system

Abstract

We provide the system. [Solution] A means for collecting behavioral and environmental data of aquatic organisms, A means of preprocessing collected data to generate an integrated dataset, A means for executing an AI algorithm that analyzes the behavioral patterns of aquatic organisms using an integrated dataset, A means for generating environmentally friendly fishing guidelines based on analysis results, A means of distributing the generated guidelines to the user's terminal, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] It is difficult to sufficiently grasp the behavior patterns and habitat changes of aquatic organisms by conventional methods. Therefore, there are problems that it is difficult to formulate sustainable fishing activities and biological conservation measures considering the impact on the environment. In addition, the lack of detailed data analysis for effective protection activities with limited resources is also a problem. By solving these problems, it is required to enhance the sustainability of the marine ecosystem.

Means for Solving the Problems

[0005] This invention provides a system that effectively collects behavioral and environmental data of aquatic organisms, preprocesses and integrates the collected data, and analyzes the behavioral patterns of aquatic organisms using advanced AI algorithms. Based on the analysis results, it automatically generates environmentally conscious fisheries guidelines and distributes them to users. This system makes it possible to analyze the behavior of aquatic organisms in detail and efficiently formulate sustainable fisheries activities and conservation measures.

[0006] "Aquatic organisms" is a general term for organisms that live and inhabit water, and includes a diverse range of species such as fish, mollusks, and crustaceans.

[0007] "Behavioral data" refers to information related to the behavior of a particular aquatic organism, such as its movement, activity patterns, and location.

[0008] "Environmental data" refers to information that indicates the conditions of the environment in which aquatic organisms live, such as water temperature, salinity, and water flow, and is used to understand the effects these have on living organisms.

[0009] "Preprocessing" refers to the data processing steps taken to remove outliers from collected raw data and standardize the format, preparing it for analysis.

[0010] An "integrated dataset" refers to a collection of data that has been preprocessed and compiled from data from different sources into a single, consistent format.

[0011] An "AI algorithm" refers to a set of computational procedures that use machine learning and deep learning techniques to analyze data and derive patterns and trends.

[0012] "Fisheries guidelines" are documents and data that outline policies and guidelines for fishing activities in order to protect aquatic organisms and their habitats.

[0013] A "user terminal" refers to a device that receives information transmitted from a server and allows the user to access it, and specifically includes smartphones, tablets, personal computers, etc. [Brief explanation of the drawing]

[0014] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system aimed at analyzing the behavior of aquatic organisms and supporting their conservation. In particular, it utilizes AI technology to achieve precise data analysis and the generation of sustainable guidelines. Embodiments of this invention are described in detail below.

[0036] The server periodically collects behavioral and environmental data of aquatic organisms from sensors, satellites, and drones installed in the ocean. This data is diverse and includes information such as water temperature, salinity, current direction, and the location of organisms. The server immediately preprocesses the collected data, cleanses it to remove outliers, and standardizes the format.

[0037] Next, the server runs an AI algorithm using the pre-processed data. This AI utilizes machine learning and deep learning to analyze the behavioral patterns of aquatic organisms. The analysis identifies the migration routes of fish schools in a specific body of water and the breeding season in a particular time of year. For example, it can reveal the range to which a particular fish species that migrates in a certain region moves and breeds during warmer periods.

[0038] Based on the analysis results, the server generates customized fishing guidelines for fishermen and environmental groups. These guidelines include fishing limits for specific periods, permitted catch limits, and suggestions for conservation activities to be carried out in specific areas.

[0039] The generated guidelines are delivered to the device, making them easily accessible to users. Users can view this information through a dedicated application or web interface and develop specific action plans. For example, fishermen can adjust their fishing vessel departure schedules according to the breeding season and practice sustainable fishing activities that consider the ecosystem.

[0040] Thus, by utilizing AI technology, this invention enables detailed behavioral analysis of aquatic organisms and the provision of precise guidelines, thereby strengthening the conservation of marine ecosystems.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server collects real-time behavioral and environmental data of aquatic organisms through sensors, satellites, and drones installed in the ocean. This includes water temperature, salinity, and the location of organisms. The collected data is stored in the server's database.

[0044] Step 2:

[0045] The server begins preprocessing the collected raw data. It cleans the data by removing outliers using an anomaly detection algorithm. It also unifies different data formats and converts them into a consistent format.

[0046] Step 3:

[0047] The server inputs pre-processed data into an AI algorithm to analyze the behavioral patterns of aquatic organisms. This algorithm utilizes machine learning and clustering techniques to identify the migration routes and breeding seasons of specific fish groups.

[0048] Step 4:

[0049] The server generates fishing guidelines based on the results of AI analysis. These guidelines include recommendations for catch limits, fishing restrictions for specific periods, and suggestions for conservation activities in specific areas.

[0050] Step 5:

[0051] The server distributes the generated guidelines to the user's device. Distribution is done via email or a dedicated application, and is configured to be easily accessible to the user.

[0052] Step 6:

[0053] The device displays the received guidelines on the user interface. Through this, the user can review detailed analysis results and suggestions.

[0054] Step 7:

[0055] Based on the information provided, users develop fishing plans and conservation activities. For example, users adjust fishing vessel schedules to accommodate breeding seasons and implement environmentally conscious fishing practices.

[0056] (Example 1)

[0057] 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."

[0058] Current technologies for the conservation of aquatic organisms are insufficient to adequately analyze their behavior patterns and environmental changes, making it difficult to provide the precise guidelines necessary for sustainable fisheries and ecosystem conservation. Therefore, there is a need for a system that can efficiently and effectively collect and analyze data and generate clear guidelines.

[0059] 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.

[0060] In this invention, the server includes means for collecting information about aquatic organisms and the surrounding environment, means for preprocessing the collected information to generate an integrated information set, and means for executing artificial intelligence to analyze the behavior patterns of aquatic organisms using the integrated information set. This enables advanced analysis based on the collected data and the provision of specific, environmentally conscious fishing guidelines.

[0061] "Aquatic organisms" refers to all living things, including plants and animals, that inhabit water.

[0062] "Information" refers to data and knowledge collected for a specific purpose.

[0063] "Surrounding conditions" refer to the elements that constitute the habitat of aquatic organisms, and include environmental factors such as water temperature, salinity, and direction of current.

[0064] "Artificial intelligence" refers to technology that enables computers and systems to mimic human intellectual behavior and autonomously perform specific tasks.

[0065] A "classification method" refers to a statistical or machine learning technique that groups data according to specific criteria and divides it into categories based on their respective characteristics.

[0066] A "migration path" refers to the path or direction in which aquatic organisms move over time.

[0067] "Guidelines" refer to guidelines that provide direction or standards for actions and decisions.

[0068] This invention is constructed as a system to support the behavioral analysis and conservation of aquatic organisms. Specific embodiments are described below.

[0069] Data Collection: The server uses various sensor devices, satellites, and drones installed in the ocean to collect information about aquatic organisms and their surrounding environment. This information includes water temperature, salinity, current direction, and the location of organisms.

[0070] Data preprocessing: The server immediately preprocesses the collected information, performing cleansing to remove outliers. This improves data accuracy and standardizes it into a format suitable for analysis.

[0071] Data Analysis: The server drives artificial intelligence using pre-processed data. The server executes machine learning and deep learning algorithms to analyze the behavior patterns of specific aquatic organisms. For example, it can identify the migration routes of fish schools or the breeding season in specific times of the year.

[0072] Guideline Generation: Based on the analysis results, the server generates customized fishing guidelines for fishermen and environmental groups. These guidelines include suggestions for catch limits and adjustments to fishing activities during specific periods.

[0073] Guideline distribution: The server distributes the generated guidelines to the user's device, making the information more accessible to the user.

[0074] User Use: Users access the distributed guidelines via a dedicated application or web interface through their devices. Based on this information, users develop specific action plans and implement sustainable fishing and environmental conservation activities.

[0075] A concrete example is when fishermen adjust the operating schedules of their fishing vessels to suit specific seasons. An example of a prompt message could be: "Conduct a detailed analysis of water temperature and fish breeding patterns in a certain region and propose guidelines for sustainable fisheries."

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The server collects data on aquatic organisms and the environment from sensor devices installed in the ocean. Sensor inputs include water temperature, salinity, current direction, and organism location information. This information is stored in a database in real time and output as a collected data set.

[0079] Step 2:

[0080] The server preprocesses the collected data. It detects noisy data and outliers and performs data cleaning. It receives the stored data as input, removes outliers through visual inspection, and outputs an integrated dataset converted to a standardized format.

[0081] Step 3:

[0082] The server inputs the integrated dataset into an AI algorithm. The artificial intelligence uses a machine learning model to analyze the behavior patterns of aquatic organisms. Based on the input data, it identifies the migration routes of fish schools that roam a specific body of water using clustering techniques and outputs this as the analysis result.

[0083] Step 4:

[0084] The server generates fishing guidelines that include environmental considerations based on the analysis results. The AI ​​algorithm creates rule-based guidelines that incorporate fishing limits and conservation activities for specific periods. The generated guidelines are obtained as output.

[0085] Step 5:

[0086] The server delivers the generated guidelines to the user's terminal. The guideline data is transmitted over the network and output to the terminal in a format that the user can view.

[0087] Step 6:

[0088] Users receive guidelines via their devices and can verify them through a dedicated app or web interface. Based on this, users develop local action plans and implement sustainable fishing activities in accordance with the information received.

[0089] (Application Example 1)

[0090] 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."

[0091] In recent years, with the increasing importance of environmental protection, there has been a growing demand for the sustainable use of marine resources and environmentally conscious consumer behavior. However, there is a lack of information that makes it easy for consumers to find and use products based on sustainable fisheries. As a result, both fishermen and consumers face the challenge of having difficulty taking sustainable actions.

[0092] 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.

[0093] In this invention, the server includes means for collecting aquatic organism behavior data and environmental data; means for preprocessing the collected data to generate an integrated dataset; means for executing an AI algorithm that analyzes the behavior patterns of aquatic organisms using the integrated dataset; means for generating environmentally conscious fishing guidelines based on the analysis results; means for distributing the generated guidelines and store information based on sustainable fishing to the user terminal; and means for identifying and notifying the user of stores that provide sustainable products based on the user's location information. This enables consumers to choose sustainable products while minimizing their own environmental impact.

[0094] "Aquatic organism behavioral data" refers to information about the movement, reproduction, and predation behaviors of organisms living in oceans and freshwater.

[0095] "Environmental data" refers to information about the natural environment that affects aquatic organisms, such as water temperature, salinity, and current direction.

[0096] "Means of collection" refers to technologies and devices used to collect data, such as sensor equipment, satellites, and drones.

[0097] "Means for generating preprocessed and integrated datasets" refer to techniques and processes that cleanse and standardize data formats, and then reconstruct them into a user-friendly form.

[0098] "Means for executing AI algorithms" refer to technologies and systems that use machine learning or deep learning to extract specific patterns or features from data being analyzed.

[0099] "Means for generating environmentally conscious fisheries guidelines" refers to technologies and methods for creating guidelines and proposals for achieving sustainable fisheries based on analysis results.

[0100] "Means of delivering information to user terminals" refers to the technologies and infrastructure used to transmit generated information to and display it on the user's digital device, such as a smartphone or computer.

[0101] "Store information based on sustainable fishing" refers to information about stores that handle products obtained through environmentally friendly fishing methods.

[0102] "Means for identifying and notifying users of stores that provide sustainable products based on user location information" refers to technologies and services that use geographic information systems to identify stores that provide products in a sustainable manner near the user's current location and to inform the user of that information.

[0103] The system for implementing this invention is designed to collect various data, analyze it, and provide users with useful information. The server first collects aquatic organism behavior data and environmental data from various devices such as marine and freshwater sensor equipment, satellites, and drones. This data includes water temperature, salinity, current direction, and location information. The collected data is quickly preprocessed to eliminate outliers and standardize the format, thereby improving the current situation and data accuracy.

[0104] Next, the server uses the pre-processed data to run AI algorithms and analyze the behavioral patterns of aquatic organisms. This analysis utilizes Python and deep learning libraries such as TENSORFLOW®. As a result of the analysis, it predicts the migration routes of fish schools within a specific area and their seasonal breeding activities, and uses this data to generate environmentally conscious fishing guidelines. These guidelines include suggestions for catch limits and conservation activities based on the Sustainable Development Goals.

[0105] The generated guidelines and store information are delivered to the user's device using Flask. The application installed on the device uses the Google® Maps API to obtain the user's location and identify and notify the user of nearby stores that offer sustainable products. For example, if the user is in a certain city, a list of restaurants and retail stores that offer fish obtained through sustainable fishing methods will be displayed based on their location.

[0106] Such a system allows users to choose sustainable products while minimizing their environmental impact. A concrete example of a prompt message would be, "I want to find a place that offers seafood that is environmentally conscious."

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The server collects behavioral and environmental data of aquatic organisms from sensor devices, satellites, and drones. Input data includes water temperature, salinity, current direction, and location information. This data is integrated and filtered to generate a clean dataset. The output is a pre-processed dataset with a standardized format.

[0110] Step 2:

[0111] The server takes the dataset generated in Step 1 as input and executes an AI algorithm. Here, a machine learning model using TensorFlow is utilized to analyze the behavioral patterns of aquatic organisms. Specifically, clustering techniques are used to predict the migration routes and breeding seasons of specific fish schools. The output consists of the analyzed behavioral patterns and predicted data.

[0112] Step 3:

[0113] The server generates environmentally conscious guidelines based on the analysis results from Step 2. The input is the analysis results, which are used to develop guidelines for sustainable fishing activities. Specifically, this involves formulating proposals for fishing limits during specific periods and conservation activities in specific areas. The output is a guideline document.

[0114] Step 4:

[0115] The server delivers store information based on generated guidelines and aquatic life and environmental data to user terminals. Specifically, it uses Flask to provide the generated documents to the user's application. Input consists of guidelines and store information, and the server sends product suggestions tailored to the user's interests. Output is displayed as information viewable by the user.

