system

Abstract

We provide the system. [Solution] Information acquisition means for collecting detection information on fish schools and traffic, An analytical means for identifying fishing grounds and optimal routes based on collected information, Route setting means for calculating a route to a specified fishing ground and the optimal route, A control system for automatically steering ships and means of transport according to a calculated route, A monitoring means that monitors fuel and energy levels and adjusts the return to port and charging routes, 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 performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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] In conventional fishing, there was a problem that it was difficult to efficiently discover a school of fish and identify its fishing ground. Also, due to insufficient optimization of weather changes and fuel consumption, it was difficult to achieve safe and economical fishing. In particular, there was no technology for beginners without a license to discover a target school of fish while safely operating a ship.

Means for Solving the Problems

[0005] This invention provides a system for efficient and safe fishing by including information acquisition means for collecting fish school detection information, analysis means for identifying fishing grounds based on the collected information, route setting means for calculating a route to the identified fishing grounds, steering means for automatically steering the vessel according to the calculated route, and monitoring means for monitoring fuel levels and adjusting the return route. This system is easy for even beginners to use and makes it possible to properly operate a vessel without a license.

[0006] "Information acquisition means" refers to components of a system that collects data on fish schools and the environment of a sea area using fish finders, sensors, etc.

[0007] "Analysis means" refers to a component of a system that analyzes data collected by information acquisition means to identify the location and activity patterns of fish schools.

[0008] The "route setting means" is a component of a system that calculates and sets the optimal navigation route to the fishing grounds identified by the analysis means.

[0009] "Controlling means" refers to a device or program that automatically controls a ship based on the route calculated by the route setting means.

[0010] "Monitoring means" refers to components of a system that constantly monitors the fuel level of a vessel and adjusts the return route or sailing plan as needed. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention relates to a system for efficiently capturing schools of fish and safely maneuvering a vessel. This system mainly consists of information acquisition means, analysis means, route setting means, maneuvering means, and monitoring means.

[0033] First, in terms of information acquisition methods, the terminal uses fish finders and sensors to collect information on fish schools in the sea area, weather information, and seabed topography data. This allows for the collection of detailed information such as the location and depth of fish schools.

[0034] Next, as an analysis method, the server analyzes the collected data to identify the activity patterns and locations of the fish schools. In this process, the data is evaluated according to the characteristics of the target fish species, and suitable fishing grounds are determined.

[0035] Subsequently, the route setting mechanism calculates the optimal route to the identified fishing grounds. This process takes into account weather conditions and currents to ensure a route that is both safe and efficient.

[0036] In terms of control, the terminal automatically steers the vessel according to a calculated route. The user requires no special operation; the system properly navigates the vessel to the fishing grounds.

[0037] In addition, as a monitoring measure, the server continuously monitors the fuel level and makes adjustments to maintain a safe return route to port. This helps avoid the risk of fuel shortage and allows the ship to return to port safely.

[0038] For example, if the terminal detects a large school of fish at a depth of 15 meters using the fish finder, the server will use this information to set an appropriate route, and the terminal will automatically steer the boat using that route. By minimizing the user's onboard operations and leaving the entire process to the system, efficient fishing becomes possible.

[0039] Thus, the present invention aims to provide a safe fishing experience that is easy for beginners to use and does not require a boat license.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The terminal activates a fish finder, seabed scanner, and visibility sensor to collect real-time information on fish schools, seabed topography, and visibility data for the area. This includes the location and depth of fish schools, seabed topography, and weather conditions.

[0043] Step 2:

[0044] The server receives the collected data and performs analysis based on factors such as the fish school's location, water temperature, and depth. Calculations are then performed to identify activity patterns based on the target fish species.

[0045] Step 3:

[0046] The server identifies the optimal fishing grounds based on the analysis results. Fishing ground selection takes into account the activity patterns of the fish species, safety, and optimal environmental conditions.

[0047] Step 4:

[0048] The server calculates the optimal route to the identified fishing grounds. This calculation includes wind direction, currents, distance, and fuel consumption. At this stage, the most efficient and safest route is determined.

[0049] Step 5:

[0050] The terminal initiates autopilot of the vessel based on the calculated route. The user can review the route and make minor adjustments as needed, but essentially the system fully supports the navigation.

[0051] Step 6:

[0052] The server monitors the fuel level during navigation and adjusts the navigation mode as needed. This ensures safe and efficient navigation according to the fuel situation.

