the optimal route.

[0099] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0100] Step 1:

[0101] The terminal acquires marine information. Sensors mounted on the terminal measure temperature, salinity, current speed, etc., in real time, and transmit this data to the server via a 5G communication module. The input is marine environmental sensor data, and the output is data transmission to the server.

[0102] Step 2:

[0103] The server analyzes the received data. Using a data analysis device, the server inputs the prompt message "Detect environmental anomalies based on current ocean data" into the generating AI model, and detects anomalies and environmental changes. The input is ocean data from sensors, and the output is anomaly detection results and predicted information on environmental changes.

[0104] Step 3:

[0105] The server optimizes the route based on the analysis results. The route optimization device calculates the optimal route based on the analysis results of the generated AI model and feeds this back to the terminal. The input is the analyzed environmental information, and the output is the route information of the moving object.

[0106] Step 4:

[0107] The user reviews the analysis results. The server provides the user with visualized analysis results through a user interface, making it easy to view the information in a dashboard format. The input is the analysis's predicted information and optimized route, and the output is the information display to the user.

[0108] Step 5:

[0109] The terminal continues its operation according to the new route. The terminal operates autonomously based on the optimized route provided by the server. The input is route information from the server, and the output is the operation of the mobile object along the route.

[0110] 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.

[0111] The ocean management system of the present invention not only efficiently collects and analyzes ocean data and provides the results to the user, but also incorporates an emotion engine to recognize the user's emotions and optimize the information provided. The system mainly consists of multiple mobile units for ocean data collection, a server, a user interface, and an emotion engine.

[0112] First, the mobile terminal is equipped with marine environmental sensors to collect data within a designated area. This data includes temperature, salinity, and ocean current speed. The collected data is transmitted in real time to a server via a base station.

[0113] The server stores the received data in a database and performs analysis using generating AI. This analysis includes anomaly detection, environmental change prediction, and proposal of optimal resource exploration routes. The analysis results are generated as a visual report and provided to the user's dashboard.

[0114] Furthermore, the system uses an emotion engine to recognize the user's emotions. The user's emotional status is evaluated based on their interaction with the system and data input. Based on this emotional status, the format and content of the information presented are dynamically adjusted. For example, if the user is evaluated as feeling stressed, the information is presented more concisely, and alerts are displayed that focus on the essential points.

[0115] As a concrete example, consider a researcher monitoring climate change. This researcher uses the system to acquire ocean data and receive detailed predictions of environmental change. If the emotion engine determines that the researcher's stress level is high, the necessary data is provided in an intuitively understandable highlighted format.

[0116] Thus, the marine management system of the present invention promotes the effective use of information by efficiently collecting and analyzing data, as well as individually optimizing the user experience through emotion recognition.

[0117] The following describes the processing flow.

[0118] Step 1:

[0119] The device moves through the ocean following a pre-programmed route, collecting oceanographic data using sensors. This data includes information such as temperature, salinity, and ocean current speed.

[0120] Step 2:

[0121] The terminal transmits data collected by sensors to the base station in real time. This communication uses a 5G network or acoustic communication to achieve high-speed and stable data transfer.

[0122] Step 3:

[0123] The server receives data from the base station, first verifies the data's integrity, and then stores it in the database. If any data is missing, it executes an algorithm to fill in the gaps.

[0124] Step 4:

[0125] The server analyzes the received ocean data using a generative AI model. The analysis process includes detecting anomalous data points, predicting future environmental changes, and calculating the optimal resource exploration route.

[0126] Step 5:

[0127] The server inputs the analysis results into the emotion engine and optimizes the information provided to reflect the user's emotional state. The emotion engine estimates emotions from the user's recent interaction data.

[0128] Step 6:

[0129] The server sends the generated report to the user in dashboard format. The way information is presented and the points emphasized are adjusted according to the user's emotional state. For example, if the user is determined to be confused, the information is visualized and the most important data is summarized.

[0130] Step 7:

[0131] Users review the information provided through the dashboard and make decisions as needed, including environmental measures and adjustments to resource exploration. User feedback is used to improve the system's future sentiment recognition accuracy.

[0132] (Example 2)

[0133] 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".

[0134] There is a challenge in efficiently collecting and analyzing marine data, while simultaneously lacking methods and technologies to optimize information delivery based on user emotions. Current systems fail to adequately present large amounts of data to users, and information delivery does not take into account user emotions or stress levels, making effective decision-making difficult.

[0135] 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.

[0136] In this invention, the server includes multiple mobile means for collecting ocean data, emotion recognition means for recognizing the user's emotions, and data analysis means for analyzing the data using a generative AI model. This enables not only the collection and analysis of ocean data, but also the provision of optimal information tailored to the user's emotions.

[0137] "Oceanic data" refers to information about the marine environment, including indicators such as temperature, salinity, and ocean current speed.

[0138] A "mobile device" refers to a device equipped with sensors that collects ocean data and moves within a designated area.

[0139] "Communication means" refers to the technology or method for transmitting data obtained from a mobile device to a server in real time.

[0140] "Data analysis methods" refer to technologies used to analyze collected data and detect anomalies or predict environmental changes.

[0141] "Emotion recognition means" refers to technology that evaluates the user's emotions and optimizes information provision based on that information.

[0142] "Information provision means" refers to the methods and interfaces used to present the analyzed results to the user.

[0143] A "generative AI model" refers to a model that uses AI technology to perform data analysis, particularly for predicting and proposing environmental changes.

[0144] This invention is a system that collects data on the marine environment and provides the analysis results to the user. Furthermore, it aims to recognize the user's emotions and optimize the information provided in accordance with those emotions.

[0145] The terminal includes a mobile unit equipped with marine environmental sensors. This unit moves through a designated ocean area, collecting data such as temperature, salinity, and ocean current speed in real time. The collected data is immediately transmitted to a server, allowing for analysis based on the latest information. Standard wireless communication technology is used for this communication.

[0146] The server stores the received data in a database and performs analysis using a generative AI model. During the analysis, prompts are used to detect anomalies, predict environmental changes, and propose resource exploration routes. The AI ​​model employs an open-source machine learning framework and is customized as needed. Specific analysis methods include anomaly detection algorithms and time-series analysis.

[0147] Furthermore, the server utilizes an emotion engine to recognize the user's emotions. This allows the system to evaluate the user's emotional status based on their interactions and inputs, and adjust the information provided based on the results. The way information is presented is tailored to the user's stress level, becoming more concise or highlighting important information clearly.

[0148] As a concrete example, consider a researcher conducting marine research who uses this system. This researcher receives environmental change predictions from a generative AI model based on marine data collected by a mobile device, and uses this information to inform their research. In addition, if the emotion engine determines that stress levels are high, important information is highlighted and presented in an easy-to-understand manner.

[0149] An example of a prompt to be input to the generating AI model is: "Analyze the water temperature and salinity data for the specified sea area and predict environmental changes by comparing them to the same period last year. Also, consider how to present information concisely if the user is feeling stressed."

[0150] Thus, the present invention provides a means for reliably collecting marine environmental data and providing flexible information tailored to the user's situation.

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] The terminal uses a mobile device equipped with marine environmental sensors to collect data from a designated sea area. Specifically, it obtains measurements such as temperature, salinity, and ocean current speed through the sensors. This data is compiled in real time by the mobile device's control system. The input is the sensor values, and the output is consistent data collected in real time.

[0154] Step 2:

[0155] The terminal transmits the data collected in Step 1 to the server using a communication method. The data is sent to a base station using common wireless communication technology and then securely transferred to the server. The input is consistent data, and the output is the data sent to the server.

[0156] Step 3:

[0157] The server stores the data received in step 2 into the database. Simultaneously, it checks for duplicates and missing data and performs data cleaning. The input is the received data, and the output is the cleaned database entry. This process forms the foundation for the subsequent data analysis stage.

[0158] Step 4:

[0159] The server applies the generated AI model to the data cleaned in step 3 and performs analysis. Specifically, it utilizes prompts to perform anomaly detection, predict environmental changes, and propose resource exploration routes. In this analysis, the AI ​​model performs pattern recognition and time series analysis. The input is the cleaned data, and the output is the analysis results.

[0160] Step 5:

[0161] The server evaluates the user's emotions through emotion recognition mechanisms. Based on user interactions and input data, the emotion engine measures stress levels and levels of interest. Input is the user's behavioral history and input data, and output is the evaluation result of the emotional state.

[0162] Step 6:

[0163] The server determines the format of information delivery based on the analysis results in step 4 and the sentiment assessment in step 5. It adjusts the data presentation method according to the user's emotional state, emphasizing concise and important information when stress levels are high. The input is the analysis results and sentiment assessment, and the output is the adjusted information delivery. This allows the user to receive detailed and effective information relevant to their situation.

[0164] (Application Example 2)

[0165] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0166] In logistics operations, improving time efficiency and accuracy is essential, but conventional systems are slow to transmit and analyze data, making real-time optimization difficult. Furthermore, employee emotional states affect work efficiency, but there is a lack of information provision methods that take this into account.

[0167] 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.

[0168] In this invention, the server includes means for controlling multiple mobile objects that collect data, means for receiving data obtained from the mobile objects in real time, and means for analyzing the received data. This streamlines data collection in logistics operations and enables the provision of optimal work instructions based on data analysis in real time. Furthermore, the emotion analysis means enables the provision of information tailored to the emotional state of employees, thereby improving work efficiency and employee satisfaction.

[0169] "Multiple mobile devices for data collection" refers to a collection of portable measurement units used to efficiently gather necessary information in an environment or work site.

[0170] "Communication means" refers to wireless or wired data transmission devices used to transfer data acquired from a mobile device to a server or processing device in real time.

[0171] "Data analysis means" refers to software or algorithms used to analyze received data and extract or predict necessary information.

[0172] "Information provision means" refers to an interface or device for displaying analyzed data in a format that is easy for users to understand.

[0173] "Emotional analysis means" refers to sensors and analytical algorithms used to evaluate a user's emotional state and optimize the content of the information provided.

[0174] The system for realizing this invention consists of multiple mobile units for data collection, a server equipped with communication technology, an application for data analysis, a user interface for providing information, and an engine for performing sentiment analysis.

[0175] The server utilizes high-speed communication technology to receive data acquired from each mobile device in real time. The received data is processed by a data analysis application, which extracts necessary information using conventional analysis algorithms and machine learning techniques. In this process, Python and its libraries, such as Tensorflow®, are commonly used as programming languages ​​and generative AI models. The analysis results are displayed as a visual dashboard in the user interface. Specifically, information necessary for improving work efficiency and optimizing inventory management is displayed.

[0176] Furthermore, the server uses an emotion analysis engine to estimate the user's emotional state from their voice and physical data, and adjusts the content and format of the information presented based on the results. For this purpose, a device with a built-in microphone and camera sensor is used. If the user's stress level is determined to be high, the system is designed to reduce the user's burden by selectively displaying only concise and important information.