[0116] Step 5:

[0117] The device utilizes the user's location information and the Google Maps API to identify and notify the user of nearby stores offering sustainable products. The input is the user's location, and the system cross-checks this information to identify nearby sustainable businesses. The output provides the user with specific store names and descriptions of the products offered.

[0118] 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.

[0119] This invention combines a system for analyzing the behavior and supporting the conservation of aquatic organisms with an emotion engine that recognizes user emotions. This emotion engine enables the generation and provision of flexible guidelines that take user feedback into consideration.

[0120] The server collects behavioral data from aquatic organisms using sensors and drones installed in the ocean. This data includes the location, movement patterns, and environmental conditions of the aquatic organisms. Once the data is collected, the server preprocesses it, removing outliers and integrating it into a consistent format.

[0121] Next, the server uses an AI algorithm to analyze the behavioral patterns of aquatic organisms from the pre-processed data. This AI performs machine learning techniques, including clustering, to identify specific migration routes and breeding patterns. Based on the analysis results, the server automatically generates environmentally conscious fishing guidelines.

[0122] The generated guidelines are refined into their final form through a process where the emotion engine recognizes the user's emotions. Users input opinions and feedback into the system via their terminals. By analyzing this input data and audio, the emotion engine identifies the user's emotional state. Based on this, the server adjusts the content and presentation method of the guidelines and delivers them in a format suitable for each individual user.

[0123] For example, if a user expresses dissatisfaction with the presented guidelines, the sentiment engine analyzes this, and the server generates new guidelines that consider alternatives. These new guidelines are then redistributed to the user's device as more convincing information. This adjustment enables the implementation of intuitive and effective fishing plans and conservation activities that reflect the user's intentions.

[0124] Thus, by incorporating an emotion engine, the present invention provides an advanced system that can dynamically reflect user feedback and support sustainable activities tailored to individual needs.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The server collects behavioral and environmental data of aquatic organisms from sensors and drones installed in the ocean. This data is transmitted to the server in real time and stored in a database. The collected data includes water temperature, salinity, current strength, and location information.

[0128] Step 2:

[0129] The server organizes and preprocesses the collected raw data. It removes outliers and standardizes the data format as needed. In this process, it applies data cleansing and standardization algorithms to prepare a dataset suitable for analysis.

[0130] Step 3:

[0131] The server runs an AI algorithm using pre-processed data. This algorithm utilizes machine learning and clustering techniques to identify migration routes and breeding behaviors of aquatic organisms. The analysis results provide important insights into the characteristics and behavioral patterns of these organisms.

[0132] Step 4:

[0133] The server generates environmentally conscious fishing guidelines based on AI analysis results. These guidelines include suggestions such as fishing restrictions for specific periods and regions, and recommended catch limits. The generated guidelines are customized for each user.

[0134] Step 5:

[0135] The device collects user feedback. Users can input their thoughts and opinions on the guidelines through the device. Feedback can be in the form of text or audio.

[0136] Step 6:

[0137] The server uses an emotion engine to analyze user feedback and identify their emotional state. This analysis allows for a quantitative evaluation of user satisfaction and dissatisfaction, and identifies areas for improvement.

[0138] Step 7:

[0139] The server adjusts the content and presentation method of the guidelines, taking into account the analysis results of the emotion engine. It modifies the suggested content according to the user's emotions and delivers it to the user's terminal in an appropriate format. This enables the provision of information optimized for each user.

[0140] Step 8:

[0141] Users can view the adjusted guidelines on their devices and use them in actual fishing plans and conservation activities. Based on these adjustments, users can plan their activities and implement sustainable management practices.

[0142] (Example 2)

[0143] 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".

[0144] Conventional guideline systems for aquatic organism conservation and fisheries failed to adequately consider the feelings and opinions of users, resulting in difficulty in providing flexible guidelines tailored to individual needs. Furthermore, there was a lack of information necessary to conduct detailed analyses of environmental conditions and aquatic organism behavior patterns in order to implement more effective conservation activities.

[0145] 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.

[0146] In this invention, the server includes means for using a device equipped with the function of collecting aquatic organism behavioral data and environmental data; means for preprocessing the collected data to generate an integrated dataset; means for executing an artificial intelligence algorithm that analyzes the behavioral patterns of aquatic organisms using the integrated dataset; means for generating guidelines for environmentally conscious fishing based on the analysis results; means for delivering the generated guidelines to the user's device; means for analyzing feedback from the user and identifying the user's emotional state using an emotion analysis engine; and means for adjusting the guidelines generated based on the results of the emotion analysis to suit the user. This enables dynamic reflection of user feedback and sustainable activities that are environmentally conscious and meet individual needs.

[0147] "Aquatic organisms" is a general term for organisms that inhabit aquatic environments such as lakes, rivers, and oceans, and live in natural aquatic environments.

[0148] "Behavioral data" refers to data that includes information about the movements, habits, and migration patterns of aquatic organisms.

[0149] "Environmental data" refers to data that includes information indicating the conditions of the environment in which aquatic organisms live, such as water temperature, oxygen concentration, salinity, and current speed.

[0150] An "artificial intelligence algorithm" is a computational method that uses machine learning and data analysis to learn patterns from data and perform predictions and classifications.

[0151] An "emotion analysis engine" is software that recognizes the user's emotional state from their opinions and feedback through language and voice analysis.

[0152] "Guidelines" are a set of recommended procedures or policies provided to achieve a specific purpose or activity.

[0153] To implement this invention, the server first collects behavioral data of aquatic organisms and environmental data using multiple measuring instruments and mobile devices installed in the marine environment. Specifically, it operates devices such as water temperature sensors, acoustic depth sounders, and drones to collect the necessary data. The server then performs data cleaning on the collected data to remove outliers and improve data accuracy. Subsequently, it centralizes the data and prepares it in a format suitable for analysis.

[0154] The server performs analysis using artificial intelligence algorithms with the pre-processed data. This analysis uses clustering techniques, a type of machine learning technology, to explore the behavioral patterns and environmental adaptability of aquatic organisms. Using a generative AI model, fisheries guidelines are generated based on a series of prompts, "Please propose guidelines for sustainable fisheries."

[0155] Subsequently, the generated fishing guidelines are refined by analyzing user feedback using an emotion analysis engine. Users provide their thoughts and opinions to the system through their devices, and this information is processed by the emotion analysis engine. As a result, the server effectively optimizes and delivers the guidelines according to the user's emotional state. The refined guidelines are transmitted to the devices, enabling users to implement sustainable fishing plans based on them.

[0156] As a concrete example, the aforementioned system is particularly effective in providing fishing strategies that respond to changes in the marine environment caused by climate change. In this case, real-time feedback is collected, and the emotion engine analyzes users' reactions to conventional guidelines. Based on the analysis results, the server can regenerate and deliver appropriate guidelines tailored to the user, thereby improving user satisfaction and productivity.

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The server collects behavioral and environmental data of aquatic organisms from measuring instruments and mobile devices installed in the ocean. The input is raw data acquired from various sensors and drones. This raw data includes environmental information such as location, water temperature, and oxygen concentration. The server periodically acquires this data and stores it in a database. The output is an unprocessed dataset.

[0160] Step 2:

[0161] The server preprocesses the collected raw data. This preprocessing removes outliers and transforms the data into a consistent format. Specifically, it performs data formatting, normalization, and outlier removal. The input is the raw dataset obtained in step 1, and the output is a formatted, consistent dataset.

[0162] Step 3:

[0163] The server performs analysis using an artificial intelligence algorithm with pre-processed data. The input is the dataset formatted in step 2. This analysis uses clustering techniques to analyze the behavioral patterns of aquatic organisms from the dataset. Specifically, it classifies the data into clusters and identifies specific movement patterns and breeding areas. The output is pattern data containing the analysis results.

[0164] Step 4:

[0165] The server generates fishing guidelines using a generative AI model based on the analysis results. The input is the pattern data obtained in step 3. Specifically, the AI ​​model generates appropriate guidelines using the prompt "Please propose guidelines for sustainable fisheries." The output is the generated fishing guidelines.

[0166] Step 5:

[0167] The server delivers the generated guidelines to the user via the terminal. The input is the guidelines generated in step 4. The terminal receives the guidelines and notifies the user. Specifically, it displays the guidelines on the screen or via audio using an information transmission protocol. The output is the guidelines presented in a format that the user can confirm.

[0168] Step 6:

[0169] The user sends feedback on the guidelines to the system via the terminal. The input is the guidelines presented in step 5. The user inputs the feedback as text or voice, and the terminal sends it to the server. The output is the user's feedback data.

[0170] Step 7:

[0171] The server processes the feedback data using an emotion analysis engine to identify the user's emotional state. The input is the feedback data obtained in step 6. Specifically, it analyzes text and audio data using natural language processing techniques to determine emotions. The output is the emotion analysis result.

[0172] Step 8:

[0173] The server adjusts and regenerates the guidelines based on the sentiment analysis results to suit the user. The input is the sentiment analysis results obtained in step 7. The server applies the adjustments to the guidelines according to the analysis results and considers alternatives as needed. The output is the adjusted guidelines.

[0174] Step 9:

[0175] The server redistributes the adjusted guidance to the terminal and notifies the user. The input is the adjusted guidance obtained in step 8. The terminal receives the redistributed guidance and notifies the user of it. The output is a re-presentation of the adjusted guidance.

[0176] (Application Example 2)

[0177] 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".

[0178] In analyzing the behavior of aquatic organisms and generating environmental guidelines, it is difficult to reflect the individual emotions and feedback of users. Furthermore, because the information provided is not tailored to the user's interests and emotions, there is a challenge in providing an intuitive and satisfying experience for the user.

[0179] 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.

[0180] In this invention, the server includes means for collecting aquatic organism behavior data and environmental data; means for preprocessing the collected data to generate an integrated dataset; means for executing an information processing device that analyzes the behavior patterns of aquatic organisms using the integrated dataset; means for generating environmentally conscious usage guidelines based on the analysis results; means for transmitting the generated guidelines to a user device; means for analyzing the user's emotions using an emotion recognition device and adjusting the guidelines based on the analysis results; and means for individually providing the adjusted guidelines via a display device. This makes it possible to provide personalized guidelines that are adjusted based on the user's emotions and interests.

[0181] "Aquatic organism behavioral data" refers to information collected to analyze the movement and behavioral patterns of organisms living in water.

[0182] "Environmental data" refers to information about the conditions of the natural environment in which aquatic organisms live, including elements such as temperature, salinity, and light intensity.

[0183] "Preprocessing" is the process of transforming collected data into an analyzable format, including the removal of outliers and standardization of the format.

[0184] An "integrated dataset" is a collection of data that has undergone preprocessing and been organized into a unified format.

[0185] An "information processing device" is a computing device used for analyzing data and is programmed to perform specific processes.

[0186] An "emotion recognition device" is a system that analyzes a user's emotional state based on their voice, physiological responses, and other factors.

[0187] "Guidelines" are proposed guidelines for action to conserve and sustainably use aquatic life.

[0188] A "user device" is a device used by a user to receive guidelines and information.

[0189] A "display device" is a device used to visualize and present adjusted guidelines and analysis results to the user.

[0190] This invention realizes a system for collecting data on the behavior and environment of aquatic organisms and providing users with interactive, environmentally conscious guidelines based on that data. The server acquires behavioral and environmental data of aquatic organisms using underwater sensors and drones. This data is preprocessed, such as removing outliers and converting it to the required format, and then integrated into a consistent dataset.

[0191] The server uses an information processing unit to execute AI algorithms based on the integrated dataset. This allows for the analysis of biological movement and behavioral patterns. The hardware used includes underwater sensors and drones, while the software employs machine learning models based on Python.

[0192] Based on the analysis results, the server automatically generates environmentally conscious usage guidelines and distributes them to the user's device. To achieve this, the server uses an emotion recognition device to analyze feedback from the user. It then optimizes the guidelines according to the user's emotional state, providing personalized information.

[0193] A concrete example is an interactive experience where, as visitors move around the aquarium using smart glasses, the exhibits are adjusted in real time based on their interests and reactions. In this way, a feedback loop between the server and the user is used to provide more effective and convincing guidelines.

[0194] Examples of prompts include, "Consider a system that provides dynamic guidance based on user emotions during individual tours at an aquarium," and "Show how to use visitor emotion data to suggest content that might interest them about aquatic life."

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The server acquires behavioral and environmental data of aquatic organisms from sensors and drones. This includes location information, movement patterns, and environmental conditions. The raw data obtained is transferred to the server. The input at this time is data from various sensors, and the output is raw data stored on the server.

[0198] Step 2:

[0199] The server preprocesses the input data and generates a unified dataset in a consistent format. Specifically, it detects and removes outliers and converts the data to a standard format. The input is raw data, and the output is a clean, formatted dataset.

[0200] Step 3:

[0201] The server uses an information processing device to analyze the behavioral patterns of aquatic organisms from an integrated dataset. At this stage, AI algorithms are used, particularly applying machine learning classification techniques. The input is the integrated dataset, and the output is the analyzed behavioral patterns.

[0202] Step 4:

[0203] The server generates environmentally conscious usage guidelines based on the analysis results. This generation process uses a generation AI model to automatically generate content that ensures the guidelines are sustainable. The input is the analysis results, and the output is the initial usage guidelines.

[0204] Step 5:

[0205] The user inputs feedback through the terminal. At this time, the terminal uses an emotion recognition device to analyze the user's emotional state and sends the data to the server. The input is the user's feedback and emotional data, and the output is the emotional analysis result sent to the server.

[0206] Step 6:

[0207] The server adjusts the guidelines based on the received sentiment analysis results, according to the user's emotions. The adjusted guidelines are designed to improve user interest and acceptance. The input is the sentiment analysis results and the initial guidelines, and the output is the adjusted usage guidelines.