[0053] Step 7:

[0054] The server plans a safe return route to the nearest port. This route plan takes into account fuel level, wind direction, currents, and port location. The system helps ensure a safe return journey.

[0055] (Example 1)

[0056] 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."

[0057] In modern fishing, accurately understanding ocean conditions and selecting the optimal route is essential for efficiently catching schools of fish. However, with current technology, accurately analyzing the location and activity patterns of fish schools, and setting safe and efficient routes that take weather and currents into consideration, has been difficult. Furthermore, there has been a lack of means to efficiently maneuver while considering fuel consumption and to return to port safely.

[0058] 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.

[0059] In this invention, the server includes information acquisition means for collecting fish school detection information, analysis means for analyzing the activity patterns of fish schools based on the collected information to identify fishing grounds, route setting means for calculating the optimal route to the identified fishing grounds considering weather conditions and currents, and monitoring means for continuously monitoring fuel levels to maintain a safe return route. This enables fishing activities to be carried out safely and efficiently, and makes it easy for even beginners to enjoy fishing.

[0060] "Fish school detection information" refers to data that shows the location, density, and movement of schools of fish in the water.

[0061] "Information acquisition means" refers to a device or method for collecting data related to schools of fish using a fish finder, sensor, etc.

[0062] "Analysis means" refers to a device or method for analyzing the behavior and patterns of fish schools using collected data and for identifying fishing grounds.

[0063] "Route setting means" refers to a device or method for calculating the optimal route to the fishing grounds identified through analysis, taking into account safety and efficiency.

[0064] "Maneuvering means" refers to a system or method for guiding a vessel along a set course using automatic steering.

[0065] "Monitoring means" refers to a device or method for continuously checking the remaining fuel level and adjusting the return route to port as necessary.

[0066] "Correction means" refers to a device or method for improving the accuracy of detection information using environmental data.

[0067] "Safety analysis means" refers to a device or method that analyzes meteorological data and tidal current data and incorporates the results to ensure the safety of shipping lanes.

[0068] This system is designed to efficiently capture schools of fish and safely maneuver vessels, and mainly consists of information acquisition means, analysis means, route setting means, steering means, and monitoring means.

[0069] First, the device collects data about the sea area using fish finders and sensors. Specifically, it obtains information such as the location, depth, and density of fish schools from the fish finder, and collects weather information and seabed topography data from sensors. The hardware used in this process includes common fish finders and weather sensors.

[0070] Next, the data collected by the server is analyzed. Using advanced analytical algorithms, the activity patterns and movements of fish schools are predicted, and the optimal fishing grounds are identified based on the target fish species. Dedicated analytical software is used for this process.

[0071] Based on the analysis results, the server calculates the optimal route to the identified fishing grounds. It evaluates the effects of weather conditions and currents to set a route that balances vessel safety and fuel efficiency. High-performance navigation software is used for this route setting.

[0072] Subsequently, the terminal controls the vessel via the autopilot system according to the route information received from the server. The autopilot system follows the set route, requiring no special operation from the user. This allows even those with little experience operating vessels to navigate with confidence.

[0073] The server also continuously monitors the fuel level. Fuel sensors measure the rate of fuel consumption and make necessary adjustments for a safe return to port. For example, if the fuel level falls below a certain amount, the server can suggest an alternative return route to the user.

[0074] As a concrete example, consider the operation when a terminal detects a large school of fish at a depth of 15 meters using a fish finder. The server then sets an appropriate route based on this, and the terminal automatically steers the boat using that route. By entrusting the steering to this system and minimizing onboard operations, the user can enjoy efficient and safe fishing.

[0075] Examples of input prompts for a generative AI model:

[0076] "Based on the data obtained from the fish finder and sensors, calculate the optimal route considering the currents and weather conditions, and set the autopilot system accordingly."

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The terminal uses a fish finder and sensors to collect data on the location of fish schools, weather information, and seabed topography. In this step, the location and depth of fish schools are determined based on position data (input) obtained from the fish finder, and weather data and seabed topography data (input) are collected from sensors. This information is integrated and output as environmental data to prepare for subsequent analysis. Specifically, the terminal periodically reads the sensors and updates the data in real time.