[0177] As a concrete example, consider a scenario where a new employee is performing their first tasks at a logistics center. The server displays work instructions on a glasses-type device, and when the emotion analysis engine detects the new employee's anxiety, it suggests simplifying the information and providing it in stages to facilitate understanding. An example of a prompt to the generative AI model in this case might be, "Please suggest efficient instructions for a new employee."

[0178] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0179] Step 1:

[0180] The terminal collects data from the environment using sensors mounted on a mobile device. This data includes inventory status and pallet location information within the logistics center. Raw data from the sensors is the input, and this data is sent to the server in a formatted form via a communication means as the output.

[0181] Step 2:

[0182] The server receives collected data from terminals in real time. It receives formatted data from terminals as input and stores it in a database for analysis. After performing preprocessing such as data organization and adding timestamps, it provides the prepared data to the analysis platform as output.

[0183] Step 3:

[0184] The server analyzes the received data using a data analysis application. During this process, it runs a generative AI model using libraries such as TensorFlow from Python. The input is the data prepared in step 2, and the output provides insights useful for optimizing inventory management and improving logistics efficiency. Specific operations include anomaly detection and real-time demand forecasting.

[0185] Step 4:

[0186] Information for the user is provided to the user interface by the server. The input is the analysis results generated in step 3, and the output is route information and work instructions displayed on the employee's glasses-type display. Specifically, a visual dashboard is generated and customized to the user's needs.

[0187] Step 5:

[0188] The emotion analysis engine built into the device monitors the user's voice data and behavior to evaluate their emotional state. Inputs include sensor data from the microphone and camera, and output is generated representing the user's emotional state, such as their stress level. Specifically, an algorithm operates to infer emotions from voice tone and facial expressions.

[0189] Step 6:

[0190] The server dynamically adjusts the information provided based on the results of the emotion analysis engine. The input is the emotional state obtained in step 5, and the output is information modified to suit the user's state, which is then displayed on the screen. Specifically, this involves selecting information of high importance and changing the presentation style.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Second Embodiment]

[0195] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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".

[0207] The ocean management system of the present invention consists of numerous mobile units and servers and users that interact with these mobile units. The main objective of the system is to achieve sustainable ocean management by efficiently collecting and analyzing ocean data.

[0208] First, the mobile terminals are deployed in and on the seabed, collecting various marine data using sensors. This data includes seawater temperature, salinity, and current speed. The collected data is transmitted to a server via a base station. Acoustic communication and the latest 5G communication technologies are used for this process, enabling real-time data transmission.

[0209] The server stores the received ocean data in a database and performs analysis using generative AI. This analysis process includes automatic detection of anomalies, prediction of environmental changes, and proposal of optimal resource exploration routes. The generative AI learns from vast amounts of historical data to perform highly accurate analysis.

[0210] The analysis results are provided to the user. Users can view the analysis results and predictive information on a dedicated dashboard. This dashboard visualizes the collected data in graphs and maps for intuitive understanding. In addition, if an anomaly is detected, the user will be notified promptly and provided with information to consider necessary countermeasures.

[0211] For example, if an oil field development company uses this system, the optimal oil field exploration route will be proposed based on oceanographic data, and anomalies in the water temperature and salinity at the site will be detected quickly. This will enable efficient resource development while minimizing the impact on the environment.

[0212] Thus, the marine management system according to the present invention realizes sustainable management of the marine environment through real-time analysis of collected data and efficient information provision.

[0213] The following describes the processing flow.

[0214] Step 1:

[0215] When the device reaches a designated ocean area, it activates its built-in sensors to measure surrounding ocean data. This data includes temperature, salinity, and ocean current speed.

[0216] Step 2:

[0217] The terminal processes the measured data in real time and transmits it to the underwater base station via acoustic communication or a 5G network. The base station aggregates this data and transfers it to a server.

[0218] Step 3:

[0219] The server first stores the data received from the base station in a database. It checks the integrity of the data and filters out inaccurate or missing data.

[0220] Step 4:

[0221] The server feeds the verified data into the AI ​​module. The AI ​​analyzes the data to detect anomalies, predict environmental changes, and optimize resource exploration routes.

[0222] Step 5:

[0223] The server uses visualization tools to generate reports based on the analysis results. The reports are optimized for dashboards, and the data is visually represented using charts and maps.

[0224] Step 6:

[0225] Users log in to the provided dashboard to view the latest analysis results and forecast information. Based on this, users can quickly make necessary business decisions and implement environmental measures.

[0226] (Example 1)

[0227] 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."

[0228] Conventional marine management systems suffer from insufficient collection of environmental data and difficulties in real-time data transmission and analysis, making it challenging to predict environmental changes quickly and accurately or detect anomalies. Furthermore, there is a lack of information provided that allows users to intuitively understand the analysis results and take prompt countermeasures.

[0229] 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.

[0230] In this invention, the server includes means for collecting environmental information using a plurality of mobile devices, communication means for transmitting the information acquired from the mobile devices in real time, preprocessing means for storing and organizing the acquired information, data analysis means for analyzing the information using a generation AI model, detecting anomalies, predicting environmental changes, and proposing optimal resource exploration, and information provision means for displaying the analyzed results and transmitting warnings. This enables highly accurate prediction of environmental changes and detection of anomalies, and realizes the provision of efficient and intuitive information to the user.

[0231] A "mobile device" refers to equipment that is placed in the ocean or on the seabed to collect information in the marine environment, and is equipped with sensors to acquire data.

[0232] "Communication methods" refer to technologies and devices for transmitting environmental information from mobile devices to servers in real time, and include acoustic communication and 5G communication.

[0233] "Preprocessing means" refers to the process of data storage, organization, and imputation of missing values ​​performed on the server to prepare the received information into a format that is easy to analyze.

[0234] A "generative AI model" refers to an artificial intelligence algorithm that learns from a large amount of historical data and is capable of accurately predicting environmental changes and detecting anomalies.

[0235] "Data analysis means" refers to the process of analyzing data using an AI model based on acquired environmental information, detecting anomalies, predicting environmental changes, and proposing the optimal resource exploration route.

[0236] "Information provision means" refers to systems and devices that display the analyzed results to the user and promptly notify them of warnings as needed.

[0237] The program in this marine management system has three main components: servers, terminals, and users, each playing a specific role to make the entire system function.

[0238] The mobile terminal is deployed in a marine environment and is equipped with temperature sensors, salinity sensors, and current velocity sensors. These sensors acquire data on seawater temperature, salinity, and current velocity, and transmit this data to a server in real time. Communication uses acoustic and 5G technologies, and this combination enables stable communication with minimal data interruptions.

[0239] The server receives data sent from the terminal and stores it in a database. ETL (Extract, Transform, Load) tools are used to preprocess the received raw data, organizing it into a format suitable for analysis. Next, the server performs data analysis using a generative AI model. This generative AI model is trained on a large amount of historical ocean data, enabling highly accurate anomaly detection, environmental change prediction, and optimal resource exploration route proposals. Specifically, prompts are used to input data into the AI ​​model, generating output based on the analysis results. For example, a user could use the prompt, "Please tell me the data analysis patterns necessary to predict environmental change."

[0240] Users can view analysis results through a dedicated dashboard. The dashboard visualizes acquired data and analysis results as graphs and maps, allowing users to intuitively understand the information. Furthermore, if an anomaly is detected, the system sends a rapid notification, providing users with quick access to information to consider appropriate countermeasures.

[0241] As a concrete example, this system may be used by oil field development companies. In this case, the system proposes the optimal exploration route based on seawater environmental data and supports the rapid detection of environmental changes in the field. In this way, the present invention seamlessly handles everything from data collection and analysis to the provision of results in the marine environment, thereby realizing sustainable marine management.

[0242] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0243] Step 1:

[0244] The terminals are placed in and on the seabed and collect marine environmental data using temperature sensors, salinity sensors, and current velocity sensors. Inputs include physical characteristics (temperature, salinity, current velocity) acquired by the sensors, which are then converted into digital data. Specifically, each sensor collects data at regular intervals and stores it in a built-in data logger. The output is environmental data in digital format, which is transmitted to a communication module.

[0245] Step 2:

[0246] The terminal transmits the collected data to the server using acoustic and 5G communication technologies. The input is digital environmental data within the terminal, which is packetized according to the communication protocol. In specific operation, the data is transmitted to the base station in real time, where it is aggregated. The output is well-formed data packets received at the base station.

[0247] Step 3:

[0248] The server receives data packets transmitted from base stations and stores them in a database. The input is well-formed data packets, which are then converted into a format suitable for the database. Specifically, ETL (Extract, Transform, Load) tools are used to preprocess the data, performing necessary indexing and missing value imputation. The output is a dataset in a format optimized for analysis.

[0249] Step 4:

[0250] The server analyzes pre-processed data using a generative AI model. The input is a dataset stored in a database, which is then used for anomaly detection, environmental change prediction, and optimal resource exploration route suggestions. Specifically, the generative AI model interprets the data based on historical training data and real-time data to generate results. The output includes analysis results such as anomalies, prediction models, and recommended routes.

[0251] Step 5:

[0252] Users view the analysis results provided by the server on a dedicated dashboard. The input is the analysis results from the server, which are displayed as graphs and maps by a visualization engine. Specifically, users access the dashboard and utilize filtering and reporting functions as needed. The output is analysis results in an intuitively easy-to-understand format, including notifications to prompt necessary actions.

[0253] In this way, the system promotes the sustainable management of the marine environment through a series of processes, from data collection at terminals to analysis by servers and provision of information to users.

[0254] (Application Example 1)

[0255] 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."

[0256] There is a need to improve the operational efficiency and safety of autonomous mobile vehicles by collecting and analyzing environmental information in real time. However, existing technologies have not provided sufficient means to quickly reflect the analysis of collected data in the operation of the mobile vehicles. This invention aims to solve this problem and achieve efficient and safe optimization of the operating route.

[0257] 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.

[0258] In this invention, the server includes means for collecting environmental information using sensors mounted on a mobile vehicle, means for analyzing the information in real time and optimizing the route, and means for predicting environmental changes using a generative AI model. This enables optimization of the route and improvement of safety based on the collected data.

[0259] "Oceanographic information" refers to measurement data related to the marine environment, such as temperature, salinity, and current speed.

[0260] A "mobile device" is a machine or device that can be moved to collect data underwater and along the coast.

[0261] A "communication device" is a device that transmits data received from a mobile device to a central server in real time.

[0262] An "information analysis device" is a device that analyzes information received from a communication device to perform anomaly detection and prediction.

[0263] A "results presentation device" is a device that visualizes the results obtained through analysis and provides them to the user.

[0264] A "sensor" is a device used to measure environmental information and collect data.

[0265] A "route optimization device" is a device that optimizes the operating route of an autonomously operating mobile vehicle based on collected data and analysis results.

[0266] A "generative AI model" is an artificial intelligence technology that learns from collected ocean data to predict environmental changes and detect anomalies.