[0208] Step 7:

[0209] The server delivers the revised usage guidelines to the user via a display device. The user optimizes their behavior based on the newly provided guidelines on their device. The input is the revised guidelines, and the output is the presentation of the guidelines to the user.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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".

[0226] This invention is a system aimed at analyzing the behavior of aquatic organisms and supporting their conservation. In particular, it utilizes AI technology to achieve precise data analysis and the generation of sustainable guidelines. Embodiments of this invention are described in detail below.

[0227] The server periodically collects behavioral and environmental data of aquatic organisms from sensors, satellites, and drones installed in the ocean. This data is diverse and includes information such as water temperature, salinity, current direction, and the location of organisms. The server immediately preprocesses the collected data, cleanses it to remove outliers, and standardizes the format.

[0228] Next, the server runs an AI algorithm using the pre-processed data. This AI utilizes machine learning and deep learning to analyze the behavioral patterns of aquatic organisms. The analysis identifies the migration routes of fish schools in a specific body of water and the breeding season in a particular time of year. For example, it can reveal the range to which a particular fish species that migrates in a certain region moves and breeds during warmer periods.

[0229] Based on the analysis results, the server generates customized fishing guidelines for fishermen and environmental groups. These guidelines include fishing limits for specific periods, permitted catch limits, and suggestions for conservation activities to be carried out in specific areas.

[0230] The generated guidelines are delivered to the device, making them easily accessible to users. Users can view this information through a dedicated application or web interface and develop specific action plans. For example, fishermen can adjust their fishing vessel departure schedules according to the breeding season and practice sustainable fishing activities that consider the ecosystem.

[0231] Thus, by utilizing AI technology, this invention enables detailed behavioral analysis of aquatic organisms and the provision of precise guidelines, thereby strengthening the conservation of marine ecosystems.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The server collects real-time behavioral and environmental data of aquatic organisms through sensors, satellites, and drones installed in the ocean. This includes water temperature, salinity, and the location of organisms. The collected data is stored in the server's database.

[0235] Step 2:

[0236] The server begins preprocessing the collected raw data. It cleans the data by removing outliers using an anomaly detection algorithm. It also unifies different data formats and converts them into a consistent format.

[0237] Step 3:

[0238] The server inputs pre-processed data into an AI algorithm to analyze the behavioral patterns of aquatic organisms. This algorithm utilizes machine learning and clustering techniques to identify the migration routes and breeding seasons of specific fish groups.

[0239] Step 4:

[0240] The server generates fishing guidelines based on the results of AI analysis. These guidelines include recommendations for catch limits, fishing restrictions for specific periods, and suggestions for conservation activities in specific areas.

[0241] Step 5:

[0242] The server distributes the generated guidelines to the user's device. Distribution is done via email or a dedicated application, and is configured to be easily accessible to the user.

[0243] Step 6:

[0244] The device displays the received guidelines on the user interface. Through this, the user can review detailed analysis results and suggestions.

[0245] Step 7:

[0246] Based on the information provided, users develop fishing plans and conservation activities. For example, users adjust fishing vessel schedules to accommodate breeding seasons and implement environmentally conscious fishing practices.

[0247] (Example 1)

[0248] 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."

[0249] Current technologies for the conservation of aquatic organisms are insufficient to adequately analyze their behavior patterns and environmental changes, making it difficult to provide the precise guidelines necessary for sustainable fisheries and ecosystem conservation. Therefore, there is a need for a system that can efficiently and effectively collect and analyze data and generate clear guidelines.

[0250] 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.

[0251] In this invention, the server includes means for collecting information about aquatic organisms and the surrounding environment, means for preprocessing the collected information to generate an integrated information set, and means for executing artificial intelligence to analyze the behavior patterns of aquatic organisms using the integrated information set. This enables advanced analysis based on the collected data and the provision of specific, environmentally conscious fishing guidelines.

[0252] "Aquatic organisms" refers to all living things, including plants and animals, that inhabit water.

[0253] "Information" refers to data and knowledge collected for a specific purpose.

[0254] "Surrounding conditions" refer to the elements that constitute the habitat of aquatic organisms, and include environmental factors such as water temperature, salinity, and direction of current.

[0255] "Artificial intelligence" refers to technology that enables computers and systems to mimic human intellectual behavior and autonomously perform specific tasks.

[0256] A "classification method" refers to a statistical or machine learning technique that groups data according to specific criteria and divides it into categories based on their respective characteristics.

[0257] A "migration path" refers to the path or direction in which aquatic organisms move over time.

[0258] "Guidelines" refer to guidelines that provide direction or standards for actions and decisions.

[0259] This invention is constructed as a system to support the behavioral analysis and conservation of aquatic organisms. Specific embodiments are described below.

[0260] Data Collection: The server uses various sensor devices, satellites, and drones installed in the ocean to collect information about aquatic organisms and their surrounding environment. This information includes water temperature, salinity, current direction, and the location of organisms.

[0261] Data preprocessing: The server immediately preprocesses the collected information, performing cleansing to remove outliers. This improves data accuracy and standardizes it into a format suitable for analysis.

[0262] Data Analysis: The server drives artificial intelligence using pre-processed data. The server executes machine learning and deep learning algorithms to analyze the behavior patterns of specific aquatic organisms. For example, it can identify the migration routes of fish schools or the breeding season in specific times of the year.

[0263] Guideline Generation: Based on the analysis results, the server generates customized fishing guidelines for fishermen and environmental groups. These guidelines include suggestions for catch limits and adjustments to fishing activities during specific periods.

[0264] Guideline distribution: The server distributes the generated guidelines to the user's device, making the information more accessible to the user.

[0265] User Use: Users access the distributed guidelines via a dedicated application or web interface through their devices. Based on this information, users develop specific action plans and implement sustainable fishing and environmental conservation activities.

[0266] A concrete example is when fishermen adjust the operating schedules of their fishing vessels to suit specific seasons. An example of a prompt message could be: "Conduct a detailed analysis of water temperature and fish breeding patterns in a certain region and propose guidelines for sustainable fisheries."

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The server collects data on aquatic organisms and the environment from sensor devices installed in the ocean. Sensor inputs include water temperature, salinity, current direction, and organism location information. This information is stored in a database in real time and output as a collected data set.

[0270] Step 2:

[0271] The server preprocesses the collected data. It detects noisy data and outliers and performs data cleaning. It receives the stored data as input, removes outliers through visual inspection, and outputs an integrated dataset converted to a standardized format.

[0272] Step 3:

[0273] The server inputs the integrated dataset into an AI algorithm. The artificial intelligence uses a machine learning model to analyze the behavior patterns of aquatic organisms. Based on the input data, it identifies the migration routes of fish schools that roam a specific body of water using clustering techniques and outputs this as the analysis result.

[0274] Step 4:

[0275] The server generates fishing guidelines that include environmental considerations based on the analysis results. The AI ​​algorithm creates rule-based guidelines that incorporate fishing limits and conservation activities for specific periods. The generated guidelines are obtained as output.

[0276] Step 5:

[0277] The server delivers the generated guidelines to the user's terminal. The guideline data is transmitted over the network and output to the terminal in a format that the user can view.

[0278] Step 6:

[0279] Users receive guidelines via their devices and can verify them through a dedicated app or web interface. Based on this, users develop local action plans and implement sustainable fishing activities in accordance with the information received.

[0280] (Application Example 1)

[0281] 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."

[0282] In recent years, with the increasing importance of environmental protection, there has been a growing demand for the sustainable use of marine resources and environmentally conscious consumer behavior. However, there is a lack of information that makes it easy for consumers to find and use products based on sustainable fisheries. As a result, both fishermen and consumers face the challenge of having difficulty taking sustainable actions.

[0283] 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.

[0284] In this invention, the server includes means for collecting the behavioral data and environmental data of aquatic organisms, means for preprocessing the collected data to generate an integrated dataset, means for executing an AI algorithm for analyzing the behavioral patterns of aquatic organisms using the integrated dataset, means for generating fishery guidelines considering the environment based on the analysis results, means for distributing the generated guidelines and store information based on sustainable fishery to the user terminal, and means for identifying and notifying a sales location that provides sustainable products based on the user's location information. As a result, consumers can select sustainable products while suppressing their own environmental impact.

[0285] The "behavioral data of aquatic organisms" refers to information regarding the behaviors of organisms living in the ocean or fresh water, such as movement, reproduction, and predation.

[0286] The "environmental data" refers to information regarding the natural environment that affects aquatic organisms, such as water temperature, salinity concentration, and flow direction.

[0287] The "means for collecting" refers to the technologies and devices for collecting data using sensor equipment, satellites, drones, etc.

[0288] The "means for generating an integrated dataset by preprocessing" refers to the technologies and processes for performing data cleansing and format unification and restructuring the data into an easy-to-handle form.

[0289] The "means for executing an AI algorithm" refers to the technologies and systems for extracting specific patterns and features from the data to be analyzed using machine learning and deep learning.

[0290] The "means for generating fishery guidelines considering the environment" refers to the technologies and methods for creating guidelines and proposals for realizing sustainable fishery based on the analysis results.

[0291] "Means of delivering information to user terminals" refers to the technologies and infrastructure used to transmit generated information to and display it on the user's digital device, such as a smartphone or computer.

[0292] "Store information based on sustainable fishing" refers to information about stores that handle products obtained through environmentally friendly fishing methods.

[0293] "Means for identifying and notifying users of stores that provide sustainable products based on user location information" refers to technologies and services that use geographic information systems to identify stores that provide products in a sustainable manner near the user's current location and to inform the user of that information.

[0294] The system for implementing this invention is designed to collect various data, analyze it, and provide users with useful information. The server first collects aquatic organism behavior data and environmental data from various devices such as marine and freshwater sensor equipment, satellites, and drones. This data includes water temperature, salinity, current direction, and location information. The collected data is quickly preprocessed to eliminate outliers and standardize the format, thereby improving the current situation and data accuracy.

[0295] Next, the server uses the pre-processed data to run AI algorithms and analyze the behavioral patterns of aquatic organisms. This analysis utilizes Python and deep learning libraries such as TensorFlow. As a result of the analysis, it predicts the migration routes of fish schools within a specific region and their seasonal breeding activities, and uses this data to generate environmentally conscious fishing guidelines. These guidelines include suggestions for catch limits and conservation activities based on sustainable development goals.

[0296] The generated guidelines and store information are delivered to the user's device using Flask. The application installed on the device uses the Google Maps API to obtain the user's location and identify and notify the user of nearby stores that offer sustainable products. For example, if the user is in a certain city, a list of restaurants and retail stores that serve fish obtained through sustainable fishing methods will be displayed based on their location.

[0297] Such a system allows users to choose sustainable products while minimizing their environmental impact. A concrete example of a prompt message would be, "I want to find a place that offers seafood that is environmentally conscious."

[0298] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0299] Step 1:

[0300] The server collects behavioral and environmental data of aquatic organisms from sensor devices, satellites, and drones. Input data includes water temperature, salinity, current direction, and location information. This data is integrated and filtered to generate a clean dataset. The output is a pre-processed dataset with a standardized format.

[0301] Step 2:

[0302] The server takes the dataset generated in Step 1 as input and executes an AI algorithm. Here, a machine learning model using TensorFlow is utilized to analyze the behavioral patterns of aquatic organisms. Specifically, clustering techniques are used to predict the migration routes and breeding seasons of specific fish schools. The output consists of the analyzed behavioral patterns and predicted data.

[0303] Step 3:

[0304] Based on the analysis results in step 2, the server generates guidelines considering the environment. The input is the analysis results, and based on them, guidelines regarding sustainable fishing activities are formulated. As specific actions, catch restrictions during a specific period and proposals for conservation activities in specific areas are formulated. The output is a guideline document.

[0305] Step 4:

[0306] The server distributes the generated guidelines and store information based on aquatic organisms and environmental data to the user terminal. In particular, using Flask, the generated document is provided to the user's application. The input is the guidelines and store information, and proposals for products that the user is interested in are sent. The output is displayed as information that the user can view.

[0307] Step 5:

[0308] The terminal utilizes the user's location information and uses the Google Maps API to identify and notify nearby sales outlets that offer sustainable products. The input is the user's location information, and the system cross-checks the information for identifying surrounding sustainable business premises. As output, the specific store name and descriptions of the products offered are notified to the user.

[0309] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0310] The present invention combines an emotion engine for recognizing the user's emotions with a system aimed at analyzing the behavior of aquatic organisms and supporting conservation. With this emotion engine, it becomes possible to generate and provide flexible guidelines considering the user's feedback.

[0311] The server collects behavioral data from aquatic organisms using sensors and drones installed in the ocean. This data includes the location, movement patterns, and environmental conditions of the aquatic organisms. Once the data is collected, the server preprocesses it, removing outliers and integrating it into a consistent format.

[0312] Next, the server uses an AI algorithm to analyze the behavioral patterns of aquatic organisms from the pre-processed data. This AI performs machine learning techniques, including clustering, to identify specific migration routes and breeding patterns. Based on the analysis results, the server automatically generates environmentally conscious fishing guidelines.

[0313] The generated guidelines are refined into their final form through a process where the emotion engine recognizes the user's emotions. Users input opinions and feedback into the system via their terminals. By analyzing this input data and audio, the emotion engine identifies the user's emotional state. Based on this, the server adjusts the content and presentation method of the guidelines and delivers them in a format suitable for each individual user.

[0314] For example, if a user expresses dissatisfaction with the presented guidelines, the sentiment engine analyzes this, and the server generates new guidelines that consider alternatives. These new guidelines are then redistributed to the user's device as more convincing information. This adjustment enables the implementation of intuitive and effective fishing plans and conservation activities that reflect the user's intentions.