[0080] Step 2:

[0081] The server analyzes the information received from the terminal. Here, it uses the collected environmental data (input) to identify the activity patterns of fish schools and uses data analysis algorithms to predict the behavior of the target fish species. The result of this analysis is the optimal fishing ground and its location (output), which is used for setting the next fishing route. Specifically, the server runs the data analysis software, fits the input data into the model, and prepares to display the results to the user.

[0082] Step 3:

[0083] The server calculates the optimal route to the fishing grounds based on the analysis results. Inputs include location information of the fishing grounds, weather conditions, and tidal current data. From this data, navigation software is used to set a safe and efficient route (output). Specifically, the server activates the route calculation algorithm and sends the calculation results to the terminal.

[0084] Step 4:

[0085] The terminal controls the autopilot system based on the route information it receives. Following the calculated route (input), the terminal automatically steers the vessel and navigates to the set destination. The output is real-time location information of the vessel. Specifically, the terminal applies automatic steering instructions to steer and adjust speed.

[0086] Step 5:

[0087] The server monitors the fuel level and adjusts the return route as needed. In this step, it continuously analyzes data (input) from the fuel sensor, calculates fuel consumption, and proposes a safe return route. The output is a proposed route that allows for a safe return to port. Specifically, the server monitors the fuel status and notifies the user as appropriate.

[0088] (Application Example 1)

[0089] 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."

[0090] In addition to the challenges of efficiently catching schools of fish and safely navigating vessels, there are also challenges in efficiently managing urban traffic and enabling autonomous vehicles to reach their destinations safely while using energy efficiently. This invention aims to improve efficiency and safety in both fisheries and urban traffic.

[0091] 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.

[0092] In this invention, the server includes information acquisition means for collecting detection information of fish schools and traffic, analysis means for identifying fishing grounds and optimal routes based on the collected information, and route setting means for calculating routes to the identified fishing grounds and optimal routes. This enables efficient fish capture in fisheries, as well as congestion avoidance and improved energy efficiency in urban traffic.

[0093] "Information acquisition means" refers to devices and systems installed to collect detection information related to schools of fish and urban traffic.

[0094] "Analysis means" refers to the functions and processes used to analyze collected information and identify fishing grounds and optimal routes.

[0095] "Route setting means" refers to a system for planning and calculating sea routes to specific fishing grounds or optimal transportation routes.

[0096] "Operational means" refers to devices and technologies for automatically operating ships or means of transport based on a calculated route.

[0097] "Monitoring means" refers to functions that monitor fuel levels and energy consumption, and adjust the return route or the route to charging stations as needed.

[0098] "Correction measures" refer to technologies that adjust and modify environmental data and traffic data in order to improve the detection accuracy of collected information.

[0099] "Safety analysis methods" refer to systems and methods that perform analysis based on weather data and traffic data to provide safe shipping routes.

[0100] The server provides a system for collecting detection information on fish schools and urban traffic. This system utilizes drones and autonomous vehicles equipped with various sensors. The collected information is analyzed on the server and used to identify fishing grounds and plan optimal routes for urban traffic.

[0101] The analysis uses Python-based data analysis libraries (e.g., Pandas and NumPy) to calculate the optimal route based on the identified information. Existing map services such as Google® Maps API are utilized to provide route information for the calculation.

[0102] The terminal distributes calculated route information to the vehicle or vessel and performs autonomous driving. Small devices such as Raspberry Pi are expected to be used in the autonomous driving system. Monitoring measures will also monitor fuel and energy consumption and adjust charging stations and return routes as needed.

[0103] As a concrete example, for an event held in a city on a weekend, a route that avoids traffic congestion is proposed to allow participants to move smoothly, and autonomous vehicles can travel smoothly by following this route.

[0104] As an example of a prompt to the generated AI model, it provides explicit instructions such as, "Explain how to use real-time data and autonomous driving technology to calculate the optimal route to avoid congestion in smart city traffic management."

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server collects data on fish schools and traffic conditions from sensors mounted on drones and autonomous vehicles. The input is raw data detected by the sensors. Based on this, the server accumulates information in real time and can extract the necessary information. The output is a structured dataset.

[0108] Step 2:

[0109] The server analyzes the collected data to identify the location of fish schools and urban traffic congestion. The dataset obtained in Step 1 is used as input. The data is processed using a Python data analysis library, and the analysis results are output. The identified information enables future route planning.

[0110] Step 3:

[0111] The server calculates the optimal route to the fishing grounds and the optimal path within the city based on the analysis results. The input is the analysis results obtained in step 2. Using the Google Maps API, the calculated optimal route is output. This generates specific route information to achieve efficient travel.