[0267] This invention provides a management system that optimizes the operation of an autonomous mobile vehicle by collecting ocean information in real time. The server collects data via various sensors mounted on the mobile vehicle. These sensors are installed to acquire data related to the marine environment, such as temperature, salinity, and current speed, in real time. The data is transmitted to the server using 5G communication equipment.

[0268] On the server, a data analysis device uses a generated AI model to analyze the received data. Specifically, it detects anomalies, predicts environmental changes, and generates optimized route instructions for moving objects. The generated AI model learns from past data to perform highly accurate predictions and analyses. The results of this analysis are visualized through a results presentation device provided to the user. The results are displayed in a dashboard format, and a graphical user interface is provided for intuitive understanding.

[0269] Furthermore, the route optimization device determines the operating route of the moving object based on the analysis results. For example, it may propose a new route that avoids areas with strong currents, thereby improving safety.

[0270] As a concrete example, when moving through an area with rapidly changing ocean currents, the generating AI detects these data fluctuations in advance and instructs the moving object to take a new route that avoids the surrounding area. This route selection ultimately ensures sustainable and safe travel.

[0271] An example of a prompt to use would be, "Based on current ocean data and vehicle location information, please suggest the safest and most efficient driving route." This prompt is presented to the generating AI model to assist in suggesting the optimal route.

[0272] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0273] Step 1:

[0274] The terminal acquires marine information. Sensors mounted on the terminal measure temperature, salinity, current speed, etc., in real time, and transmit this data to the server via a 5G communication module. The input is marine environmental sensor data, and the output is data transmission to the server.

[0275] Step 2:

[0276] The server analyzes the received data. Using a data analysis device, the server inputs the prompt message "Detect environmental anomalies based on current ocean data" into the generating AI model, and detects anomalies and environmental changes. The input is ocean data from sensors, and the output is anomaly detection results and predicted information on environmental changes.

[0277] Step 3:

[0278] The server optimizes the route based on the analysis results. The route optimization device calculates the optimal route based on the analysis results of the generated AI model and feeds this back to the terminal. The input is the analyzed environmental information, and the output is the route information of the moving object.

[0279] Step 4:

[0280] The user reviews the analysis results. The server provides the user with visualized analysis results through a user interface, making it easy to view the information in a dashboard format. The input is the analysis's predicted information and optimized route, and the output is the information display to the user.

[0281] Step 5:

[0282] The terminal continues to operate according to the new operation route. The terminal performs autonomous operation based on the optimized operation route provided by the server. The input is the operation route information from the server, and the output is the operation of the moving body along the route.

[0283] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0284] The marine management system of the present invention not only efficiently collects marine data and provides the analysis results to the user, but also incorporates an emotion engine to recognize the user's emotion and optimize information provision. The system mainly consists of a plurality of moving bodies for marine data collection, a server, a user interface, and an emotion engine.

[0285] First, the moving body as a terminal is equipped with a marine environment sensor and collects data within the designated area. This includes temperature, salinity concentration, and sea current speed. The collected data is transmitted to the server in real time via the base station.

[0286] The server stores the received data in the database and performs analysis using the generation AI. In the analysis, detection of abnormalities, prediction of environmental changes, proposal of an optimal resource exploration route, etc. are performed. The analysis results are generated as a visual report and provided to the user's dashboard.

[0287] Furthermore, the system uses an emotion engine to recognize the user's emotions. The user's emotional status is evaluated based on their interaction with the system and data input. Based on this emotional status, the format and content of the information presented are dynamically adjusted. For example, if the user is evaluated as feeling stressed, the information is presented more concisely, and alerts are displayed that focus on the essential points.

[0288] As a concrete example, consider a researcher monitoring climate change. This researcher uses the system to acquire ocean data and receive detailed predictions of environmental change. If the emotion engine determines that the researcher's stress level is high, the necessary data is provided in an intuitively understandable highlighted format.

[0289] Thus, the marine management system of the present invention promotes the effective use of information by efficiently collecting and analyzing data, as well as individually optimizing the user experience through emotion recognition.

[0290] The following describes the processing flow.

[0291] Step 1:

[0292] The device moves through the ocean following a pre-programmed route, collecting oceanographic data using sensors. This data includes information such as temperature, salinity, and ocean current speed.

[0293] Step 2:

[0294] The terminal transmits data collected by sensors to the base station in real time. This communication uses a 5G network or acoustic communication to achieve high-speed and stable data transfer.

[0295] Step 3:

[0296] The server receives data from the base station, first verifies the data's integrity, and then stores it in the database. If any data is missing, it executes an algorithm to fill in the gaps.

[0297] Step 4:

[0298] The server analyzes the received ocean data using a generative AI model. The analysis process includes detecting anomalous data points, predicting future environmental changes, and calculating the optimal resource exploration route.

[0299] Step 5:

[0300] The server inputs the analysis results into the emotion engine and optimizes the information provided to reflect the user's emotional state. The emotion engine estimates emotions from the user's recent interaction data.

[0301] Step 6:

[0302] The server sends the generated report to the user in dashboard format. The way information is presented and the points emphasized are adjusted according to the user's emotional state. For example, if the user is determined to be confused, the information is visualized and the most important data is summarized.

[0303] Step 7:

[0304] Users review the information provided through the dashboard and make decisions as needed, including environmental measures and adjustments to resource exploration. User feedback is used to improve the system's future sentiment recognition accuracy.

[0305] (Example 2)

[0306] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0307] There is a problem that there is a lack of methods and technologies for optimizing information provision based on the emotions of users while efficiently collecting and analyzing ocean data. In the current system, a large amount of data cannot be appropriately presented to users, and information transmission considering the emotions and stress levels of users is not carried out, making it difficult to make effective decisions.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0309] In this invention, the server includes a plurality of mobile means for collecting ocean data, an emotion recognition means for recognizing the emotions of users, and a data analysis means for analyzing data using a generated AI model. Thereby, not only the collection and analysis of ocean data can be performed, but also optimal information provision according to the emotions of users becomes possible.

[0310] "Ocean data" refers to information related to the ocean environment and includes indicators such as temperature, salinity concentration, and ocean current speed.

[0311] "Mobile body" refers to a device equipped with sensors to collect ocean data and move in a designated area.

[0312] "Communication means" refers to a technology or method for transmitting data obtained from a mobile body to the server in real time.

[0313] "Data analysis means" refers to a technology for analyzing the collected data to detect abnormalities and predict environmental changes.

[0314] "Emotion recognition means" refers to a technology for evaluating the emotions of users and optimizing information provision based on that information.

[0315] "Information provision means" refers to a method or interface for presenting the analyzed results to users.

[0316] <00009A "generative AI model" refers to a model that uses AI technology to perform data analysis, particularly for predicting and proposing environmental changes.

[0317] This invention is a system that collects data on the marine environment and provides the analysis results to the user. Furthermore, it aims to recognize the user's emotions and optimize the information provided in accordance with those emotions.

[0318] The terminal includes a mobile unit equipped with marine environmental sensors. This unit moves through a designated ocean area, collecting data such as temperature, salinity, and ocean current speed in real time. The collected data is immediately transmitted to a server, allowing for analysis based on the latest information. Standard wireless communication technology is used for this communication.

[0319] The server stores the received data in a database and performs analysis using a generative AI model. During the analysis, prompts are used to detect anomalies, predict environmental changes, and propose resource exploration routes. The AI ​​model employs an open-source machine learning framework and is customized as needed. Specific analysis methods include anomaly detection algorithms and time-series analysis.

[0320] Furthermore, the server utilizes an emotion engine to recognize the user's emotions. This allows the system to evaluate the user's emotional status based on their interactions and inputs, and adjust the information provided based on the results. The way information is presented is tailored to the user's stress level, becoming more concise or highlighting important information clearly.

[0321] As a concrete example, consider a researcher conducting marine research who uses this system. This researcher receives environmental change predictions from a generative AI model based on marine data collected by a mobile device, and uses this information to inform their research. In addition, if the emotion engine determines that stress levels are high, important information is highlighted and presented in an easy-to-understand manner.

[0322] An example of a prompt to be input to the generating AI model is: "Analyze the water temperature and salinity data for the specified sea area and predict environmental changes by comparing them to the same period last year. Also, consider how to present information concisely if the user is feeling stressed."

[0323] Thus, the present invention provides a means for reliably collecting marine environmental data and providing flexible information tailored to the user's situation.

[0324] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0325] Step 1:

[0326] The terminal uses a mobile device equipped with marine environmental sensors to collect data from a designated sea area. Specifically, it obtains measurements such as temperature, salinity, and ocean current speed through the sensors. This data is compiled in real time by the mobile device's control system. The input is the sensor values, and the output is consistent data collected in real time.

[0327] Step 2:

[0328] The terminal transmits the data collected in Step 1 to the server using a communication method. The data is sent to a base station using common wireless communication technology and then securely transferred to the server. The input is consistent data, and the output is the data sent to the server.

[0329] Step 3:

[0330] The server stores the data received in step 2 into the database. Simultaneously, it checks for duplicates and missing data and performs data cleaning. The input is the received data, and the output is the cleaned database entry. This process forms the foundation for the subsequent data analysis stage.

[0331] Step 4:

[0332] The server applies the generated AI model to the data cleaned in step 3 and performs analysis. Specifically, it utilizes prompts to perform anomaly detection, predict environmental changes, and propose resource exploration routes. In this analysis, the AI ​​model performs pattern recognition and time series analysis. The input is the cleaned data, and the output is the analysis results.

[0333] Step 5:

[0334] The server evaluates the user's emotions through emotion recognition mechanisms. Based on user interactions and input data, the emotion engine measures stress levels and levels of interest. Input is the user's behavioral history and input data, and output is the evaluation result of the emotional state.

[0335] Step 6:

[0336] The server determines the format of information delivery based on the analysis results in step 4 and the sentiment assessment in step 5. It adjusts the data presentation method according to the user's emotional state, emphasizing concise and important information when stress levels are high. The input is the analysis results and sentiment assessment, and the output is the adjusted information delivery. This allows the user to receive detailed and effective information relevant to their situation.

[0337] (Application Example 2)

[0338] 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."

[0339] In logistics operations, improving time efficiency and accuracy is essential, but conventional systems are slow to transmit and analyze data, making real-time optimization difficult. Furthermore, employee emotional states affect work efficiency, but there is a lack of information provision methods that take this into account.

[0340] 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.

[0341] In this invention, the server includes means for controlling multiple mobile objects that collect data, means for receiving data obtained from the mobile objects in real time, and means for analyzing the received data. This streamlines data collection in logistics operations and enables the provision of optimal work instructions based on data analysis in real time. Furthermore, the emotion analysis means enables the provision of information tailored to the emotional state of employees, thereby improving work efficiency and employee satisfaction.

[0342] "Multiple mobile devices for data collection" refers to a collection of portable measurement units used to efficiently gather necessary information in an environment or work site.