[0315] Thus, by incorporating an emotion engine, the present invention provides an advanced system that can dynamically reflect user feedback and support sustainable activities tailored to individual needs.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] The server collects behavioral and environmental data of aquatic organisms from sensors and drones installed in the ocean. This data is transmitted to the server in real time and stored in a database. The collected data includes water temperature, salinity, current strength, and location information.

[0319] Step 2:

[0320] The server organizes and preprocesses the collected raw data. It removes outliers and standardizes the data format as needed. In this process, it applies data cleansing and standardization algorithms to prepare a dataset suitable for analysis.

[0321] Step 3:

[0322] The server runs an AI algorithm using pre-processed data. This algorithm utilizes machine learning and clustering techniques to identify migration routes and breeding behaviors of aquatic organisms. The analysis results provide important insights into the characteristics and behavioral patterns of these organisms.

[0323] Step 4:

[0324] The server generates environmentally conscious fishing guidelines based on AI analysis results. These guidelines include suggestions such as fishing restrictions for specific periods and regions, and recommended catch limits. The generated guidelines are customized for each user.

[0325] Step 5:

[0326] The device collects user feedback. Users can input their thoughts and opinions on the guidelines through the device. Feedback can be in the form of text or audio.

[0327] Step 6:

[0328] The server uses an emotion engine to analyze user feedback and identify their emotional state. This analysis allows for a quantitative evaluation of user satisfaction and dissatisfaction, and identifies areas for improvement.

[0329] Step 7:

[0330] The server adjusts the content and presentation method of the guidelines, taking into account the analysis results of the emotion engine. It modifies the suggested content according to the user's emotions and delivers it to the user's terminal in an appropriate format. This enables the provision of information optimized for each user.

[0331] Step 8:

[0332] Users can view the adjusted guidelines on their devices and use them in actual fishing plans and conservation activities. Based on these adjustments, users can plan their activities and implement sustainable management practices.

[0333] (Example 2)

[0334] 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".

[0335] Conventional guideline systems for aquatic organism conservation and fisheries failed to adequately consider the feelings and opinions of users, resulting in difficulty in providing flexible guidelines tailored to individual needs. Furthermore, there was a lack of information necessary to conduct detailed analyses of environmental conditions and aquatic organism behavior patterns in order to implement more effective conservation activities.

[0336] 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.

[0337] In this invention, the server includes means for using a device equipped with the function of collecting aquatic organism behavioral data and environmental data; means for preprocessing the collected data to generate an integrated dataset; means for executing an artificial intelligence algorithm that analyzes the behavioral patterns of aquatic organisms using the integrated dataset; means for generating guidelines for environmentally conscious fishing based on the analysis results; means for delivering the generated guidelines to the user's device; means for analyzing feedback from the user and identifying the user's emotional state using an emotion analysis engine; and means for adjusting the guidelines generated based on the results of the emotion analysis to suit the user. This enables dynamic reflection of user feedback and sustainable activities that are environmentally conscious and meet individual needs.

[0338] "Aquatic organisms" is a general term for organisms that inhabit aquatic environments such as lakes, rivers, and oceans, and live in natural aquatic environments.

[0339] "Behavioral data" refers to data that includes information about the movements, habits, and migration patterns of aquatic organisms.

[0340] "Environmental data" refers to data that includes information indicating the conditions of the environment in which aquatic organisms live, such as water temperature, oxygen concentration, salinity, and current speed.

[0341] An "artificial intelligence algorithm" is a computational method that uses machine learning and data analysis to learn patterns from data and perform predictions and classifications.

[0342] An "emotion analysis engine" is software that recognizes the user's emotional state from their opinions and feedback through language and voice analysis.

[0343] "Guidelines" are a set of recommended procedures or policies provided to achieve a specific purpose or activity.

[0344] To implement this invention, the server first collects behavioral data of aquatic organisms and environmental data using multiple measuring instruments and mobile devices installed in the marine environment. Specifically, it operates devices such as water temperature sensors, acoustic depth sounders, and drones to collect the necessary data. The server then performs data cleaning on the collected data to remove outliers and improve data accuracy. Subsequently, it centralizes the data and prepares it in a format suitable for analysis.

[0345] The server performs analysis using artificial intelligence algorithms with the pre-processed data. This analysis uses clustering techniques, a type of machine learning technology, to explore the behavioral patterns and environmental adaptability of aquatic organisms. Using a generative AI model, fisheries guidelines are generated based on a series of prompts, "Please propose guidelines for sustainable fisheries."

[0346] Subsequently, the generated fishing guidelines are refined by analyzing user feedback using an emotion analysis engine. Users provide their thoughts and opinions to the system through their devices, and this information is processed by the emotion analysis engine. As a result, the server effectively optimizes and delivers the guidelines according to the user's emotional state. The refined guidelines are transmitted to the devices, enabling users to implement sustainable fishing plans based on them.

[0347] As a concrete example, the aforementioned system is particularly effective in providing fishing strategies that respond to changes in the marine environment caused by climate change. In this case, real-time feedback is collected, and the emotion engine analyzes users' reactions to conventional guidelines. Based on the analysis results, the server can regenerate and deliver appropriate guidelines tailored to the user, thereby improving user satisfaction and productivity.

[0348] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0349] Step 1:

[0350] The server collects behavioral and environmental data of aquatic organisms from measuring instruments and mobile devices installed in the ocean. The input is raw data acquired from various sensors and drones. This raw data includes environmental information such as location, water temperature, and oxygen concentration. The server periodically acquires this data and stores it in a database. The output is an unprocessed dataset.

[0351] Step 2:

[0352] The server preprocesses the collected raw data. This preprocessing removes outliers and transforms the data into a consistent format. Specifically, it performs data formatting, normalization, and outlier removal. The input is the raw dataset obtained in step 1, and the output is a formatted, consistent dataset.

[0353] Step 3:

[0354] The server performs analysis using an artificial intelligence algorithm with pre-processed data. The input is the dataset formatted in step 2. This analysis uses clustering techniques to analyze the behavioral patterns of aquatic organisms from the dataset. Specifically, it classifies the data into clusters and identifies specific movement patterns and breeding areas. The output is pattern data containing the analysis results.

[0355] Step 4:

[0356] The server generates fishing guidelines using a generative AI model based on the analysis results. The input is the pattern data obtained in step 3. Specifically, the AI ​​model generates appropriate guidelines using the prompt "Please propose guidelines for sustainable fisheries." The output is the generated fishing guidelines.

[0357] Step 5:

[0358] The server delivers the generated guidelines to the user via the terminal. The input is the guidelines generated in step 4. The terminal receives the guidelines and notifies the user. Specifically, it displays the guidelines on the screen or via audio using an information transmission protocol. The output is the guidelines presented in a format that the user can confirm.

[0359] Step 6:

[0360] The user sends feedback on the guidelines to the system via the terminal. The input is the guidelines presented in step 5. The user inputs the feedback as text or voice, and the terminal sends it to the server. The output is the user's feedback data.

[0361] Step 7:

[0362] The server processes the feedback data using an emotion analysis engine to identify the user's emotional state. The input is the feedback data obtained in step 6. Specifically, it analyzes text and audio data using natural language processing techniques to determine emotions. The output is the emotion analysis result.

[0363] Step 8:

[0364] The server adjusts and regenerates the guidelines based on the sentiment analysis results to suit the user. The input is the sentiment analysis results obtained in step 7. The server applies the adjustments to the guidelines according to the analysis results and considers alternatives as needed. The output is the adjusted guidelines.

[0365] Step 9:

[0366] The server redistributes the adjusted guidance to the terminal and notifies the user. The input is the adjusted guidance obtained in step 8. The terminal receives the redistributed guidance and notifies the user of it. The output is a re-presentation of the adjusted guidance.

[0367] (Application Example 2)

[0368] 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 as the "terminal".

[0369] In analyzing the behavior of aquatic organisms and generating environmental guidelines, it is difficult to reflect the individual emotions and feedback of users. Furthermore, because the information provided is not tailored to the user's interests and emotions, there is a challenge in providing an intuitive and satisfying experience for the user.

[0370] 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.

[0371] In this invention, the server includes means for collecting aquatic organism behavior data and environmental data; means for preprocessing the collected data to generate an integrated dataset; means for executing an information processing device that analyzes the behavior patterns of aquatic organisms using the integrated dataset; means for generating environmentally conscious usage guidelines based on the analysis results; means for transmitting the generated guidelines to a user device; means for analyzing the user's emotions using an emotion recognition device and adjusting the guidelines based on the analysis results; and means for individually providing the adjusted guidelines via a display device. This makes it possible to provide personalized guidelines that are adjusted based on the user's emotions and interests.

[0372] "Aquatic organism behavioral data" refers to information collected to analyze the movement and behavioral patterns of organisms living in water.

[0373] "Environmental data" refers to information about the conditions of the natural environment in which aquatic organisms live, including elements such as temperature, salinity, and light intensity.

[0374] "Preprocessing" is the process of transforming collected data into an analyzable format, including the removal of outliers and standardization of the format.

[0375] An "integrated dataset" is a collection of data that has undergone preprocessing and been organized into a unified format.

[0376] An "information processing device" is a computing device used for analyzing data and is programmed to perform specific processes.

[0377] An "emotion recognition device" is a system that analyzes a user's emotional state based on their voice, physiological responses, and other factors.

[0378] "Guidelines" are proposed guidelines for action to conserve and sustainably use aquatic life.

[0379] A "user device" is a device used by a user to receive guidelines and information.

[0380] A "display device" is a device used to visualize and present adjusted guidelines and analysis results to the user.

[0381] This invention realizes a system for collecting data on the behavior and environment of aquatic organisms and providing users with interactive, environmentally conscious guidelines based on that data. The server acquires behavioral and environmental data of aquatic organisms using underwater sensors and drones. This data is preprocessed, such as removing outliers and converting it to the required format, and then integrated into a consistent dataset.

[0382] The server uses an information processing unit to execute AI algorithms based on the integrated dataset. This allows for the analysis of biological movement and behavioral patterns. The hardware used includes underwater sensors and drones, while the software employs machine learning models based on Python.

[0383] Based on the analysis results, the server automatically generates environmentally conscious usage guidelines and distributes them to the user's device. To achieve this, the server uses an emotion recognition device to analyze feedback from the user. It then optimizes the guidelines according to the user's emotional state, providing personalized information.

[0384] A concrete example is an interactive experience where, as visitors move around the aquarium using smart glasses, the exhibits are adjusted in real time based on their interests and reactions. In this way, a feedback loop between the server and the user is used to provide more effective and convincing guidelines.

[0385] Examples of prompts include, "Consider a system that provides dynamic guidance based on user emotions during individual tours at an aquarium," and "Show how to use visitor emotion data to suggest content that might interest them about aquatic life."

[0386] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0387] Step 1:

[0388] The server acquires behavioral and environmental data of aquatic organisms from sensors and drones. This includes location information, movement patterns, and environmental conditions. The raw data obtained is transferred to the server. The input at this time is data from various sensors, and the output is raw data stored on the server.

[0389] Step 2:

[0390] The server preprocesses the input data and generates a unified dataset in a consistent format. Specifically, it detects and removes outliers and converts the data to a standard format. The input is raw data, and the output is a clean, formatted dataset.

[0391] Step 3:

[0392] The server uses an information processing device to analyze the behavioral patterns of aquatic organisms from an integrated dataset. At this stage, AI algorithms are used, particularly applying machine learning classification techniques. The input is the integrated dataset, and the output is the analyzed behavioral patterns.

[0393] Step 4:

[0394] The server generates environmentally conscious usage guidelines based on the analysis results. This generation process uses a generation AI model to automatically generate content that ensures the guidelines are sustainable. The input is the analysis results, and the output is the initial usage guidelines.

[0395] Step 5:

[0396] The user inputs feedback through the terminal. At this time, the terminal uses an emotion recognition device to analyze the user's emotional state and sends the data to the server. The input is the user's feedback and emotional data, and the output is the emotional analysis result sent to the server.

[0397] Step 6:

[0398] The server adjusts the guidelines based on the received sentiment analysis results, according to the user's emotions. The adjusted guidelines are designed to improve user interest and acceptance. The input is the sentiment analysis results and the initial guidelines, and the output is the adjusted usage guidelines.

[0399] Step 7:

[0400] The server delivers the revised usage guidelines to the user via a display device. The user optimizes their behavior based on the newly provided guidelines on their device. The input is the revised guidelines, and the output is the presentation of the guidelines to the user.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] [Third Embodiment]

[0405] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0406] 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.

[0407] 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).

[0408] 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.

[0409] 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.

[0410] 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).

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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".

[0417] This invention is a system aimed at analyzing the behavior of aquatic organisms and supporting their conservation. In particular, it utilizes AI technology to achieve precise data analysis and the generation of sustainable guidelines. Embodiments of this invention are described in detail below.

[0418] The server periodically collects behavioral and environmental data of aquatic organisms from sensors, satellites, and drones installed in the ocean. This data is diverse and includes information such as water temperature, salinity, current direction, and the location of organisms. The server immediately preprocesses the collected data, cleanses it to remove outliers, and standardizes the format.

[0419] Next, the server runs an AI algorithm using the pre-processed data. This AI utilizes machine learning and deep learning to analyze the behavioral patterns of aquatic organisms. The analysis identifies the migration routes of fish schools in a specific body of water and the breeding season in a particular time of year. For example, it can reveal the range to which a particular fish species that migrates in a certain region moves and breeds during warmer periods.

[0420] Based on the analysis results, the server generates customized fishing guidelines for fishermen and environmental groups. These guidelines include fishing limits for specific periods, permitted catch limits, and suggestions for conservation activities to be carried out in specific areas.

[0421] The generated guidelines are delivered to the device, making them easily accessible to users. Users can view this information through a dedicated application or web interface and develop specific action plans. For example, fishermen can adjust their fishing vessel departure schedules according to the breeding season and practice sustainable fishing activities that consider the ecosystem.