[0112] Step 4:

[0113] The terminal transmits route information received from the server to the autonomous driving system, and the vehicle or vessel automatically begins to move. The input is the route information calculated in step 3. The terminal sends route instructions to the device's control system, and the actual operation begins as the output.

[0114] Step 5:

[0115] The server monitors the fuel and energy status of moving vehicles and vessels, and adjusts return-to-port or charging routes as needed. Input is fuel and battery data obtained from the vehicle. The server analyzes this information and issues instructions for returning to port or charging at the optimal time. This maximizes energy efficiency.

[0116] 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.

[0117] This invention provides a system for efficiently capturing schools of fish and for comfortably and safely operating a vessel, incorporating an emotion engine that recognizes user emotions and optimizes the experience. This system, along with information acquisition means, analysis means, route setting means, operation means, and monitoring means, provides an onboard experience tailored to the user's emotions and stress level by incorporating the emotion engine.

[0118] First, in terms of information acquisition, the terminal collects information on fish schools, seabed topography, and weather conditions in the sea area via fish finders and sensors. This information serves as basic data for the system to conduct efficient fishing.

[0119] Next, the analysis system operates on the server and analyzes the collected data. This allows for the identification of the fish school's location and the activity patterns of the target fish species, and analysis is then performed for selecting fishing grounds.

[0120] The route setting method involves a server calculating the optimal route based on the analysis results, and setting a safe and efficient route that takes into account weather conditions and tidal currents.

[0121] In terms of control, the terminal automatically steers the vessel according to a pre-set route. With the system's steering assistance, users can reach fishing grounds without requiring complex operations.

[0122] The monitoring system uses a server to monitor fuel levels and optimize the return route to port. This ensures a safe return to port.

[0123] Furthermore, the emotion engine recognizes the user's emotions in real time and provides feedback tailored to the user's state. For example, if the user is feeling stressed, the emotion engine detects this and activates a function to provide relaxing music or information.

[0124] For example, if a user experiences stress while sailing, the emotion engine detects this state, and the server provides appropriate music or guide information to reduce the user's stress level. This allows the user to enjoy fishing in a comfortable environment.

[0125] Thus, this invention not only improves the efficiency of fishing but also realizes a system that provides an experience that resonates with the user's emotions.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The terminal activates fish finders and sensors to collect real-time information on fish schools, seabed topography, and weather conditions in the area. This provides data that can be used to find the most promising fishing grounds from the ship.

[0129] Step 2:

[0130] The server receives the collected data and analyzes the location of the fish school, water depth, and environmental conditions. Based on the server's analysis, it identifies appropriate fishing grounds for the target fish species.

[0131] Step 3:

[0132] The server calculates the optimal route to the identified fishing grounds based on the analysis results. Here, weather data and ocean currents are taken into consideration to set a safe and efficient navigation route.

[0133] Step 4:

[0134] The terminal automatically steers the vessel according to the calculated route and begins sailing towards the designated fishing grounds. User intervention is essentially unnecessary, as the system automatically performs the optimal steering.

[0135] Step 5:

[0136] The server monitors fuel consumption during navigation and adjusts the navigation mode as needed. This ensures that a fuel plan is maintained that allows for a safe return to port.

[0137] Step 6:

[0138] The emotion engine recognizes the user's emotions from their facial expressions, tone of voice, and other factors. The emotion data is sent to a server, where the user's stress level is analyzed.

[0139] Step 7:

[0140] Based on the analysis results of the emotion engine, the server provides suggestions and feedback tailored to the user's emotional state. For example, if it determines that relaxation is needed, the device will take actions such as playing relaxing music.

[0141] Step 8:

[0142] Upon returning to port, the server recalculates the optimal return route considering the fuel situation and provides instructions or prompts for adjustments to the user as needed, thereby supporting a safe return to port. As a result, the user can complete their voyage with peace of mind.

[0143] (Example 2)

[0144] 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".

[0145] Modern fishing activities require technologies for accurate detection of fish schools and efficient harvesting. At the same time, it is crucial to support operators in safely and comfortably managing their vessels. However, conventional technologies have been insufficient in handling complex data analysis and emotions, making efficient and safe fishing operations difficult.

[0146] 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.