[0343] "Communication means" refers to wireless or wired data transmission devices used to transfer data acquired from a mobile device to a server or processing device in real time.

[0344] "Data analysis means" refers to software or algorithms used to analyze received data and extract or predict necessary information.

[0345] "Information provision means" refers to an interface or device for displaying analyzed data in a format that is easy for users to understand.

[0346] "Emotional analysis means" refers to sensors and analytical algorithms used to evaluate a user's emotional state and optimize the content of the information provided.

[0347] The system for realizing this invention consists of multiple mobile units for data collection, a server equipped with communication technology, an application for data analysis, a user interface for providing information, and an engine for performing sentiment analysis.

[0348] The server utilizes high-speed communication technology to receive data acquired from each mobile device in real time. The received data is processed by a data analysis application, which extracts necessary information using conventional analysis algorithms and machine learning techniques. In this process, Python and its libraries, such as TensorFlow, are commonly used as programming languages ​​and generative AI models. The analysis results are displayed as a visual dashboard in the user interface. Specifically, information necessary for improving work efficiency and optimizing inventory management is displayed.

[0349] Furthermore, the server uses an emotion analysis engine to estimate the user's emotional state from their voice and physical data, and adjusts the content and format of the information presented based on the results. For this purpose, a device with a built-in microphone and camera sensor is used. If the user's stress level is determined to be high, the system is designed to reduce the user's burden by selectively displaying only concise and important information.

[0350] As a concrete example, consider a scenario where a new employee is performing their first tasks at a logistics center. The server displays work instructions on a glasses-type device, and when the emotion analysis engine detects the new employee's anxiety, it suggests simplifying the information and providing it in stages to facilitate understanding. An example of a prompt to the generative AI model in this case might be, "Please suggest efficient instructions for a new employee."

[0351] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0352] Step 1:

[0353] The terminal collects data from the environment using sensors mounted on a mobile device. This data includes inventory status and pallet location information within the logistics center. Raw data from the sensors is the input, and this data is sent to the server in a formatted form via a communication means as the output.

[0354] Step 2:

[0355] The server receives collected data from terminals in real time. It receives formatted data from terminals as input and stores it in a database for analysis. After performing preprocessing such as data organization and adding timestamps, it provides the prepared data to the analysis platform as output.

[0356] Step 3:

[0357] The server analyzes the received data using a data analysis application. During this process, it runs a generative AI model using libraries such as TensorFlow from Python. The input is the data prepared in step 2, and the output provides insights useful for optimizing inventory management and improving logistics efficiency. Specific operations include anomaly detection and real-time demand forecasting.

[0358] Step 4:

[0359] Information for the user is provided to the user interface by the server. The input is the analysis results generated in step 3, and the output is route information and work instructions displayed on the employee's glasses-type display. Specifically, a visual dashboard is generated and customized to the user's needs.

[0360] Step 5:

[0361] The emotion analysis engine built into the device monitors the user's voice data and behavior to evaluate their emotional state. Inputs include sensor data from the microphone and camera, and output is generated representing the user's emotional state, such as their stress level. Specifically, an algorithm operates to infer emotions from voice tone and facial expressions.

[0362] Step 6:

[0363] The server dynamically adjusts the information provided based on the results of the emotion analysis engine. The input is the emotional state obtained in step 5, and the output is information modified to suit the user's state, which is then displayed on the screen. Specifically, this involves selecting information of high importance and changing the presentation style.

[0364] 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.

[0365] 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.

[0366] 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.

[0367] [Third Embodiment]

[0368] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0369] 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.

[0370] 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).

[0371] 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.

[0372] 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.

[0373] 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).

[0374] 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.

[0375] 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.

[0376] 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.

[0377] 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.

[0378] 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.

[0379] 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".

[0380] The ocean management system of the present invention consists of numerous mobile units and servers and users that interact with these mobile units. The main objective of the system is to achieve sustainable ocean management by efficiently collecting and analyzing ocean data.

[0381] First, the mobile terminals are deployed in and on the seabed, collecting various marine data using sensors. This data includes seawater temperature, salinity, and current speed. The collected data is transmitted to a server via a base station. Acoustic communication and the latest 5G communication technologies are used for this process, enabling real-time data transmission.

[0382] The server stores the received ocean data in a database and performs analysis using generative AI. This analysis process includes automatic detection of anomalies, prediction of environmental changes, and proposal of optimal resource exploration routes. The generative AI learns from vast amounts of historical data to perform highly accurate analysis.

[0383] The analysis results are provided to the user. Users can view the analysis results and predictive information on a dedicated dashboard. This dashboard visualizes the collected data in graphs and maps for intuitive understanding. In addition, if an anomaly is detected, the user will be notified promptly and provided with information to consider necessary countermeasures.

[0384] For example, if an oil field development company uses this system, the optimal oil field exploration route will be proposed based on oceanographic data, and anomalies in the water temperature and salinity at the site will be detected quickly. This will enable efficient resource development while minimizing the impact on the environment.

[0385] Thus, the marine management system according to the present invention realizes sustainable management of the marine environment through real-time analysis of collected data and efficient information provision.

[0386] The following describes the processing flow.

[0387] Step 1:

[0388] When the device reaches a designated ocean area, it activates its built-in sensors to measure surrounding ocean data. This data includes temperature, salinity, and ocean current speed.

[0389] Step 2:

[0390] The terminal processes the measured data in real time and transmits it to the underwater base station via acoustic communication or a 5G network. The base station aggregates this data and transfers it to a server.

[0391] Step 3:

[0392] The server first stores the data received from the base station in a database. It checks the integrity of the data and filters out inaccurate or missing data.

[0393] Step 4:

[0394] The server feeds the verified data into the AI ​​module. The AI ​​analyzes the data to detect anomalies, predict environmental changes, and optimize resource exploration routes.

[0395] Step 5:

[0396] The server uses visualization tools to generate reports based on the analysis results. The reports are optimized for dashboards, and the data is visually represented using charts and maps.

[0397] Step 6:

[0398] Users log in to the provided dashboard to view the latest analysis results and forecast information. Based on this, users can quickly make necessary business decisions and implement environmental measures.

[0399] (Example 1)

[0400] 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."

[0401] Conventional marine management systems suffer from insufficient collection of environmental data and difficulties in real-time data transmission and analysis, making it challenging to predict environmental changes quickly and accurately or detect anomalies. Furthermore, there is a lack of information provided that allows users to intuitively understand the analysis results and take prompt countermeasures.

[0402] 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.

[0403] In this invention, the server includes means for collecting environmental information using a plurality of mobile devices, communication means for transmitting the information acquired from the mobile devices in real time, preprocessing means for storing and organizing the acquired information, data analysis means for analyzing the information using a generation AI model, detecting anomalies, predicting environmental changes, and proposing optimal resource exploration, and information provision means for displaying the analyzed results and transmitting warnings. This enables highly accurate prediction of environmental changes and detection of anomalies, and realizes the provision of efficient and intuitive information to the user.

[0404] A "mobile device" refers to equipment that is placed in the ocean or on the seabed to collect information in the marine environment, and is equipped with sensors to acquire data.

[0405] "Communication methods" refer to technologies and devices for transmitting environmental information from mobile devices to servers in real time, and include acoustic communication and 5G communication.

[0406] "Preprocessing means" refers to the process of data storage, organization, and imputation of missing values ​​performed on the server to prepare the received information into a format that is easy to analyze.

[0407] A "generative AI model" refers to an artificial intelligence algorithm that learns from a large amount of historical data and is capable of accurately predicting environmental changes and detecting anomalies.

[0408] "Data analysis means" refers to the process of analyzing data using an AI model based on acquired environmental information, detecting anomalies, predicting environmental changes, and proposing the optimal resource exploration route.

[0409] "Information provision means" refers to systems and devices that display the analyzed results to the user and promptly notify them of warnings as needed.

[0410] The program in this marine management system has three main components: servers, terminals, and users, each playing a specific role to make the entire system function.

[0411] The mobile terminal is deployed in a marine environment and is equipped with temperature sensors, salinity sensors, and current velocity sensors. These sensors acquire data on seawater temperature, salinity, and current velocity, and transmit this data to a server in real time. Communication uses acoustic and 5G technologies, and this combination enables stable communication with minimal data interruptions.

[0412] The server receives data sent from the terminal and stores it in a database. ETL (Extract, Transform, Load) tools are used to preprocess the received raw data, organizing it into a format suitable for analysis. Next, the server performs data analysis using a generative AI model. This generative AI model is trained on a large amount of historical ocean data, enabling highly accurate anomaly detection, environmental change prediction, and optimal resource exploration route proposals. Specifically, prompts are used to input data into the AI ​​model, generating output based on the analysis results. For example, a user could use the prompt, "Please tell me the data analysis patterns necessary to predict environmental change."

[0413] Users can view analysis results through a dedicated dashboard. The dashboard visualizes acquired data and analysis results as graphs and maps, allowing users to intuitively understand the information. Furthermore, if an anomaly is detected, the system sends a rapid notification, providing users with quick access to information to consider appropriate countermeasures.

[0414] As a concrete example, this system may be used by oil field development companies. In this case, the system proposes the optimal exploration route based on seawater environmental data and supports the rapid detection of environmental changes in the field. In this way, the present invention seamlessly handles everything from data collection and analysis to the provision of results in the marine environment, thereby realizing sustainable marine management.

[0415] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0416] Step 1:

[0417] The terminals are placed in and on the seabed and collect marine environmental data using temperature sensors, salinity sensors, and current velocity sensors. Inputs include physical characteristics (temperature, salinity, current velocity) acquired by the sensors, which are then converted into digital data. Specifically, each sensor collects data at regular intervals and stores it in a built-in data logger. The output is environmental data in digital format, which is transmitted to a communication module.

[0418] Step 2:

[0419] The terminal transmits the collected data to the server using acoustic and 5G communication technologies. The input is digital environmental data within the terminal, which is packetized according to the communication protocol. In specific operation, the data is transmitted to the base station in real time, where it is aggregated. The output is well-formed data packets received at the base station.

[0420] Step 3:

[0421] The server receives data packets transmitted from base stations and stores them in a database. The input is well-formed data packets, which are then converted into a format suitable for the database. Specifically, ETL (Extract, Transform, Load) tools are used to preprocess the data, performing necessary indexing and missing value imputation. The output is a dataset in a format optimized for analysis.

[0422] Step 4:

[0423] The server analyzes pre-processed data using a generative AI model. The input is a dataset stored in a database, which is then used for anomaly detection, environmental change prediction, and optimal resource exploration route suggestions. Specifically, the generative AI model interprets the data based on historical training data and real-time data to generate results. The output includes analysis results such as anomalies, prediction models, and recommended routes.