[0422] Thus, by utilizing AI technology, this invention enables detailed behavioral analysis of aquatic organisms and the provision of precise guidelines, thereby strengthening the conservation of marine ecosystems.

[0423] The following describes the processing flow.

[0424] Step 1:

[0425] The server collects real-time behavioral and environmental data of aquatic organisms through sensors, satellites, and drones installed in the ocean. This includes water temperature, salinity, and the location of organisms. The collected data is stored in the server's database.

[0426] Step 2:

[0427] The server begins preprocessing the collected raw data. It cleans the data by removing outliers using an anomaly detection algorithm. It also unifies different data formats and converts them into a consistent format.

[0428] Step 3:

[0429] The server inputs pre-processed data into an AI algorithm to analyze the behavioral patterns of aquatic organisms. This algorithm utilizes machine learning and clustering techniques to identify the migration routes and breeding seasons of specific fish groups.

[0430] Step 4:

[0431] The server generates fishing guidelines based on the results of AI analysis. These guidelines include recommendations for catch limits, fishing restrictions for specific periods, and suggestions for conservation activities in specific areas.

[0432] Step 5:

[0433] The server distributes the generated guidelines to the user's device. Distribution is done via email or a dedicated application, and is configured to be easily accessible to the user.

[0434] Step 6:

[0435] The device displays the received guidelines on the user interface. Through this, the user can review detailed analysis results and suggestions.

[0436] Step 7:

[0437] Based on the information provided, users develop fishing plans and conservation activities. For example, users adjust fishing vessel schedules to accommodate breeding seasons and implement environmentally conscious fishing practices.

[0438] (Example 1)

[0439] 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."

[0440] Current technologies for the conservation of aquatic organisms are insufficient to adequately analyze their behavior patterns and environmental changes, making it difficult to provide the precise guidelines necessary for sustainable fisheries and ecosystem conservation. Therefore, there is a need for a system that can efficiently and effectively collect and analyze data and generate clear guidelines.

[0441] 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.

[0442] In this invention, the server includes means for collecting information about aquatic organisms and the surrounding environment, means for preprocessing the collected information to generate an integrated information set, and means for executing artificial intelligence to analyze the behavior patterns of aquatic organisms using the integrated information set. This enables advanced analysis based on the collected data and the provision of specific, environmentally conscious fishing guidelines.

[0443] "Aquatic organisms" refers to all living things, including plants and animals, that inhabit water.

[0444] "Information" refers to data and knowledge collected for a specific purpose.

[0445] "Surrounding conditions" refer to the elements that constitute the habitat of aquatic organisms, and include environmental factors such as water temperature, salinity, and direction of current.

[0446] "Artificial intelligence" refers to technology that enables computers and systems to mimic human intellectual behavior and autonomously perform specific tasks.

[0447] A "classification method" refers to a statistical or machine learning technique that groups data according to specific criteria and divides it into categories based on their respective characteristics.

[0448] A "migration path" refers to the path or direction in which aquatic organisms move over time.

[0449] "Guidelines" refer to guidelines that provide direction or standards for actions and decisions.

[0450] This invention is constructed as a system to support the behavioral analysis and conservation of aquatic organisms. Specific embodiments are described below.

[0451] Data Collection: The server uses various sensor devices, satellites, and drones installed in the ocean to collect information about aquatic organisms and their surrounding environment. This information includes water temperature, salinity, current direction, and the location of organisms.

[0452] Data preprocessing: The server immediately preprocesses the collected information, performing cleansing to remove outliers. This improves data accuracy and standardizes it into a format suitable for analysis.

[0453] Data Analysis: The server drives artificial intelligence using pre-processed data. The server executes machine learning and deep learning algorithms to analyze the behavior patterns of specific aquatic organisms. For example, it can identify the migration routes of fish schools or the breeding season in specific times of the year.

[0454] Guideline Generation: Based on the analysis results, the server generates customized fishing guidelines for fishermen and environmental groups. These guidelines include suggestions for catch limits and adjustments to fishing activities during specific periods.

[0455] Guideline distribution: The server distributes the generated guidelines to the user's device, making the information more accessible to the user.

[0456] User Use: Users access the distributed guidelines via a dedicated application or web interface through their devices. Based on this information, users develop specific action plans and implement sustainable fishing and environmental conservation activities.

[0457] A concrete example is when fishermen adjust the operating schedules of their fishing vessels to suit specific seasons. An example of a prompt message could be: "Conduct a detailed analysis of water temperature and fish breeding patterns in a certain region and propose guidelines for sustainable fisheries."

[0458] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0459] Step 1:

[0460] The server collects data on aquatic organisms and the environment from sensor devices installed in the ocean. Sensor inputs include water temperature, salinity, current direction, and the location of organisms. This information is stored in a database in real time and output as a collected data set.

[0461] Step 2:

[0462] The server preprocesses the collected data. It detects noisy data and outliers and performs data cleaning. It receives the stored data as input, removes outliers through visual inspection, and outputs an integrated dataset converted to a standardized format.

[0463] Step 3:

[0464] The server inputs the integrated dataset into an AI algorithm. The artificial intelligence uses a machine learning model to analyze the behavior patterns of aquatic organisms. Based on the input data, it identifies the migration routes of fish schools circulating in a specific body of water using clustering techniques and outputs this as the analysis result.

[0465] Step 4:

[0466] The server generates fishing guidelines that include environmental considerations based on the analysis results. The AI ​​algorithm creates rule-based guidelines that incorporate fishing limits and conservation activities for specific periods. The generated guidelines are obtained as output.

[0467] Step 5:

[0468] The server delivers the generated guidelines to the user's terminal. The guideline data is transmitted over the network and output to the terminal in a format that the user can view.

[0469] Step 6:

[0470] Users receive guidelines via their devices and can verify them through a dedicated app or web interface. Based on this, users develop local action plans and implement sustainable fishing activities in accordance with the information received.

[0471] (Application Example 1)

[0472] 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."

[0473] In recent years, with the increasing importance of environmental protection, there has been a growing demand for the sustainable use of marine resources and environmentally conscious consumer behavior. However, there is a lack of information that makes it easy for consumers to find and use products based on sustainable fisheries. As a result, both fishermen and consumers face the challenge of having difficulty taking sustainable actions.

[0474] 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.

[0475] In this invention, the server includes means for collecting aquatic organism behavior data and environmental data; means for preprocessing the collected data to generate an integrated dataset; means for executing an AI algorithm that analyzes the behavior patterns of aquatic organisms using the integrated dataset; means for generating environmentally conscious fishing guidelines based on the analysis results; means for distributing the generated guidelines and store information based on sustainable fishing to the user terminal; and means for identifying and notifying the user of stores that provide sustainable products based on the user's location information. This enables consumers to choose sustainable products while minimizing their own environmental impact.

[0476] "Aquatic organism behavioral data" refers to information about the movement, reproduction, and predation behaviors of organisms living in oceans and freshwater.

[0477] "Environmental data" refers to information about the natural environment that affects aquatic organisms, such as water temperature, salinity, and current direction.

[0478] "Means of collection" refers to technologies and devices used to collect data, such as sensor equipment, satellites, and drones.

[0479] "Means for generating preprocessed and integrated datasets" refer to techniques and processes that cleanse and standardize data formats, and then reconstruct them into a user-friendly form.

[0480] "Means for executing AI algorithms" refer to technologies and systems that use machine learning or deep learning to extract specific patterns or features from data being analyzed.

[0481] "Means for generating environmentally conscious fisheries guidelines" refers to technologies and methods for creating guidelines and proposals for achieving sustainable fisheries based on analysis results.

[0482] "Means of distribution to user terminals" refers to the technologies and infrastructure for transmitting generated information to and displaying it on the user's digital terminal, such as a smartphone or computer.

[0483] "Store information based on sustainable fishing" refers to information about stores that handle products obtained through environmentally friendly fishing methods.

[0484] "Means for identifying and notifying users of stores that provide sustainable products based on user location information" refers to technologies and services that use geographic information systems to identify stores that provide products in a sustainable manner near the user's current location and to inform the user of that information.

[0485] The system for implementing this invention is designed to collect various data, analyze it, and provide users with useful information. The server first collects aquatic organism behavior data and environmental data from various devices such as marine and freshwater sensor equipment, satellites, and drones. This data includes water temperature, salinity, current direction, and location information. The collected data is quickly preprocessed to eliminate outliers and standardize the format, thereby improving the current situation and data accuracy.

[0486] Next, the server uses the pre-processed data to run AI algorithms and analyze the behavioral patterns of aquatic organisms. This analysis utilizes Python and deep learning libraries such as TensorFlow. As a result of the analysis, it predicts the migration routes of fish schools within a specific region and their seasonal breeding activities, and uses this data to generate environmentally conscious fishing guidelines. These guidelines include suggestions for catch limits and conservation activities based on sustainable development goals.

[0487] The generated guidelines and store information are delivered to the user's device using Flask. The application installed on the device uses the Google Maps API to obtain the user's location and identify and notify the user of nearby stores that offer sustainable products. For example, if the user is in a certain city, a list of restaurants and retail stores that serve fish obtained through sustainable fishing methods will be displayed based on their location.

[0488] Such a system allows users to choose sustainable products while minimizing their environmental impact. A concrete example of a prompt message would be, "I want to find a place that offers seafood that is environmentally conscious."

[0489] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0490] Step 1:

[0491] The server collects behavioral and environmental data of aquatic organisms from sensor devices, satellites, and drones. Input data includes water temperature, salinity, current direction, and location information. This data is integrated and filtered to generate a clean dataset. The output is a pre-processed dataset with a standardized format.

[0492] Step 2:

[0493] The server takes the dataset generated in Step 1 as input and executes an AI algorithm. Here, a machine learning model using TensorFlow is utilized to analyze the behavioral patterns of aquatic organisms. Specifically, clustering techniques are used to predict the migration routes and breeding seasons of specific fish schools. The output consists of the analyzed behavioral patterns and predicted data.

[0494] Step 3:

[0495] The server generates environmentally conscious guidelines based on the analysis results from Step 2. The input is the analysis results, which are used to develop guidelines for sustainable fishing activities. Specifically, this involves formulating proposals for fishing limits during specific periods and conservation activities in specific areas. The output is a guideline document.

[0496] Step 4:

[0497] The server delivers store information based on generated guidelines and aquatic life and environmental data to user terminals. Specifically, it uses Flask to provide the generated documents to the user's application. Input consists of guidelines and store information, and the server sends product suggestions tailored to the user's interests. Output is displayed as information viewable by the user.

[0498] Step 5:

[0499] The device utilizes the user's location information and the Google Maps API to identify and notify the user of nearby stores offering sustainable products. The input is the user's location, and the system cross-checks this information to identify nearby sustainable businesses. The output provides the user with specific store names and descriptions of the products offered.

[0500] 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.

[0501] This invention combines a system for analyzing the behavior and supporting the conservation of aquatic organisms with an emotion engine that recognizes user emotions. This emotion engine enables the generation and provision of flexible guidelines that take user feedback into consideration.

[0502] The server collects behavioral data from aquatic organisms using sensors and drones installed in the ocean. This data includes the location, movement patterns, and environmental conditions of the aquatic organisms. Once the data is collected, the server preprocesses it, removing outliers and integrating it into a consistent format.

[0503] Next, the server uses an AI algorithm to analyze the behavioral patterns of aquatic organisms from the pre-processed data. This AI performs machine learning techniques, including clustering, to identify specific migration routes and breeding patterns. Based on the analysis results, the server automatically generates environmentally conscious fishing guidelines.

[0504] The generated guidelines are refined into their final form through a process where the emotion engine recognizes the user's emotions. Users input opinions and feedback into the system via their terminals. By analyzing this input data and audio, the emotion engine identifies the user's emotional state. Based on this, the server adjusts the content and presentation method of the guidelines and delivers them in a format suitable for each individual user.

[0505] For example, if a user expresses dissatisfaction with the presented guidelines, the sentiment engine analyzes this, and the server generates new guidelines that consider alternatives. These new guidelines are then redistributed to the user's device as more convincing information. This adjustment enables the implementation of intuitive and effective fishing plans and conservation activities that reflect the user's intentions.

[0506] Thus, by incorporating an emotion engine, the present invention provides an advanced system that can dynamically reflect user feedback and support sustainable activities tailored to individual needs.

[0507] The following describes the processing flow.

[0508] Step 1:

[0509] The server collects behavioral and environmental data of aquatic organisms from sensors and drones installed in the ocean. This data is transmitted to the server in real time and stored in a database. The collected data includes water temperature, salinity, current strength, and location information.

[0510] Step 2:

[0511] The server organizes and preprocesses the collected raw data. It removes outliers and standardizes the data format as needed. In this process, it applies data cleansing and standardization algorithms to prepare a dataset suitable for analysis.

[0512] Step 3:

[0513] The server runs an AI algorithm using pre-processed data. This algorithm utilizes machine learning and clustering techniques to identify migration routes and breeding behaviors of aquatic organisms. The analysis results provide important insights into the characteristics and behavioral patterns of these organisms.

[0514] Step 4:

[0515] The server generates environmentally conscious fishing guidelines based on AI analysis results. These guidelines include suggestions such as fishing restrictions for specific periods and regions, and recommended catch limits. The generated guidelines are customized for each user.

[0516] Step 5:

[0517] The device collects user feedback. Users can input their thoughts and opinions on the guidelines through the device. Feedback can be in the form of text or audio.

[0518] Step 6:

[0519] The server uses an emotion engine to analyze user feedback and identify their emotional state. This analysis allows for a quantitative evaluation of user satisfaction and dissatisfaction, and identifies areas for improvement.

[0520] Step 7:

[0521] The server adjusts the content and presentation method of the guidelines, taking into account the analysis results of the emotion engine. It modifies the suggested content according to the user's emotions and delivers it to the user's terminal in an appropriate format. This enables the provision of information optimized for each user.

[0522] Step 8:

[0523] Users can view the adjusted guidelines on their devices and use them in actual fishing plans and conservation activities. Based on these adjustments, users can plan their activities and implement sustainable management practices.

[0524] (Example 2)

[0525] 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."

[0526] Conventional guideline systems for aquatic organism conservation and fisheries failed to adequately consider the feelings and opinions of users, resulting in difficulty in providing flexible guidelines tailored to individual needs. Furthermore, there was a lack of information necessary to conduct detailed analyses of environmental conditions and aquatic organism behavior patterns in order to implement more effective conservation activities.

[0527] 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.

[0528] In this invention, the server includes means for using a device equipped with the function of collecting aquatic organism behavioral data and environmental data; means for preprocessing the collected data to generate an integrated dataset; means for executing an artificial intelligence algorithm that analyzes the behavioral patterns of aquatic organisms using the integrated dataset; means for generating guidelines for environmentally conscious fishing based on the analysis results; means for delivering the generated guidelines to the user's device; means for analyzing feedback from the user and identifying the user's emotional state using an emotion analysis engine; and means for adjusting the guidelines generated based on the results of the emotion analysis to suit the user. This enables dynamic reflection of user feedback and sustainable activities that are environmentally conscious and meet individual needs.

[0529] "Aquatic organisms" is a general term for organisms that inhabit aquatic environments such as lakes, rivers, and oceans, and live in natural aquatic environments.

[0530] "Behavioral data" refers to data that includes information about the movements, habits, and migration patterns of aquatic organisms.

[0531] "Environmental data" refers to data that includes information indicating the conditions of the environment in which aquatic organisms live, such as water temperature, oxygen concentration, salinity, and current speed.

[0532] An "artificial intelligence algorithm" is a computational method that uses machine learning and data analysis to learn patterns from data and perform predictions and classifications.

[0533] An "emotion analysis engine" is software that recognizes the user's emotional state from their opinions and feedback through language and voice analysis.

[0534] "Guidelines" are a set of recommended procedures or policies provided to achieve a specific purpose or activity.

[0535] To implement this invention, the server first collects behavioral data of aquatic organisms and environmental data using multiple measuring instruments and mobile devices installed in the marine environment. Specifically, it operates devices such as water temperature sensors, acoustic depth sounders, and drones to collect the necessary data. The server then performs data cleaning on the collected data to remove outliers and improve data accuracy. Subsequently, it centralizes the data and prepares it in a format suitable for analysis.

[0536] The server performs analysis using artificial intelligence algorithms with the pre-processed data. This analysis uses clustering techniques, a type of machine learning technology, to explore the behavioral patterns and environmental adaptability of aquatic organisms. Using a generative AI model, fisheries guidelines are generated based on a series of prompts, "Please propose guidelines for sustainable fisheries."

[0537] Subsequently, the generated fishing guidelines are refined by analyzing user feedback using an emotion analysis engine. Users provide their thoughts and opinions to the system through their devices, and this information is processed by the emotion analysis engine. As a result, the server effectively optimizes and delivers the guidelines according to the user's emotional state. The refined guidelines are transmitted to the devices, enabling users to implement sustainable fishing plans based on them.

[0538] As a concrete example, the aforementioned system is particularly effective in providing fishing strategies that respond to changes in the marine environment caused by climate change. In this case, real-time feedback is collected, and the emotion engine analyzes users' reactions to conventional guidelines. Based on the analysis results, the server can regenerate and deliver appropriate guidelines tailored to the user, thereby improving user satisfaction and productivity.

[0539] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0540] Step 1:

[0541] The server collects behavioral and environmental data of aquatic organisms from measuring instruments and mobile devices installed in the ocean. The input is raw data acquired from various sensors and drones. This raw data includes environmental information such as location, water temperature, and oxygen concentration. The server periodically acquires this data and stores it in a database. The output is an unprocessed dataset.

[0542] Step 2:

[0543] The server preprocesses the collected raw data. This preprocessing removes outliers and transforms the data into a consistent format. Specifically, it performs data formatting, normalization, and outlier removal. The input is the raw dataset obtained in step 1, and the output is a formatted, consistent dataset.

[0544] Step 3:

[0545] The server performs analysis using an artificial intelligence algorithm with pre-processed data. The input is the dataset formatted in step 2. This analysis uses clustering techniques to analyze the behavioral patterns of aquatic organisms from the dataset. Specifically, it classifies the data into clusters and identifies specific movement patterns and breeding areas. The output is pattern data containing the analysis results.

[0546] Step 4:

[0547] The server generates fishing guidelines using a generative AI model based on the analysis results. The input is the pattern data obtained in step 3. Specifically, the AI ​​model generates appropriate guidelines using the prompt "Please propose guidelines for sustainable fisheries." The output is the generated fishing guidelines.

[0548] Step 5:

[0549] The server delivers the generated guidelines to the user via the terminal. The input is the guidelines generated in step 4. The terminal receives the guidelines and notifies the user. Specifically, it displays the guidelines on the screen or via audio using an information transmission protocol. The output is the guidelines presented in a format that the user can confirm.

[0550] Step 6:

[0551] The user sends feedback on the guidelines to the system via the terminal. The input is the guidelines presented in step 5. The user inputs the feedback as text or voice, and the terminal sends it to the server. The output is the user's feedback data.

[0552] Step 7:

[0553] The server processes the feedback data using an emotion analysis engine to identify the user's emotional state. The input is the feedback data obtained in step 6. Specifically, it analyzes text and audio data using natural language processing techniques to determine emotions. The output is the emotion analysis result.

[0554] Step 8:

[0555] The server adjusts and regenerates the guidelines based on the sentiment analysis results to suit the user. The input is the sentiment analysis results obtained in step 7. The server applies the adjustments to the guidelines according to the analysis results and considers alternatives as needed. The output is the adjusted guidelines.

[0556] Step 9:

[0557] The server redistributes the adjusted guidance to the terminal and notifies the user. The input is the adjusted guidance obtained in step 8. The terminal receives the redistributed guidance and notifies the user of it. The output is a re-presentation of the adjusted guidance.

[0558] (Application Example 2)

[0559] 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."

[0560] In analyzing the behavior of aquatic organisms and generating environmental guidelines, it is difficult to reflect the individual emotions and feedback of users. Furthermore, because the information provided is not tailored to the user's interests and emotions, there is a challenge in providing an intuitive and satisfying experience for the user.

[0561] 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.

[0562] In this invention, the server includes means for collecting aquatic organism behavior data and environmental data; means for preprocessing the collected data to generate an integrated dataset; means for executing an information processing device that analyzes the behavior patterns of aquatic organisms using the integrated dataset; means for generating environmentally conscious usage guidelines based on the analysis results; means for transmitting the generated guidelines to a user device; means for analyzing the user's emotions using an emotion recognition device and adjusting the guidelines based on the analysis results; and means for individually providing the adjusted guidelines via a display device. This makes it possible to provide personalized guidelines that are adjusted based on the user's emotions and interests.

[0563] "Aquatic organism behavioral data" refers to information collected to analyze the movement and behavioral patterns of organisms living in water.

[0564] "Environmental data" refers to information about the conditions of the natural environment in which aquatic organisms live, including elements such as temperature, salinity, and light intensity.

[0565] "Preprocessing" is the process of transforming collected data into an analyzable format, including the removal of outliers and standardization of the format.

[0566] An "integrated dataset" is a collection of data that has undergone preprocessing and been organized into a unified format.

[0567] An "information processing device" is a computing device used for analyzing data and is programmed to perform specific processes.

[0568] An "emotion recognition device" is a system that analyzes a user's emotional state based on their voice, physiological responses, and other factors.

[0569] "Guidelines" are proposed guidelines for action to conserve and sustainably use aquatic life.

[0570] A "user device" is a device used by a user to receive guidelines and information.

[0571] A "display device" is a device used to visualize and present adjusted guidelines and analysis results to the user.

[0572] This invention realizes a system for collecting data on the behavior and environment of aquatic organisms and providing users with interactive, environmentally conscious guidelines based on that data. The server acquires behavioral and environmental data of aquatic organisms using underwater sensors and drones. This data is preprocessed, such as removing outliers and converting it to the required format, and then integrated into a consistent dataset.

[0573] The server uses an information processing unit to execute AI algorithms based on the integrated dataset. This allows for the analysis of biological movement and behavioral patterns. The hardware used includes underwater sensors and drones, while the software employs machine learning models based on Python.

[0574] Based on the analysis results, the server automatically generates environmentally conscious usage guidelines and distributes them to the user's device. To achieve this, the server uses an emotion recognition device to analyze feedback from the user. It then optimizes the guidelines according to the user's emotional state, providing personalized information.

[0575] A concrete example is an interactive experience where, as visitors move around the aquarium using smart glasses, the exhibits are adjusted in real time based on their interests and reactions. In this way, a feedback loop between the server and the user is used to provide more effective and convincing guidelines.

[0576] Examples of prompts include, "Consider a system that provides dynamic guidance based on user emotions during individual tours at an aquarium," and "Show how to use visitor emotion data to suggest content that might interest them about aquatic life."

[0577] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0578] Step 1:

[0579] The server acquires behavioral and environmental data of aquatic organisms from sensors and drones. This includes location information, movement patterns, and environmental conditions. The raw data obtained is transferred to the server. The input at this time is data from various sensors, and the output is raw data stored on the server.

[0580] Step 2:

[0581] The server preprocesses the input data and generates a unified dataset in a consistent format. Specifically, it detects and removes outliers and converts the data to a standard format. The input is raw data, and the output is a clean, formatted dataset.

[0582] Step 3:

[0583] The server uses an information processing device to analyze the behavioral patterns of aquatic organisms from an integrated dataset. At this stage, AI algorithms are used, particularly applying machine learning classification techniques. The input is the integrated dataset, and the output is the analyzed behavioral patterns.

[0584] Step 4:

[0585] The server generates environmentally conscious usage guidelines based on the analysis results. This generation process uses a generation AI model to automatically generate content that ensures the guidelines are sustainable. The input is the analysis results, and the output is the initial usage guidelines.

[0586] Step 5:

[0587] The user inputs feedback through the terminal. At this time, the terminal uses an emotion recognition device to analyze the user's emotional state and sends the data to the server. The input is the user's feedback and emotional data, and the output is the emotional analysis result sent to the server.

[0588] Step 6:

[0589] The server adjusts the guidelines based on the received sentiment analysis results, according to the user's emotions. The adjusted guidelines are designed to improve user interest and acceptance. The input is the sentiment analysis results and the initial guidelines, and the output is the adjusted usage guidelines.

[0590] Step 7:

[0591] The server delivers the revised usage guidelines to the user via a display device. The user optimizes their behavior based on the newly provided guidelines on their device. The input is the revised guidelines, and the output is the presentation of the guidelines to the user.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] [Fourth Embodiment]

[0596] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0597] 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.

[0598] 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).

[0599] 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.

[0600] 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.

[0601] 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).

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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".

[0609] This invention is a system aimed at analyzing the behavior of aquatic organisms and supporting their conservation. In particular, it utilizes AI technology to achieve precise data analysis and the generation of sustainable guidelines. Embodiments of this invention are described in detail below.

[0610] The server periodically collects behavioral and environmental data of aquatic organisms from sensors, satellites, and drones installed in the ocean. This data is diverse and includes information such as water temperature, salinity, current direction, and the location of organisms. The server immediately preprocesses the collected data, cleanses it to remove outliers, and standardizes the format.

[0611] Next, the server runs an AI algorithm using the pre-processed data. This AI utilizes machine learning and deep learning to analyze the behavioral patterns of aquatic organisms. The analysis identifies the migration routes of fish schools in a specific body of water and the breeding season in a particular time of year. For example, it can reveal the range to which a particular fish species that migrates in a certain region moves and breeds during warmer periods.

[0612] Based on the analysis results, the server generates customized fishing guidelines for fishermen and environmental groups. These guidelines include fishing limits for specific periods, permitted catch limits, and suggestions for conservation activities to be carried out in specific areas.

[0613] The generated guidelines are delivered to the device, making them easily accessible to users. Users can view this information through a dedicated application or web interface and develop specific action plans. For example, fishermen can adjust their fishing vessel departure schedules according to the breeding season and practice sustainable fishing activities that consider the ecosystem.

[0614] Thus, by utilizing AI technology, this invention enables detailed behavioral analysis of aquatic organisms and the provision of precise guidelines, thereby strengthening the conservation of marine ecosystems.

[0615] The following describes the processing flow.

[0616] Step 1:

[0617] The server collects real-time behavioral and environmental data of aquatic organisms through sensors, satellites, and drones installed in the ocean. This includes water temperature, salinity, and the location of organisms. The collected data is stored in the server's database.

[0618] Step 2:

[0619] The server begins preprocessing the collected raw data. It cleans the data by removing outliers using an anomaly detection algorithm. It also unifies different data formats and converts them into a consistent format.

[0620] Step 3:

[0621] The server inputs pre-processed data into an AI algorithm to analyze the behavioral patterns of aquatic organisms. This algorithm utilizes machine learning and clustering techniques to identify the migration routes and breeding seasons of specific fish groups.

[0622] Step 4:

[0623] The server generates fishing guidelines based on the results of AI analysis. These guidelines include recommendations for catch limits, fishing restrictions for specific periods, and suggestions for conservation activities in specific areas.

[0624] Step 5:

[0625] The server distributes the generated guidelines to the user's device. Distribution is done via email or a dedicated application, and is configured to be easily accessible to the user.

[0626] Step 6:

[0627] The device displays the received guidelines on the user interface. Through this, the user can review detailed analysis results and suggestions.

[0628] Step 7:

[0629] Based on the information provided, users develop fishing plans and conservation activities. For example, users adjust fishing vessel schedules to accommodate breeding seasons and implement environmentally conscious fishing practices.

[0630] (Example 1)

[0631] 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".