[0424] Step 5:

[0425] Users view the analysis results provided by the server on a dedicated dashboard. The input is the analysis results from the server, which are displayed as graphs and maps by a visualization engine. Specifically, users access the dashboard and utilize filtering and reporting functions as needed. The output is analysis results in an intuitively easy-to-understand format, including notifications to prompt necessary actions.

[0426] In this way, the system promotes the sustainable management of the marine environment through a series of processes, from data collection at terminals to analysis by servers and provision of information to users.

[0427] (Application Example 1)

[0428] 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."

[0429] There is a need to improve the operational efficiency and safety of autonomous mobile vehicles by collecting and analyzing environmental information in real time. However, existing technologies have not provided sufficient means to quickly reflect the analysis of collected data in the operation of the mobile vehicles. This invention aims to solve this problem and achieve efficient and safe optimization of the operating route.

[0430] 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.

[0431] In this invention, the server includes means for collecting environmental information using sensors mounted on a mobile vehicle, means for analyzing the information in real time and optimizing the route, and means for predicting environmental changes using a generative AI model. This enables optimization of the route and improvement of safety based on the collected data.

[0432] "Oceanographic information" refers to measurement data related to the marine environment, such as temperature, salinity, and current speed.

[0433] A "mobile device" is a machine or device that can be moved to collect data underwater and along the coast.

[0434] A "communication device" is a device that transmits data received from a mobile device to a central server in real time.

[0435] An "information analysis device" is a device that analyzes information received from a communication device to perform anomaly detection and prediction.

[0436] A "results presentation device" is a device that visualizes the results obtained through analysis and provides them to the user.

[0437] A "sensor" is a device used to measure environmental information and collect data.

[0438] A "route optimization device" is a device that optimizes the operating route of an autonomously operating mobile vehicle based on collected data and analysis results.

[0439] A "generative AI model" is an artificial intelligence technology that learns from collected ocean data to predict environmental changes and detect anomalies.

[0440] This invention provides a management system that optimizes the operation of an autonomous mobile vehicle by collecting ocean information in real time. The server collects data via various sensors mounted on the mobile vehicle. These sensors are installed to acquire data related to the marine environment, such as temperature, salinity, and current speed, in real time. The data is transmitted to the server using 5G communication equipment.

[0441] On the server, a data analysis device uses a generated AI model to analyze the received data. Specifically, it detects anomalies, predicts environmental changes, and generates optimized route instructions for moving objects. The generated AI model learns from past data to perform highly accurate predictions and analyses. The results of this analysis are visualized through a results presentation device provided to the user. The results are displayed in a dashboard format, and a graphical user interface is provided for intuitive understanding.

[0442] Furthermore, the route optimization device determines the operating route of the moving object based on the analysis results. For example, it may propose a new route that avoids areas with strong currents, thereby improving safety.

[0443] As a concrete example, when moving through an area with rapidly changing ocean currents, the generating AI detects these data fluctuations in advance and instructs the moving object to take a new route that avoids the surrounding area. This route selection ultimately ensures sustainable and safe travel.

[0444] An example of a prompt to use would be, "Based on current ocean data and vehicle location information, please suggest the safest and most efficient driving route." This prompt is presented to the generating AI model to assist in suggesting the optimal route.

[0445] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0446] Step 1:

[0447] The terminal acquires marine information. Sensors mounted on the terminal measure temperature, salinity, current speed, etc., in real time, and transmit this data to the server via a 5G communication module. The input is marine environmental sensor data, and the output is data transmission to the server.

[0448] Step 2:

[0449] The server analyzes the received data. Using a data analysis device, the server inputs the prompt message "Detect environmental anomalies based on current ocean data" into the generating AI model, and detects anomalies and environmental changes. The input is ocean data from sensors, and the output is anomaly detection results and predicted information on environmental changes.

[0450] Step 3:

[0451] The server optimizes the route based on the analysis results. The route optimization device calculates the optimal route based on the analysis results of the generated AI model and feeds this back to the terminal. The input is the analyzed environmental information, and the output is the route information of the moving object.

[0452] Step 4:

[0453] The user reviews the analysis results. The server provides the user with visualized analysis results through a user interface, making it easy to view the information in a dashboard format. The input is the analysis's predicted information and optimized route, and the output is the information display to the user.

[0454] Step 5:

[0455] The terminal continues its operation according to the new route. The terminal operates autonomously based on the optimized route provided by the server. The input is route information from the server, and the output is the operation of the mobile object along the route.

[0456] 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.

[0457] The ocean management system of the present invention not only efficiently collects and analyzes ocean data and provides the results to the user, but also incorporates an emotion engine to recognize the user's emotions and optimize the information provided. The system mainly consists of multiple mobile units for ocean data collection, a server, a user interface, and an emotion engine.

[0458] First, the mobile terminal is equipped with marine environmental sensors to collect data within a designated area. This data includes temperature, salinity, and ocean current speed. The collected data is transmitted in real time to a server via a base station.

[0459] The server stores the received data in a database and performs analysis using generating AI. This analysis includes anomaly detection, environmental change prediction, and proposal of optimal resource exploration routes. The analysis results are generated as a visual report and provided to the user's dashboard.

[0460] Furthermore, the system uses an emotion engine to recognize the user's emotions. The user's emotional status is evaluated based on their interaction with the system and data input. Based on this emotional status, the format and content of the information presented are dynamically adjusted. For example, if the user is evaluated as feeling stressed, the information is presented more concisely, and alerts are displayed that focus on the essential points.

[0461] As a concrete example, consider a researcher monitoring climate change. This researcher uses the system to acquire ocean data and receive detailed predictions of environmental change. If the emotion engine determines that the researcher's stress level is high, the necessary data is provided in an intuitively understandable highlighted format.

[0462] Thus, the marine management system of the present invention promotes the effective use of information by efficiently collecting and analyzing data, as well as individually optimizing the user experience through emotion recognition.

[0463] The following describes the processing flow.

[0464] Step 1:

[0465] The device moves through the ocean following a pre-programmed route, collecting oceanographic data using sensors. This data includes information such as temperature, salinity, and ocean current speed.

[0466] Step 2:

[0467] The terminal transmits data collected by sensors to the base station in real time. This communication uses a 5G network or acoustic communication to achieve high-speed and stable data transfer.

[0468] Step 3:

[0469] The server receives data from the base station, first verifies the data's integrity, and then stores it in the database. If any data is missing, it executes an algorithm to fill in the gaps.

[0470] Step 4:

[0471] The server analyzes the received ocean data using a generative AI model. The analysis process includes detecting anomalous data points, predicting future environmental changes, and calculating the optimal resource exploration route.

[0472] Step 5:

[0473] The server inputs the analysis results into the emotion engine and optimizes the information provided to reflect the user's emotional state. The emotion engine estimates emotions from the user's recent interaction data.

[0474] Step 6:

[0475] The server sends the generated report to the user in dashboard format. The way information is presented and the points emphasized are adjusted according to the user's emotional state. For example, if the user is determined to be confused, the information is visualized and the most important data is summarized.

[0476] Step 7:

[0477] Users review the information provided through the dashboard and make decisions as needed, including environmental measures and adjustments to resource exploration. User feedback is used to improve the system's future sentiment recognition accuracy.

[0478] (Example 2)

[0479] 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."

[0480] There is a challenge in efficiently collecting and analyzing marine data, while simultaneously lacking methods and technologies to optimize information delivery based on user emotions. Current systems fail to adequately present large amounts of data to users, and information delivery does not take into account user emotions or stress levels, making effective decision-making difficult.

[0481] 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.

[0482] In this invention, the server includes multiple mobile means for collecting ocean data, emotion recognition means for recognizing the user's emotions, and data analysis means for analyzing the data using a generative AI model. This enables not only the collection and analysis of ocean data, but also the provision of optimal information tailored to the user's emotions.

[0483] "Oceanic data" refers to information about the marine environment, including indicators such as temperature, salinity, and ocean current speed.

[0484] A "mobile device" refers to a device equipped with sensors that collects ocean data and moves within a designated area.

[0485] "Communication means" refers to the technology or method for transmitting data obtained from a mobile device to a server in real time.

[0486] "Data analysis methods" refer to technologies used to analyze collected data and detect anomalies or predict environmental changes.

[0487] "Emotion recognition means" refers to technology that evaluates the user's emotions and optimizes information provision based on that information.

[0488] "Information provision means" refers to the methods and interfaces used to present the analyzed results to the user.

[0489] A "generative AI model" refers to a model that uses AI technology to perform data analysis, particularly for predicting and proposing environmental changes.

[0490] This invention is a system that collects data on the marine environment and provides the analysis results to the user. Furthermore, it aims to recognize the user's emotions and optimize the information provided in accordance with those emotions.

[0491] The terminal includes a mobile unit equipped with marine environmental sensors. This unit moves through a designated ocean area, collecting data such as temperature, salinity, and ocean current speed in real time. The collected data is immediately transmitted to a server, allowing for analysis based on the latest information. Standard wireless communication technology is used for this communication.

[0492] The server stores the received data in a database and performs analysis using a generative AI model. During the analysis, prompts are used to detect anomalies, predict environmental changes, and propose resource exploration routes. The AI ​​model employs an open-source machine learning framework and is customized as needed. Specific analysis methods include anomaly detection algorithms and time-series analysis.

[0493] Furthermore, the server utilizes an emotion engine to recognize the user's emotions. This allows the system to evaluate the user's emotional status based on their interactions and inputs, and adjust the information provided based on the results. The way information is presented is tailored to the user's stress level, becoming more concise or highlighting important information clearly.

[0494] As a concrete example, consider a researcher conducting marine research who uses this system. This researcher receives environmental change predictions from a generative AI model based on marine data collected by a mobile device, and uses this information to inform their research. In addition, if the emotion engine determines that stress levels are high, important information is highlighted and presented in an easy-to-understand manner.

[0495] An example of a prompt to be input to the generating AI model is: "Analyze the water temperature and salinity data for the specified sea area and predict environmental changes by comparing them to the same period last year. Also, consider how to present information concisely if the user is feeling stressed."

[0496] Thus, the present invention provides a means for reliably collecting marine environmental data and providing flexible information tailored to the user's situation.

[0497] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0498] Step 1:

[0499] The terminal uses a mobile device equipped with marine environmental sensors to collect data from a designated sea area. Specifically, it obtains measurements such as temperature, salinity, and ocean current speed through the sensors. This data is compiled in real time by the mobile device's control system. The input is the sensor values, and the output is consistent data collected in real time.

[0500] Step 2:

[0501] The terminal transmits the data collected in Step 1 to the server using a communication method. The data is sent to a base station using common wireless communication technology and then securely transferred to the server. The input is consistent data, and the output is the data sent to the server.

[0502] Step 3:

[0503] The server stores the data received in step 2 into the database. Simultaneously, it checks for duplicates and missing data and performs data cleaning. The input is the received data, and the output is the cleaned database entry. This process forms the foundation for the subsequent data analysis stage.