[0632] Current technologies for the conservation of aquatic organisms are insufficient to adequately analyze their behavior patterns and environmental changes, making it difficult to provide the precise guidelines necessary for sustainable fisheries and ecosystem conservation. Therefore, there is a need for a system that can efficiently and effectively collect and analyze data and generate clear guidelines.

[0633] 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.

[0634] In this invention, the server includes means for collecting information about aquatic organisms and the surrounding environment, means for preprocessing the collected information to generate an integrated information set, and means for executing artificial intelligence to analyze the behavior patterns of aquatic organisms using the integrated information set. This enables advanced analysis based on the collected data and the provision of specific, environmentally conscious fishing guidelines.

[0635] "Aquatic organisms" refers to all living things, including plants and animals, that inhabit water.

[0636] "Information" refers to data and knowledge collected for a specific purpose.

[0637] "Surrounding conditions" refer to the elements that constitute the habitat of aquatic organisms, and include environmental factors such as water temperature, salinity, and direction of current.

[0638] "Artificial intelligence" refers to technology that enables computers and systems to mimic human intellectual behavior and autonomously perform specific tasks.

[0639] A "classification method" refers to a statistical or machine learning technique that groups data according to specific criteria and divides it into categories based on their respective characteristics.

[0640] A "migration path" refers to the path or direction in which aquatic organisms move over time.

[0641] "Guidelines" refer to guidelines that provide direction or standards for actions and decisions.

[0642] This invention is constructed as a system to support the behavioral analysis and conservation of aquatic organisms. Specific embodiments are described below.

[0643] Data Collection: The server uses various sensor devices, satellites, and drones installed in the ocean to collect information about aquatic organisms and their surrounding environment. This information includes water temperature, salinity, current direction, and the location of organisms.

[0644] Data preprocessing: The server immediately preprocesses the collected information, performing cleansing to remove outliers. This improves data accuracy and standardizes it into a format suitable for analysis.

[0645] Data Analysis: The server drives artificial intelligence using pre-processed data. The server executes machine learning and deep learning algorithms to analyze the behavior patterns of specific aquatic organisms. For example, it can identify the migration routes of fish schools or the breeding season in specific times of the year.

[0646] Guideline Generation: Based on the analysis results, the server generates customized fishing guidelines for fishermen and environmental groups. These guidelines include suggestions for catch limits and adjustments to fishing activities during specific periods.

[0647] Guideline distribution: The server distributes the generated guidelines to the user's device, making the information more accessible to the user.

[0648] User Use: Users access the distributed guidelines via a dedicated application or web interface through their devices. Based on this information, users develop specific action plans and implement sustainable fishing and environmental conservation activities.

[0649] A concrete example is when fishermen adjust the operating schedules of their fishing vessels to suit specific seasons. An example of a prompt message could be: "Conduct a detailed analysis of water temperature and fish breeding patterns in a certain region and propose guidelines for sustainable fisheries."

[0650] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0651] Step 1:

[0652] The server collects data on aquatic organisms and the environment from sensor devices installed in the ocean. Sensor inputs include water temperature, salinity, current direction, and the location of organisms. This information is stored in a database in real time and output as a collected data set.

[0653] Step 2:

[0654] The server preprocesses the collected data. It detects noisy data and outliers and performs data cleaning. It receives the stored data as input, removes outliers through visual inspection, and outputs an integrated dataset converted to a standardized format.

[0655] Step 3:

[0656] The server inputs the integrated dataset into an AI algorithm. The artificial intelligence uses a machine learning model to analyze the behavior patterns of aquatic organisms. Based on the input data, it identifies the migration routes of fish schools circulating in a specific body of water using clustering techniques and outputs this as the analysis result.

[0657] Step 4:

[0658] The server generates fishing guidelines that include environmental considerations based on the analysis results. The AI ​​algorithm creates rule-based guidelines that incorporate fishing limits and conservation activities for specific periods. The generated guidelines are obtained as output.

[0659] Step 5:

[0660] The server delivers the generated guidelines to the user's terminal. The guideline data is transmitted over the network and output to the terminal in a format that the user can view.

[0661] Step 6:

[0662] Users receive guidelines via their devices and can verify them through a dedicated app or web interface. Based on this, users develop local action plans and implement sustainable fishing activities in accordance with the information received.

[0663] (Application Example 1)

[0664] 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".

[0665] In recent years, with the increasing importance of environmental protection, there has been a growing demand for the sustainable use of marine resources and environmentally conscious consumer behavior. However, there is a lack of information that makes it easy for consumers to find and use products based on sustainable fisheries. As a result, both fishermen and consumers face the challenge of having difficulty taking sustainable actions.

[0666] 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.

[0667] In this invention, the server includes means for collecting aquatic organism behavior data and environmental data; means for preprocessing the collected data to generate an integrated dataset; means for executing an AI algorithm that analyzes the behavior patterns of aquatic organisms using the integrated dataset; means for generating environmentally conscious fishing guidelines based on the analysis results; means for distributing the generated guidelines and store information based on sustainable fishing to the user terminal; and means for identifying and notifying the user of stores that provide sustainable products based on the user's location information. This enables consumers to choose sustainable products while minimizing their own environmental impact.

[0668] "Aquatic organism behavioral data" refers to information about the movement, reproduction, and predation behaviors of organisms living in oceans and freshwater.

[0669] "Environmental data" refers to information about the natural environment that affects aquatic organisms, such as water temperature, salinity, and current direction.

[0670] "Means of collection" refers to technologies and devices used to collect data, such as sensor equipment, satellites, and drones.

[0671] "Means for generating preprocessed and integrated datasets" refer to techniques and processes that cleanse and standardize data formats, and then reconstruct them into a user-friendly form.

[0672] "Means for executing AI algorithms" refer to technologies and systems that use machine learning or deep learning to extract specific patterns or features from data being analyzed.

[0673] "Means for generating environmentally conscious fisheries guidelines" refers to technologies and methods for creating guidelines and proposals for achieving sustainable fisheries based on analysis results.

[0674] "Means of distribution to user terminals" refers to the technologies and infrastructure for transmitting generated information to and displaying it on the user's digital terminal, such as a smartphone or computer.

[0675] "Store information based on sustainable fishing" refers to information about stores that handle products obtained through environmentally friendly fishing methods.

[0676] "Means for identifying and notifying users of stores that provide sustainable products based on user location information" refers to technologies and services that use geographic information systems to identify stores that provide products in a sustainable manner near the user's current location and to inform the user of that information.

[0677] The system for implementing this invention is designed to collect various data, analyze it, and provide users with useful information. The server first collects aquatic organism behavior data and environmental data from various devices such as marine and freshwater sensor equipment, satellites, and drones. This data includes water temperature, salinity, current direction, and location information. The collected data is quickly preprocessed to eliminate outliers and standardize the format, thereby improving the current situation and data accuracy.

[0678] Next, the server uses the pre-processed data to run AI algorithms and analyze the behavioral patterns of aquatic organisms. This analysis utilizes Python and deep learning libraries such as TensorFlow. As a result of the analysis, it predicts the migration routes of fish schools within a specific region and their seasonal breeding activities, and uses this data to generate environmentally conscious fishing guidelines. These guidelines include suggestions for catch limits and conservation activities based on sustainable development goals.

[0679] The generated guidelines and store information are delivered to the user's device using Flask. The application installed on the device uses the Google Maps API to obtain the user's location and identify and notify the user of nearby stores that offer sustainable products. For example, if the user is in a certain city, a list of restaurants and retail stores that serve fish obtained through sustainable fishing methods will be displayed based on their location.

[0680] Such a system allows users to choose sustainable products while minimizing their environmental impact. A concrete example of a prompt message would be, "I want to find a place that offers seafood that is environmentally conscious."

[0681] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0682] Step 1:

[0683] The server collects behavioral and environmental data of aquatic organisms from sensor devices, satellites, and drones. Input data includes water temperature, salinity, current direction, and location information. This data is integrated and filtered to generate a clean dataset. The output is a pre-processed dataset with a standardized format.

[0684] Step 2:

[0685] The server takes the dataset generated in Step 1 as input and executes an AI algorithm. Here, a machine learning model using TensorFlow is utilized to analyze the behavioral patterns of aquatic organisms. Specifically, clustering techniques are used to predict the migration routes and breeding seasons of specific fish schools. The output consists of the analyzed behavioral patterns and predicted data.

[0686] Step 3:

[0687] The server generates environmentally conscious guidelines based on the analysis results from Step 2. The input is the analysis results, which are used to develop guidelines for sustainable fishing activities. Specifically, this involves formulating proposals for fishing limits during specific periods and conservation activities in specific areas. The output is a guideline document.

[0688] Step 4:

[0689] The server delivers store information based on generated guidelines and aquatic life and environmental data to user terminals. Specifically, it uses Flask to provide the generated documents to the user's application. Input consists of guidelines and store information, and the server sends product suggestions tailored to the user's interests. Output is displayed as information viewable by the user.

[0690] Step 5:

[0691] The device utilizes the user's location information and the Google Maps API to identify and notify the user of nearby stores offering sustainable products. The input is the user's location, and the system cross-checks this information to identify nearby sustainable businesses. The output provides the user with specific store names and descriptions of the products offered.

[0692] 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.

[0693] This invention combines a system for analyzing the behavior and supporting the conservation of aquatic organisms with an emotion engine that recognizes user emotions. This emotion engine enables the generation and provision of flexible guidelines that take user feedback into consideration.

[0694] The server collects behavioral data from aquatic organisms using sensors and drones installed in the ocean. This data includes the location, movement patterns, and environmental conditions of the aquatic organisms. Once the data is collected, the server preprocesses it, removing outliers and integrating it into a consistent format.

[0695] Next, the server uses an AI algorithm to analyze the behavioral patterns of aquatic organisms from the pre-processed data. This AI performs machine learning techniques, including clustering, to identify specific migration routes and breeding patterns. Based on the analysis results, the server automatically generates environmentally conscious fishing guidelines.

[0696] The generated guidelines are refined into their final form through a process where the emotion engine recognizes the user's emotions. Users input opinions and feedback into the system via their terminals. By analyzing this input data and audio, the emotion engine identifies the user's emotional state. Based on this, the server adjusts the content and presentation method of the guidelines and delivers them in a format suitable for each individual user.

[0697] For example, if a user expresses dissatisfaction with the presented guidelines, the sentiment engine analyzes this, and the server generates new guidelines that consider alternatives. These new guidelines are then redistributed to the user's device as more convincing information. This adjustment enables the implementation of intuitive and effective fishing plans and conservation activities that reflect the user's intentions.

[0698] Thus, by incorporating an emotion engine, the present invention provides an advanced system that can dynamically reflect user feedback and support sustainable activities tailored to individual needs.

[0699] The following describes the processing flow.

[0700] Step 1:

[0701] The server collects behavioral and environmental data of aquatic organisms from sensors and drones installed in the ocean. This data is transmitted to the server in real time and stored in a database. The collected data includes water temperature, salinity, current strength, and location information.

[0702] Step 2:

[0703] The server organizes and preprocesses the collected raw data. It removes outliers and standardizes the data format as needed. In this process, it applies data cleansing and standardization algorithms to prepare a dataset suitable for analysis.

[0704] Step 3:

[0705] The server runs an AI algorithm using pre-processed data. This algorithm utilizes machine learning and clustering techniques to identify migration routes and breeding behaviors of aquatic organisms. The analysis results provide important insights into the characteristics and behavioral patterns of these organisms.

[0706] Step 4:

[0707] The server generates environmentally conscious fishing guidelines based on AI analysis results. These guidelines include suggestions such as fishing restrictions for specific periods and regions, and recommended catch limits. The generated guidelines are customized for each user.

[0708] Step 5:

[0709] The device collects user feedback. Users can input their thoughts and opinions on the guidelines through the device. Feedback can be in the form of text or audio.

[0710] Step 6:

[0711] The server uses an emotion engine to analyze user feedback and identify their emotional state. This analysis allows for a quantitative evaluation of user satisfaction and dissatisfaction, and identifies areas for improvement.

[0712] Step 7:

[0713] The server adjusts the content and presentation method of the guidelines, taking into account the analysis results of the emotion engine. It modifies the suggested content according to the user's emotions and delivers it to the user's terminal in an appropriate format. This enables the provision of information optimized for each user.

[0714] Step 8:

[0715] Users can view the adjusted guidelines on their devices and use them in actual fishing plans and conservation activities. Based on these adjustments, users can plan their activities and implement sustainable management practices.

[0716] (Example 2)

[0717] 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".

[0718] Conventional guideline systems for aquatic organism conservation and fisheries failed to adequately consider the feelings and opinions of users, resulting in difficulty in providing flexible guidelines tailored to individual needs. Furthermore, there was a lack of information necessary to conduct detailed analyses of environmental conditions and aquatic organism behavior patterns in order to implement more effective conservation activities.

[0719] 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.

[0720] In this invention, the server includes means for using a device equipped with the function of collecting aquatic organism behavioral data and environmental data; means for preprocessing the collected data to generate an integrated dataset; means for executing an artificial intelligence algorithm that analyzes the behavioral patterns of aquatic organisms using the integrated dataset; means for generating guidelines for environmentally conscious fishing based on the analysis results; means for delivering the generated guidelines to the user's device; means for analyzing feedback from the user and identifying the user's emotional state using an emotion analysis engine; and means for adjusting the guidelines generated based on the results of the emotion analysis to suit the user. This enables dynamic reflection of user feedback and sustainable activities that are environmentally conscious and meet individual needs.

[0721] "Aquatic organisms" is a general term for organisms that inhabit aquatic environments such as lakes, rivers, and oceans, and live in natural aquatic environments.

[0722] "Behavioral data" refers to data that includes information about the movements, habits, and migration patterns of aquatic organisms.

[0723] "Environmental data" refers to data that includes information indicating the conditions of the environment in which aquatic organisms live, such as water temperature, oxygen concentration, salinity, and current speed.

[0724] An "artificial intelligence algorithm" is a computational method that uses machine learning and data analysis to learn patterns from data and perform predictions and classifications.

[0725] An "emotion analysis engine" is software that recognizes the user's emotional state from their opinions and feedback through language and voice analysis.

[0726] "Guidelines" are a set of recommended procedures or policies provided to achieve a specific purpose or activity.

[0727] To implement this invention, the server first collects behavioral data of aquatic organisms and environmental data using multiple measuring instruments and mobile devices installed in the marine environment. Specifically, it operates devices such as water temperature sensors, acoustic depth sounders, and drones to collect the necessary data. The server then performs data cleaning on the collected data to remove outliers and improve data accuracy. Subsequently, it centralizes the data and prepares it in a format suitable for analysis.

[0728] The server performs analysis using artificial intelligence algorithms with the pre-processed data. This analysis uses clustering techniques, a type of machine learning technology, to explore the behavioral patterns and environmental adaptability of aquatic organisms. Using a generative AI model, fisheries guidelines are generated based on a series of prompts, "Please propose guidelines for sustainable fisheries."

[0729] Subsequently, the generated fishing guidelines are refined by analyzing user feedback using an emotion analysis engine. Users provide their thoughts and opinions to the system through their devices, and this information is processed by the emotion analysis engine. As a result, the server effectively optimizes and delivers the guidelines according to the user's emotional state. The refined guidelines are transmitted to the devices, enabling users to implement sustainable fishing plans based on them.

[0730] As a concrete example, the aforementioned system is particularly effective in providing fishing strategies that respond to changes in the marine environment caused by climate change. In this case, real-time feedback is collected, and the emotion engine analyzes users' reactions to conventional guidelines. Based on the analysis results, the server can regenerate and deliver appropriate guidelines tailored to the user, thereby improving user satisfaction and productivity.

[0731] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0732] Step 1:

[0733] The server collects behavioral and environmental data of aquatic organisms from measuring instruments and mobile devices installed in the ocean. The input is raw data acquired from various sensors and drones. This raw data includes environmental information such as location, water temperature, and oxygen concentration. The server periodically acquires this data and stores it in a database. The output is an unprocessed dataset.

[0734] Step 2:

[0735] The server preprocesses the collected raw data. This preprocessing removes outliers and transforms the data into a consistent format. Specifically, it performs data formatting, normalization, and outlier removal. The input is the raw dataset obtained in step 1, and the output is a formatted, consistent dataset.

[0736] Step 3:

[0737] The server performs analysis using an artificial intelligence algorithm with pre-processed data. The input is the dataset formatted in step 2. This analysis uses clustering techniques to analyze the behavioral patterns of aquatic organisms from the dataset. Specifically, it classifies the data into clusters and identifies specific movement patterns and breeding areas. The output is pattern data containing the analysis results.

[0738] Step 4:

[0739] The server generates fishing guidelines using a generative AI model based on the analysis results. The input is the pattern data obtained in step 3. Specifically, the AI ​​model generates appropriate guidelines using the prompt "Please propose guidelines for sustainable fisheries." The output is the generated fishing guidelines.

[0740] Step 5:

[0741] The server delivers the generated guidelines to the user via the terminal. The input is the guidelines generated in step 4. The terminal receives the guidelines and notifies the user. Specifically, it displays the guidelines on the screen or via audio using an information transmission protocol. The output is the guidelines presented in a format that the user can confirm.

[0742] Step 6:

[0743] The user sends feedback on the guidelines to the system via the terminal. The input is the guidelines presented in step 5. The user inputs the feedback as text or voice, and the terminal sends it to the server. The output is the user's feedback data.

[0744] Step 7:

[0745] The server processes the feedback data using an emotion analysis engine to identify the user's emotional state. The input is the feedback data obtained in step 6. Specifically, it analyzes text and audio data using natural language processing techniques to determine emotions. The output is the emotion analysis result.

[0746] Step 8:

[0747] The server adjusts and regenerates the guidelines based on the sentiment analysis results to suit the user. The input is the sentiment analysis results obtained in step 7. The server applies the adjustments to the guidelines according to the analysis results and considers alternatives as needed. The output is the adjusted guidelines.

[0748] Step 9:

[0749] The server redistributes the adjusted guidance to the terminal and notifies the user. The input is the adjusted guidance obtained in step 8. The terminal receives the redistributed guidance and notifies the user of it. The output is a re-presentation of the adjusted guidance.

[0750] (Application Example 2)

[0751] 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".

[0752] In analyzing the behavior of aquatic organisms and generating environmental guidelines, it is difficult to reflect the individual emotions and feedback of users. Furthermore, because the information provided is not tailored to the user's interests and emotions, there is a challenge in providing an intuitive and satisfying experience for the user.

[0753] 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.

[0754] In this invention, the server includes means for collecting aquatic organism behavior data and environmental data; means for preprocessing the collected data to generate an integrated dataset; means for executing an information processing device that analyzes the behavior patterns of aquatic organisms using the integrated dataset; means for generating environmentally conscious usage guidelines based on the analysis results; means for transmitting the generated guidelines to a user device; means for analyzing the user's emotions using an emotion recognition device and adjusting the guidelines based on the analysis results; and means for individually providing the adjusted guidelines via a display device. This makes it possible to provide personalized guidelines that are adjusted based on the user's emotions and interests.

[0755] "Aquatic organism behavioral data" refers to information collected to analyze the movement and behavioral patterns of organisms living in water.

[0756] "Environmental data" refers to information about the conditions of the natural environment in which aquatic organisms live, including elements such as temperature, salinity, and light intensity.

[0757] "Preprocessing" is the process of transforming collected data into an analyzable format, including the removal of outliers and standardization of the format.

[0758] An "integrated dataset" is a collection of data that has undergone preprocessing and been organized into a unified format.

[0759] An "information processing device" is a computing device used for analyzing data and is programmed to perform specific processes.

[0760] An "emotion recognition device" is a system that analyzes a user's emotional state based on their voice, physiological responses, and other factors.

[0761] "Guidelines" are proposed guidelines for action to conserve and sustainably use aquatic life.

[0762] A "user device" is a device used by a user to receive guidelines and information.

[0763] A "display device" is a device used to visualize and present adjusted guidelines and analysis results to the user.

[0764] This invention realizes a system for collecting data on the behavior and environment of aquatic organisms and providing users with interactive, environmentally conscious guidelines based on that data. The server acquires behavioral and environmental data of aquatic organisms using underwater sensors and drones. This data is preprocessed, such as removing outliers and converting it to the required format, and then integrated into a consistent dataset.

[0765] The server uses an information processing unit to execute AI algorithms based on the integrated dataset. This allows for the analysis of biological movement and behavioral patterns. The hardware used includes underwater sensors and drones, while the software employs machine learning models based on Python.

[0766] Based on the analysis results, the server automatically generates environmentally conscious usage guidelines and distributes them to the user's device. To achieve this, the server uses an emotion recognition device to analyze feedback from the user. It then optimizes the guidelines according to the user's emotional state, providing personalized information.

[0767] A concrete example is an interactive experience where, as visitors move around the aquarium using smart glasses, the exhibits are adjusted in real time based on their interests and reactions. In this way, a feedback loop between the server and the user is used to provide more effective and convincing guidelines.

[0768] Examples of prompts include, "Consider a system that provides dynamic guidance based on user emotions during individual tours at an aquarium," and "Show how to use visitor emotion data to suggest content that might interest them about aquatic life."

[0769] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0770] Step 1:

[0771] The server acquires behavioral and environmental data of aquatic organisms from sensors and drones. This includes location information, movement patterns, and environmental conditions. The raw data obtained is transferred to the server. The input at this time is data from various sensors, and the output is raw data stored on the server.

[0772] Step 2:

[0773] The server preprocesses the input data and generates a unified dataset in a consistent format. Specifically, it detects and removes outliers and converts the data to a standard format. The input is raw data, and the output is a clean, formatted dataset.

[0774] Step 3:

[0775] The server uses an information processing device to analyze the behavioral patterns of aquatic organisms from an integrated dataset. At this stage, AI algorithms are used, particularly applying machine learning classification techniques. The input is the integrated dataset, and the output is the analyzed behavioral patterns.

[0776] Step 4:

[0777] The server generates environmentally conscious usage guidelines based on the analysis results. This generation process uses a generation AI model to automatically generate content that ensures the guidelines are sustainable. The input is the analysis results, and the output is the initial usage guidelines.

[0778] Step 5:

[0779] The user inputs feedback through the terminal. At this time, the terminal uses an emotion recognition device to analyze the user's emotional state and sends the data to the server. The input is the user's feedback and emotional data, and the output is the emotional analysis result sent to the server.

[0780] Step 6:

[0781] The server adjusts the guidelines based on the received sentiment analysis results, according to the user's emotions. The adjusted guidelines are designed to improve user interest and acceptance. The input is the sentiment analysis results and the initial guidelines, and the output is the adjusted usage guidelines.

[0782] Step 7:

[0783] The server delivers the revised usage guidelines to the user via a display device. The user optimizes their behavior based on the newly provided guidelines on their device. The input is the revised guidelines, and the output is the presentation of the guidelines to the user.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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."

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] The following is further disclosed regarding the embodiments described above.

[0806] (Claim 1)

[0807] A means for collecting behavioral and environmental data of aquatic organisms,

[0808] A means of preprocessing collected data to generate an integrated dataset,

[0809] A means for executing an AI algorithm that analyzes the behavioral patterns of aquatic organisms using an integrated dataset,

[0810] A means for generating environmentally friendly fishing guidelines based on analysis results,

[0811] A means of distributing the generated guidelines to the user's terminal,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, wherein the AI ​​algorithm identifies the migration routes of aquatic organisms using a clustering method.

[0815] (Claim 3)

[0816] The system according to claim 1, wherein the generated guidelines include proposals for catch limits and proposals for fishing ban periods.

[0817] "Example 1"

[0818] (Claim 1)

[0819] A means of collecting information on aquatic organisms and the surrounding environment,

[0820] A means for preprocessing collected information to generate an integrated information set,

[0821] A means for executing artificial intelligence that analyzes the behavioral patterns of aquatic organisms using an integrated set of information,

[0822] A means for generating environmentally friendly fishing guidelines based on analysis results,

[0823] A means of distributing the generated guidelines to the user's external device,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, wherein the artificial intelligence identifies the migration routes of aquatic organisms using a classification method.

[0827] (Claim 3)

[0828] The system according to claim 1, wherein the generated guidelines include suggestions for catch limits and suggestions for fishing ban periods.

[0829] "Application Example 1"

[0830] (Claim 1)

[0831] A means for collecting behavioral and environmental data of aquatic organisms,

[0832] A means of preprocessing collected data to generate an integrated dataset,

[0833] A means for executing an AI algorithm that analyzes the behavioral patterns of aquatic organisms using an integrated dataset,

[0834] A means for generating environmentally friendly fishing guidelines based on analysis results,

[0835] A means for distributing generated guidelines and store information based on sustainable fisheries to user terminals,

[0836] A means of identifying and notifying users of stores that provide sustainable products based on their location information,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, wherein the AI ​​algorithm identifies the migration routes of aquatic organisms using a clustering method.

[0840] (Claim 3)

[0841] The system according to claim 1, wherein the generated guidelines include proposed catch limits and proposed fishing ban periods, and provide commodity information based on sustainable fisheries.

[0842] "Example 2 of combining an emotion engine"

[0843] (Claim 1)

[0844] A means of using a device equipped with the ability to collect behavioral data and environmental data of aquatic organisms,

[0845] A means of preprocessing collected data to generate an integrated dataset,

[0846] A means for executing an artificial intelligence algorithm that analyzes the behavioral patterns of aquatic organisms using an integrated dataset,

[0847] A means for generating guidelines for environmentally conscious fisheries based on analysis results,

[0848] A means for distributing the generated guidelines to the user's device,

[0849] A means of analyzing user feedback and identifying the user's emotional state using an emotion analysis engine,

[0850] A means of adjusting the guidelines generated based on the results of emotion analysis to suit the user,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, wherein the artificial intelligence algorithm identifies the migration routes of aquatic organisms using a clustering method.

[0854] (Claim 3)

[0855] The system according to claim 1, wherein the generated guidelines include suggestions for yields and suggestions for periods of fishing bans.

[0856] "Application example 2 when combining with an emotional engine"

[0857] (Claim 1)

[0858] A means for collecting behavioral and environmental data of aquatic organisms,

[0859] A means of preprocessing collected data to generate an integrated dataset,

[0860] A means for executing an information processing device that analyzes the behavioral patterns of aquatic organisms using an integrated dataset,

[0861] A means of generating environmentally conscious usage guidelines based on the analysis results,

[0862] A means for transmitting the generated guidelines to the user's device,

[0863] A means for analyzing the user's emotions using an emotion recognition device and adjusting guidelines based on the analysis results,

[0864] A means of individually providing adjusted guidelines via a display device,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The information processing device identifies the migration routes of aquatic organisms using a classification method, according to claim 1.

[0868] (Claim 3)

[0869] The system according to claim 1, wherein the adjusted guidelines include proposals for production volume and proposals for activity ban periods. [Explanation of Symbols]

[0870] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting behavioral and environmental data of aquatic organisms, A means for preprocessing collected data to generate an integrated dataset, A means for executing an AI algorithm that analyzes the behavioral patterns of aquatic organisms using an integrated dataset, A means for generating environmentally friendly fishing guidelines based on analysis results, A means of distributing the generated guidelines to the user's terminal, A system that includes this.

2. The system according to claim 1, wherein the AI ​​algorithm identifies the migration routes of aquatic organisms using a clustering method.

3. The system according to claim 1, wherein the generated guidelines include suggestions for catch limits and suggestions for fishing ban periods.

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