[0504] Step 4:

[0505] The server applies the generated AI model to the data cleaned in step 3 and performs analysis. Specifically, it utilizes prompts to perform anomaly detection, predict environmental changes, and propose resource exploration routes. In this analysis, the AI ​​model performs pattern recognition and time series analysis. The input is the cleaned data, and the output is the analysis results.

[0506] Step 5:

[0507] The server evaluates the user's emotions through emotion recognition mechanisms. Based on user interactions and input data, the emotion engine measures stress levels and levels of interest. Input is the user's behavioral history and input data, and output is the evaluation result of the emotional state.

[0508] Step 6:

[0509] The server determines the format of information delivery based on the analysis results in step 4 and the sentiment assessment in step 5. It adjusts the data presentation method according to the user's emotional state, emphasizing concise and important information when stress levels are high. The input is the analysis results and sentiment assessment, and the output is the adjusted information delivery. This allows the user to receive detailed and effective information relevant to their situation.

[0510] (Application Example 2)

[0511] 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."

[0512] In logistics operations, improving time efficiency and accuracy is essential, but conventional systems are slow to transmit and analyze data, making real-time optimization difficult. Furthermore, employee emotional states affect work efficiency, but there is a lack of information provision methods that take this into account.

[0513] 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.

[0514] In this invention, the server includes means for controlling multiple mobile objects that collect data, means for receiving data obtained from the mobile objects in real time, and means for analyzing the received data. This streamlines data collection in logistics operations and enables the provision of optimal work instructions based on data analysis in real time. Furthermore, the emotion analysis means enables the provision of information tailored to the emotional state of employees, thereby improving work efficiency and employee satisfaction.

[0515] "Multiple mobile devices for data collection" refers to a collection of portable measurement units used to efficiently gather necessary information in an environment or work site.

[0516] "Communication means" refers to wireless or wired data transmission devices used to transfer data acquired from a mobile device to a server or processing device in real time.

[0517] "Data analysis means" refers to software or algorithms used to analyze received data and extract or predict necessary information.

[0518] "Information provision means" refers to an interface or device for displaying analyzed data in a format that is easy for users to understand.

[0519] "Emotional analysis means" refers to sensors and analytical algorithms used to evaluate a user's emotional state and optimize the content of the information provided.

[0520] The system for realizing this invention consists of multiple mobile units for data collection, a server equipped with communication technology, an application for data analysis, a user interface for providing information, and an engine for performing sentiment analysis.

[0521] The server utilizes high-speed communication technology to receive data acquired from each mobile device in real time. The received data is processed by a data analysis application, which extracts necessary information using conventional analysis algorithms and machine learning techniques. In this process, Python and its libraries, such as TensorFlow, are commonly used as programming languages ​​and generative AI models. The analysis results are displayed as a visual dashboard in the user interface. Specifically, information necessary for improving work efficiency and optimizing inventory management is displayed.

[0522] Furthermore, the server uses an emotion analysis engine to estimate the user's emotional state from their voice and physical data, and adjusts the content and format of the information presented based on the results. For this purpose, a device with a built-in microphone and camera sensor is used. If the user's stress level is determined to be high, the system is designed to reduce the user's burden by selectively displaying only concise and important information.

[0523] As a concrete example, consider a scenario where a new employee is performing their first tasks at a logistics center. The server displays work instructions on a glasses-type device, and when the emotion analysis engine detects the new employee's anxiety, it suggests simplifying the information and providing it in stages to facilitate understanding. An example of a prompt to the generative AI model in this case might be, "Please suggest efficient instructions for a new employee."

[0524] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0525] Step 1:

[0526] The terminal collects data from the environment using sensors mounted on a mobile device. This data includes inventory status and pallet location information within the logistics center. Raw data from the sensors is the input, and this data is sent to the server in a formatted form via a communication means as the output.

[0527] Step 2:

[0528] The server receives collected data from terminals in real time. It receives formatted data from terminals as input and stores it in a database for analysis. After performing preprocessing such as data organization and adding timestamps, it provides the prepared data to the analysis platform as output.

[0529] Step 3:

[0530] The server analyzes the received data using a data analysis application. During this process, it runs a generative AI model using libraries such as TensorFlow from Python. The input is the data prepared in step 2, and the output provides insights useful for optimizing inventory management and improving logistics efficiency. Specific operations include anomaly detection and real-time demand forecasting.

[0531] Step 4:

[0532] Information for the user is provided to the user interface by the server. The input is the analysis results generated in step 3, and the output is route information and work instructions displayed on the employee's glasses-type display. Specifically, a visual dashboard is generated and customized to the user's needs.

[0533] Step 5:

[0534] The emotion analysis engine built into the device monitors the user's voice data and behavior to evaluate their emotional state. Inputs include sensor data from the microphone and camera, and output is generated representing the user's emotional state, such as their stress level. Specifically, an algorithm operates to infer emotions from voice tone and facial expressions.

[0535] Step 6:

[0536] The server dynamically adjusts the information provided based on the results of the emotion analysis engine. The input is the emotional state obtained in step 5, and the output is information modified to suit the user's state, which is then displayed on the screen. Specifically, this involves selecting information of high importance and changing the presentation style.

[0537] 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.

[0538] 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.

[0539] 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.

[0540] [Fourth Embodiment]

[0541] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0542] 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.

[0543] 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).

[0544] 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.

[0545] 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.

[0546] 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).

[0547] 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.

[0548] 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.

[0549] 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.

[0550] 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.

[0551] 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.

[0552] 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.

[0553] 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".

[0554] The ocean management system of the present invention consists of numerous mobile units and servers and users that interact with these mobile units. The main objective of the system is to achieve sustainable ocean management by efficiently collecting and analyzing ocean data.

[0555] First, the mobile terminals are deployed in and on the seabed, collecting various marine data using sensors. This data includes seawater temperature, salinity, and current speed. The collected data is transmitted to a server via a base station. Acoustic communication and the latest 5G communication technologies are used for this process, enabling real-time data transmission.

[0556] The server stores the received ocean data in a database and performs analysis using generative AI. This analysis process includes automatic detection of anomalies, prediction of environmental changes, and proposal of optimal resource exploration routes. The generative AI learns from vast amounts of historical data to perform highly accurate analysis.

[0557] The analysis results are provided to the user. Users can view the analysis results and predictive information on a dedicated dashboard. This dashboard visualizes the collected data in graphs and maps for intuitive understanding. In addition, if an anomaly is detected, the user will be notified promptly and provided with information to consider necessary countermeasures.

[0558] For example, if an oil field development company uses this system, the optimal oil field exploration route will be proposed based on oceanographic data, and anomalies in the water temperature and salinity at the site will be detected quickly. This will enable efficient resource development while minimizing the impact on the environment.

[0559] Thus, the marine management system according to the present invention realizes sustainable management of the marine environment through real-time analysis of collected data and efficient information provision.

[0560] The following describes the processing flow.

[0561] Step 1:

[0562] When the device reaches a designated ocean area, it activates its built-in sensors to measure surrounding ocean data. This data includes temperature, salinity, and ocean current speed.

[0563] Step 2:

[0564] The terminal processes the measured data in real time and transmits it to the underwater base station via acoustic communication or a 5G network. The base station aggregates this data and transfers it to a server.

[0565] Step 3:

[0566] The server first stores the data received from the base station in a database. It checks the integrity of the data and filters out inaccurate or missing data.

[0567] Step 4:

[0568] The server feeds the verified data into the AI ​​module. The AI ​​analyzes the data to detect anomalies, predict environmental changes, and optimize resource exploration routes.

[0569] Step 5:

[0570] The server uses visualization tools to generate reports based on the analysis results. The reports are optimized for dashboards, and the data is visually represented using charts and maps.

[0571] Step 6:

[0572] Users log in to the provided dashboard to view the latest analysis results and forecast information. Based on this, users can quickly make necessary business decisions and implement environmental measures.

[0573] (Example 1)

[0574] 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".

[0575] Conventional marine management systems suffer from insufficient collection of environmental data and difficulties in real-time data transmission and analysis, making it challenging to predict environmental changes quickly and accurately or detect anomalies. Furthermore, there is a lack of information provided that allows users to intuitively understand the analysis results and take prompt countermeasures.

[0576] 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.

[0577] In this invention, the server includes means for collecting environmental information using a plurality of mobile devices, communication means for transmitting the information acquired from the mobile devices in real time, preprocessing means for storing and organizing the acquired information, data analysis means for analyzing the information using a generation AI model, detecting anomalies, predicting environmental changes, and proposing optimal resource exploration, and information provision means for displaying the analyzed results and transmitting warnings. This enables highly accurate prediction of environmental changes and detection of anomalies, and realizes the provision of efficient and intuitive information to the user.

[0578] A "mobile device" refers to equipment that is placed in the ocean or on the seabed to collect information in the marine environment, and is equipped with sensors to acquire data.

[0579] "Communication methods" refer to technologies and devices for transmitting environmental information from mobile devices to servers in real time, and include acoustic communication and 5G communication.

[0580] "Preprocessing means" refers to the process of data storage, organization, and imputation of missing values ​​performed on the server to prepare the received information into a format that is easy to analyze.

[0581] A "generative AI model" refers to an artificial intelligence algorithm that learns from a large amount of historical data and is capable of accurately predicting environmental changes and detecting anomalies.

[0582] "Data analysis means" refers to the process of analyzing data using an AI model based on acquired environmental information, detecting anomalies, predicting environmental changes, and proposing the optimal resource exploration route.

[0583] "Information provision means" refers to systems and devices that display the analyzed results to the user and promptly notify them of warnings as needed.

[0584] The program in this marine management system has three main components: servers, terminals, and users, each playing a specific role to make the entire system function.

[0585] The mobile terminal is deployed in a marine environment and is equipped with temperature sensors, salinity sensors, and current velocity sensors. These sensors acquire data on seawater temperature, salinity, and current velocity, and transmit this data to a server in real time. Communication uses acoustic and 5G technologies, and this combination enables stable communication with minimal data interruptions.

[0586] The server receives data sent from the terminal and stores it in a database. ETL (Extract, Transform, Load) tools are used to preprocess the received raw data, organizing it into a format suitable for analysis. Next, the server performs data analysis using a generative AI model. This generative AI model is trained on a large amount of historical ocean data, enabling highly accurate anomaly detection, environmental change prediction, and optimal resource exploration route proposals. Specifically, prompts are used to input data into the AI ​​model, generating output based on the analysis results. For example, a user could use the prompt, "Please tell me the data analysis patterns necessary to predict environmental change."

[0587] Users can view analysis results through a dedicated dashboard. The dashboard visualizes acquired data and analysis results as graphs and maps, allowing users to intuitively understand the information. Furthermore, if an anomaly is detected, the system sends a rapid notification, providing users with quick access to information to consider appropriate countermeasures.

[0588] As a concrete example, this system may be used by oil field development companies. In this case, the system proposes the optimal exploration route based on seawater environmental data and supports the rapid detection of environmental changes in the field. In this way, the present invention seamlessly handles everything from data collection and analysis to the provision of results in the marine environment, thereby realizing sustainable marine management.

[0589] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0590] Step 1:

[0591] The terminals are placed in and on the seabed and collect marine environmental data using temperature sensors, salinity sensors, and current velocity sensors. Inputs include physical characteristics (temperature, salinity, current velocity) acquired by the sensors, which are then converted into digital data. Specifically, each sensor collects data at regular intervals and stores it in a built-in data logger. The output is environmental data in digital format, which is transmitted to a communication module.

[0592] Step 2:

[0593] The terminal transmits the collected data to the server using acoustic and 5G communication technologies. The input is digital environmental data within the terminal, which is packetized according to the communication protocol. In specific operation, the data is transmitted to the base station in real time, where it is aggregated. The output is well-formed data packets received at the base station.

[0594] Step 3:

[0595] The server receives data packets transmitted from base stations and stores them in a database. The input is well-formed data packets, which are then converted into a format suitable for the database. Specifically, ETL (Extract, Transform, Load) tools are used to preprocess the data, performing necessary indexing and missing value imputation. The output is a dataset in a format optimized for analysis.

[0596] Step 4:

[0597] The server analyzes pre-processed data using a generative AI model. The input is a dataset stored in a database, which is then used for anomaly detection, environmental change prediction, and optimal resource exploration route suggestions. Specifically, the generative AI model interprets the data based on historical training data and real-time data to generate results. The output includes analysis results such as anomalies, prediction models, and recommended routes.

[0598] Step 5:

[0599] Users view the analysis results provided by the server on a dedicated dashboard. The input is the analysis results from the server, which are displayed as graphs and maps by a visualization engine. Specifically, users access the dashboard and utilize filtering and reporting functions as needed. The output is analysis results in an intuitively easy-to-understand format, including notifications to prompt necessary actions.

[0600] In this way, the system promotes the sustainable management of the marine environment through a series of processes, from data collection at terminals to analysis by servers and provision of information to users.

[0601] (Application Example 1)

[0602] 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".

[0603] There is a need to improve the operational efficiency and safety of autonomous mobile vehicles by collecting and analyzing environmental information in real time. However, existing technologies have not provided sufficient means to quickly reflect the analysis of collected data in the operation of the mobile vehicles. This invention aims to solve this problem and achieve efficient and safe optimization of the operating route.

[0604] 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.

[0605] In this invention, the server includes means for collecting environmental information using sensors mounted on a mobile vehicle, means for analyzing the information in real time and optimizing the route, and means for predicting environmental changes using a generative AI model. This enables optimization of the route and improvement of safety based on the collected data.

[0606] "Oceanographic information" refers to measurement data related to the marine environment, such as temperature, salinity, and current speed.

[0607] A "mobile device" is a machine or device that can be moved to collect data underwater and along the coast.

[0608] A "communication device" is a device that transmits data received from a mobile device to a central server in real time.

[0609] An "information analysis device" is a device that analyzes information received from a communication device to perform anomaly detection and prediction.

[0610] A "results presentation device" is a device that visualizes the results obtained through analysis and provides them to the user.

[0611] A "sensor" is a device used to measure environmental information and collect data.

[0612] A "route optimization device" is a device that optimizes the operating route of an autonomously operating mobile vehicle based on collected data and analysis results.

[0613] A "generative AI model" is an artificial intelligence technology that learns from collected ocean data to predict environmental changes and detect anomalies.

[0614] This invention provides a management system that optimizes the operation of an autonomous mobile vehicle by collecting ocean information in real time. The server collects data via various sensors mounted on the mobile vehicle. These sensors are installed to acquire data related to the marine environment, such as temperature, salinity, and current speed, in real time. The data is transmitted to the server using 5G communication equipment.

[0615] On the server, a data analysis device uses a generated AI model to analyze the received data. Specifically, it detects anomalies, predicts environmental changes, and generates optimized route instructions for moving objects. The generated AI model learns from past data to perform highly accurate predictions and analyses. The results of this analysis are visualized through a results presentation device provided to the user. The results are displayed in a dashboard format, and a graphical user interface is provided for intuitive understanding.

[0616] Furthermore, the route optimization device determines the operating route of the moving object based on the analysis results. For example, it may propose a new route that avoids areas with strong currents, thereby improving safety.

[0617] As a concrete example, when moving through an area with rapidly changing ocean currents, the generating AI detects these data fluctuations in advance and instructs the moving object to take a new route that avoids the surrounding area. This route selection ultimately ensures sustainable and safe travel.

[0618] An example of a prompt to use would be, "Based on current ocean data and vehicle location information, please suggest the safest and most efficient driving route." This prompt is presented to the generating AI model to assist in suggesting the optimal route.

[0619] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0620] Step 1:

[0621] The terminal acquires marine information. Sensors mounted on the terminal measure temperature, salinity, current speed, etc., in real time, and transmit this data to the server via a 5G communication module. The input is marine environmental sensor data, and the output is data transmission to the server.

[0622] Step 2:

[0623] The server analyzes the received data. Using a data analysis device, the server inputs the prompt message "Detect environmental anomalies based on current ocean data" into the generating AI model, and detects anomalies and environmental changes. The input is ocean data from sensors, and the output is anomaly detection results and predicted information on environmental changes.

[0624] Step 3:

[0625] The server optimizes the route based on the analysis results. The route optimization device calculates the optimal route based on the analysis results of the generated AI model and feeds this back to the terminal. The input is the analyzed environmental information, and the output is the route information of the moving object.

[0626] Step 4:

[0627] The user reviews the analysis results. The server provides the user with visualized analysis results through a user interface, making it easy to view the information in a dashboard format. The input is the analysis's predicted information and optimized route, and the output is the information display to the user.

[0628] Step 5:

[0629] The terminal continues its operation according to the new route. The terminal operates autonomously based on the optimized route provided by the server. The input is route information from the server, and the output is the operation of the mobile object along the route.

[0630] 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.

[0631] The ocean management system of the present invention not only efficiently collects and analyzes ocean data and provides the results to the user, but also incorporates an emotion engine to recognize the user's emotions and optimize the information provided. The system mainly consists of multiple mobile units for ocean data collection, a server, a user interface, and an emotion engine.

[0632] First, the mobile terminal is equipped with marine environmental sensors to collect data within a designated area. This data includes temperature, salinity, and ocean current speed. The collected data is transmitted in real time to a server via a base station.

[0633] The server stores the received data in a database and performs analysis using generating AI. This analysis includes anomaly detection, environmental change prediction, and proposal of optimal resource exploration routes. The analysis results are generated as a visual report and provided to the user's dashboard.

[0634] Furthermore, the system uses an emotion engine to recognize the user's emotions. The user's emotional status is evaluated based on their interaction with the system and data input. Based on this emotional status, the format and content of the information presented are dynamically adjusted. For example, if the user is evaluated as feeling stressed, the information is presented more concisely, and alerts are displayed that focus on the essential points.

[0635] As a concrete example, consider a researcher monitoring climate change. This researcher uses the system to acquire ocean data and receive detailed predictions of environmental change. If the emotion engine determines that the researcher's stress level is high, the necessary data is provided in an intuitively understandable highlighted format.

[0636] Thus, the marine management system of the present invention promotes the effective use of information by efficiently collecting and analyzing data, as well as individually optimizing the user experience through emotion recognition.

[0637] The following describes the processing flow.

[0638] Step 1:

[0639] The device moves through the ocean following a pre-programmed route, collecting oceanographic data using sensors. This data includes information such as temperature, salinity, and ocean current speed.

[0640] Step 2:

[0641] The terminal transmits data collected by sensors to the base station in real time. This communication uses a 5G network or acoustic communication to achieve high-speed and stable data transfer.

[0642] Step 3:

[0643] The server receives data from the base station, first verifies the data's integrity, and then stores it in the database. If any data is missing, it executes an algorithm to fill in the gaps.

[0644] Step 4:

[0645] The server analyzes the received ocean data using a generative AI model. The analysis process includes detecting anomalous data points, predicting future environmental changes, and calculating the optimal resource exploration route.

[0646] Step 5:

[0647] The server inputs the analysis results into the emotion engine and optimizes the information provided to reflect the user's emotional state. The emotion engine estimates emotions from the user's recent interaction data.

[0648] Step 6:

[0649] The server sends the generated report to the user in dashboard format. The way information is presented and the points emphasized are adjusted according to the user's emotional state. For example, if the user is determined to be confused, the information is visualized and the most important data is summarized.

[0650] Step 7:

[0651] Users review the information provided through the dashboard and make decisions as needed, including environmental measures and adjustments to resource exploration. User feedback is used to improve the system's future sentiment recognition accuracy.

[0652] (Example 2)

[0653] 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".

[0654] There is a challenge in efficiently collecting and analyzing marine data, while simultaneously lacking methods and technologies to optimize information delivery based on user emotions. Current systems fail to adequately present large amounts of data to users, and information delivery does not take into account user emotions or stress levels, making effective decision-making difficult.

[0655] 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.

[0656] In this invention, the server includes multiple mobile means for collecting ocean data, emotion recognition means for recognizing the user's emotions, and data analysis means for analyzing the data using a generative AI model. This enables not only the collection and analysis of ocean data, but also the provision of optimal information tailored to the user's emotions.

[0657] "Oceanic data" refers to information about the marine environment, including indicators such as temperature, salinity, and ocean current speed.

[0658] A "mobile device" refers to a device equipped with sensors that collects ocean data and moves within a designated area.

[0659] "Communication means" refers to the technology or method for transmitting data obtained from a mobile device to a server in real time.

[0660] "Data analysis methods" refer to technologies used to analyze collected data and detect anomalies or predict environmental changes.

[0661] "Emotion recognition means" refers to technology that evaluates the user's emotions and optimizes information provision based on that information.

[0662] "Information provision means" refers to the methods and interfaces used to present the analyzed results to the user.

[0663] A "generative AI model" refers to a model that uses AI technology to perform data analysis, particularly for predicting and proposing environmental changes.

[0664] This invention is a system that collects data on the marine environment and provides the analysis results to the user. Furthermore, it aims to recognize the user's emotions and optimize the information provided in accordance with those emotions.

[0665] The terminal includes a mobile unit equipped with marine environmental sensors. This unit moves through a designated ocean area, collecting data such as temperature, salinity, and ocean current speed in real time. The collected data is immediately transmitted to a server, allowing for analysis based on the latest information. Standard wireless communication technology is used for this communication.

[0666] The server stores the received data in a database and performs analysis using a generative AI model. During the analysis, prompts are used to detect anomalies, predict environmental changes, and propose resource exploration routes. The AI ​​model employs an open-source machine learning framework and is customized as needed. Specific analysis methods include anomaly detection algorithms and time-series analysis.

[0667] Furthermore, the server utilizes an emotion engine to recognize the user's emotions. This allows the system to evaluate the user's emotional status based on their interactions and inputs, and adjust the information provided based on the results. The way information is presented is tailored to the user's stress level, becoming more concise or highlighting important information clearly.

[0668] As a concrete example, consider a researcher conducting marine research who uses this system. This researcher receives environmental change predictions from a generative AI model based on marine data collected by a mobile device, and uses this information to inform their research. In addition, if the emotion engine determines that stress levels are high, important information is highlighted and presented in an easy-to-understand manner.

[0669] An example of a prompt to be input to the generating AI model is: "Analyze the water temperature and salinity data for the specified sea area and predict environmental changes by comparing them to the same period last year. Also, consider how to present information concisely if the user is feeling stressed."

[0670] Thus, the present invention provides a means for reliably collecting marine environmental data and providing flexible information tailored to the user's situation.

[0671] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0672] Step 1:

[0673] The terminal uses a mobile device equipped with marine environmental sensors to collect data from a designated sea area. Specifically, it obtains measurements such as temperature, salinity, and ocean current speed through the sensors. This data is compiled in real time by the mobile device's control system. The input is the sensor values, and the output is consistent data collected in real time.

[0674] Step 2:

[0675] The terminal transmits the data collected in Step 1 to the server using a communication method. The data is sent to a base station using common wireless communication technology and then securely transferred to the server. The input is consistent data, and the output is the data sent to the server.

[0676] Step 3:

[0677] The server stores the data received in step 2 into the database. Simultaneously, it checks for duplicates and missing data and performs data cleaning. The input is the received data, and the output is the cleaned database entry. This process forms the foundation for the subsequent data analysis stage.

[0678] Step 4:

[0679] The server applies the generated AI model to the data cleaned in step 3 and performs analysis. Specifically, it utilizes prompts to perform anomaly detection, predict environmental changes, and propose resource exploration routes. In this analysis, the AI ​​model performs pattern recognition and time series analysis. The input is the cleaned data, and the output is the analysis results.

[0680] Step 5:

[0681] The server evaluates the user's emotions through emotion recognition mechanisms. Based on user interactions and input data, the emotion engine measures stress levels and levels of interest. Input is the user's behavioral history and input data, and output is the evaluation result of the emotional state.

[0682] Step 6:

[0683] The server determines the format of information delivery based on the analysis results in step 4 and the sentiment assessment in step 5. It adjusts the data presentation method according to the user's emotional state, emphasizing concise and important information when stress levels are high. The input is the analysis results and sentiment assessment, and the output is the adjusted information delivery. This allows the user to receive detailed and effective information relevant to their situation.

[0684] (Application Example 2)

[0685] 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".

[0686] In logistics operations, improving time efficiency and accuracy is essential, but conventional systems are slow to transmit and analyze data, making real-time optimization difficult. Furthermore, employee emotional states affect work efficiency, but there is a lack of information provision methods that take this into account.

[0687] 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.

[0688] In this invention, the server includes means for controlling multiple mobile objects that collect data, means for receiving data obtained from the mobile objects in real time, and means for analyzing the received data. This streamlines data collection in logistics operations and enables the provision of optimal work instructions based on data analysis in real time. Furthermore, the emotion analysis means enables the provision of information tailored to the emotional state of employees, thereby improving work efficiency and employee satisfaction.

[0689] "Multiple mobile devices for data collection" refers to a collection of portable measurement units used to efficiently gather necessary information in an environment or work site.

[0690] "Communication means" refers to wireless or wired data transmission devices used to transfer data acquired from a mobile device to a server or processing device in real time.

[0691] "Data analysis means" refers to software or algorithms used to analyze received data and extract or predict necessary information.

[0692] "Information provision means" refers to an interface or device for displaying analyzed data in a format that is easy for users to understand.

[0693] "Emotional analysis means" refers to sensors and analytical algorithms used to evaluate a user's emotional state and optimize the content of the information provided.

[0694] The system for realizing this invention consists of multiple mobile units for data collection, a server equipped with communication technology, an application for data analysis, a user interface for providing information, and an engine for performing sentiment analysis.

[0695] The server utilizes high-speed communication technology to receive data acquired from each mobile device in real time. The received data is processed by a data analysis application, which extracts necessary information using conventional analysis algorithms and machine learning techniques. In this process, Python and its libraries, such as TensorFlow, are commonly used as programming languages ​​and generative AI models. The analysis results are displayed as a visual dashboard in the user interface. Specifically, information necessary for improving work efficiency and optimizing inventory management is displayed.

[0696] Furthermore, the server uses an emotion analysis engine to estimate the user's emotional state from their voice and physical data, and adjusts the content and format of the information presented based on the results. For this purpose, a device with a built-in microphone and camera sensor is used. If the user's stress level is determined to be high, the system is designed to reduce the user's burden by selectively displaying only concise and important information.

[0697] As a concrete example, consider a scenario where a new employee is performing their first tasks at a logistics center. The server displays work instructions on a glasses-type device, and when the emotion analysis engine detects the new employee's anxiety, it suggests simplifying the information and providing it in stages to facilitate understanding. An example of a prompt to the generative AI model in this case might be, "Please suggest efficient instructions for a new employee."

[0698] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0699] Step 1:

[0700] The terminal collects data from the environment using sensors mounted on a mobile device. This data includes inventory status and pallet location information within the logistics center. Raw data from the sensors is the input, and this data is sent to the server in a formatted form via a communication means as the output.

[0701] Step 2:

[0702] The server receives collected data from terminals in real time. It receives formatted data from terminals as input and stores it in a database for analysis. After performing preprocessing such as data organization and adding timestamps, it provides the prepared data to the analysis platform as output.

[0703] Step 3:

[0704] The server analyzes the received data using a data analysis application. During this process, it runs a generative AI model using libraries such as TensorFlow from Python. The input is the data prepared in step 2, and the output provides insights useful for optimizing inventory management and improving logistics efficiency. Specific operations include anomaly detection and real-time demand forecasting.

[0705] Step 4:

[0706] Information for the user is provided to the user interface by the server. The input is the analysis results generated in step 3, and the output is route information and work instructions displayed on the employee's glasses-type display. Specifically, a visual dashboard is generated and customized to the user's needs.

[0707] Step 5:

[0708] The emotion analysis engine built into the device monitors the user's voice data and behavior to evaluate their emotional state. Inputs include sensor data from the microphone and camera, and output is generated representing the user's emotional state, such as their stress level. Specifically, an algorithm operates to infer emotions from voice tone and facial expressions.

[0709] Step 6:

[0710] The server dynamically adjusts the information provided based on the results of the emotion analysis engine. The input is the emotional state obtained in step 5, and the output is information modified to suit the user's state, which is then displayed on the screen. Specifically, this involves selecting information of high importance and changing the presentation style.

[0711] 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.

[0712] 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.

[0713] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] 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."

[0720] 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.

[0721] 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.

[0722] 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.

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] The following is further disclosed regarding the embodiments described above.

[0733] (Claim 1)

[0734] Multiple mobile devices that collect ocean data,

[0735] A communication means for receiving data obtained from the aforementioned mobile body in real time,

[0736] A data analysis means for analyzing the received data,

[0737] Information provision means for presenting the results of the aforementioned analysis,

[0738] A marine management system including

[0739] (Claim 2)

[0740] The marine management system according to claim 1, wherein the plurality of mobile bodies are equipped with sensors for measuring marine environmental data.

[0741] (Claim 3)

[0742] The ocean management system according to claim 1, wherein the data analysis means predicts environmental changes using a generative AI model.

[0743] "Example 1"

[0744] (Claim 1)

[0745] A means of collecting environmental information using multiple mobile devices,

[0746] A communication means for transmitting information acquired from the aforementioned mobile device in real time,

[0747] A preprocessing means for storing and organizing the acquired information,

[0748] A data analysis means that analyzes the aforementioned information using a generative AI model, detects anomalies, predicts environmental changes, and proposes optimal resource exploration,

[0749] Information provision means that displays the analyzed results and sends a warning,

[0750] A system that includes this.

[0751] (Claim 2)

[0752] The system according to claim 1, wherein the plurality of mobile devices include sensors for measuring environmental information.

[0753] (Claim 3)

[0754] The system according to claim 1, wherein the data analysis means learns a large amount of past information to form a predictive model.

[0755] "Application Example 1"

[0756] (Claim 1)

[0757] Multiple mobile devices for acquiring marine information,

[0758] A communication device that receives information obtained from the aforementioned mobile device in real time,

[0759] An information analysis device for analyzing the received information,

[0760] A results presentation device that provides the results of the analysis,

[0761] Using sensors mounted on the mobile vehicle, environmental information is collected.

[0762] A route optimization device that optimizes the route based on the analysis results,

[0763] A management system that includes this.

[0764] (Claim 2)

[0765] The management system according to claim 1, wherein the plurality of mobile devices are equipped with sensors for measuring environmental information.

[0766] (Claim 3)

[0767] The management system according to claim 1, wherein the information analysis device predicts environmental changes using a generated AI model.

[0768] "Example 2 of combining an emotion engine"

[0769] (Claim 1)

[0770] Multiple mobile devices that collect ocean data,

[0771] A communication means for receiving data obtained from the aforementioned mobile body in real time,

[0772] A data analysis means for analyzing the received data,

[0773] An emotion recognition method that recognizes user emotions and optimizes information provision,

[0774] Information provision means for presenting the results of the aforementioned analysis,

[0775] A system that includes this.

[0776] (Claim 2)

[0777] The system according to claim 1, wherein the plurality of mobile bodies are equipped with sensors for measuring marine environmental data.

[0778] (Claim 3)

[0779] The system according to claim 1, wherein the data analysis means predicts environmental changes using a generative AI model.

[0780] "Application example 2 when combining with an emotional engine"

[0781] (Claim 1)

[0782] Multiple mobile devices that collect data,

[0783] A communication means for receiving data obtained from the aforementioned mobile body in real time,

[0784] A data analysis means for analyzing the received data,

[0785] Information provision means for presenting the results of the aforementioned analysis,

[0786] A sentiment analysis method that evaluates user emotions and optimizes information provision,

[0787] A system that includes this.

[0788] (Claim 2)

[0789] The system according to claim 1, wherein the plurality of moving bodies are equipped with sensors for measuring environmental data.

[0790] (Claim 3)

[0791] The system according to claim 1, wherein the data analysis means predicts changes in the situation using a generative AI model. [Explanation of Symbols]

[0792] 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. Multiple mobile devices that collect ocean data, A communication means for receiving data obtained from the aforementioned mobile body in real time, A data analysis means for analyzing the received data, Information provision means for presenting the results of the aforementioned analysis, A marine management system including

2. The marine management system according to claim 1, wherein the plurality of mobile bodies are equipped with sensors for measuring marine environmental data.

3. The ocean management system according to claim 1, wherein the data analysis means predicts environmental changes using a generative AI model.

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