[0147] In this invention, the server includes information acquisition means for collecting detection information of biological populations, analysis means for identifying fishing grounds based on the collected information, and route setting means for calculating a route to the identified fishing grounds. This makes it possible to efficiently capture biological populations while analyzing the user's emotions.

[0148] A "group of organisms" is a collection of marine organisms that exist in a specific body of water and move as a group.

[0149] "Information acquisition means" refers to devices or mechanisms that use fish finders or sensors to acquire information on the populations of organisms and environmental conditions from a body of water.

[0150] "Analysis means" refers to methods and devices for analyzing the location and patterns of biological populations based on collected information, and for identifying optimal fishing grounds.

[0151] "Route setting means" refers to methods and devices for calculating a safe and efficient route based on analysis results and applying it to a moving object.

[0152] "Maneuvering means" refers to a mechanism or technology for automatically steering a ship or moving object according to a calculated route.

[0153] "Monitoring means" refers to methods or devices for continuously monitoring the remaining fuel level and adjusting the return route to port as needed.

[0154] "Emotional analysis tools" are technologies and systems that analyze the user's emotional state and provide adaptive experiences tailored to that state.

[0155] This invention is a system for efficiently detecting populations of organisms in aquatic environments and enabling appropriate navigation, and it is primarily composed of a server and terminals. The server and terminals work together, coordinating multiple functions to allow users to operate the system comfortably and safely.

[0156] The server receives data from terminals that use fish finders and environmental sensors as means of acquiring information. This data includes the location of organism populations and environmental conditions. Next, the server uses analysis tools to analyze the collected data and identify the optimal fishing grounds. This analysis uses a generative AI model that learns data patterns and predicts the behavior of organisms.

[0157] Once the analysis is complete, the server uses a routing mechanism to calculate the optimal route to the identified fishing grounds. During this process, it utilizes a generative AI model and uses prompts to suggest routes that take into account conditions such as weather and currents. An example of a prompt is, "Please suggest the optimal sailing route considering wind strength."

[0158] The terminal acts as a control device, automatically steering the moving object according to a calculated route. This reduces the complexity of control for the user, enabling efficient arrival at the destination.

[0159] Furthermore, the server constantly monitors the remaining fuel level through monitoring devices and optimizes the return route as needed. This ensures the safe and reliable return of the mobile vehicle.

[0160] Furthermore, the emotion analysis system monitors the user's emotional state in real time and provides appropriate feedback. This feature can be used, for example, to suggest relaxing music if the user feels fatigued.

[0161] As described above, this invention is a system that not only efficiently captures groups of organisms but also provides an experience that takes into consideration the user's emotions.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The terminal functions as a means of acquiring information, collecting information on biological populations, seabed topography, and weather conditions from fish finders and sensors. The input consists of biosignals and environmental data obtained from the finders and sensors, which are transmitted to the server. The output is raw data provided to the server. Specifically, the terminal periodically activates sensors and samples data.

[0165] Step 2:

[0166] The server uses analytical tools to analyze data received from the terminal. The input data includes location information, movement patterns, and environmental conditions of biological populations. A generative AI model is used to learn data patterns and identify optimal fishing grounds. As output, recommended fishing ground locations based on the analysis results are generated. During processing, the server uses numerous datasets and implements a feedback loop to improve model accuracy.

[0167] Step 3:

[0168] The server uses a route setting mechanism to calculate the optimal route to the recommended fishing grounds. Input includes the fishing ground locations identified by the analysis mechanism and the latest weather and tidal current data. The server uses a generating AI model and simulates the optimal route based on prompt messages. The optimal route data is sent to the terminal as output. Specific operations include updating the simulation as needed in response to weather changes.

[0169] Step 4:

[0170] The terminal acts as the control system, automatically steering the vehicle according to route data provided by the server. The input is the optimal route information received from the server. The terminal integrates with GPS to adjust the direction of travel along the route in real time. The output is the vehicle moving along the correct navigation route. Specifically, the terminal autonomously performs position confirmation and obstacle avoidance maneuvers during navigation.

[0171] Step 5:

[0172] The server receives fuel level and location information transmitted from the terminal via monitoring devices to ensure safe navigation. Inputs are fuel level data and current location data. If a return to port is necessary, the server provides the terminal with a recalculated return route. Output is optimized return route information. Specifically, a fuel consumption prediction model is used to propose an optimized route early on.

[0173] Step 6:

[0174] The server uses emotion analysis tools to acquire user emotion data in real time. Input is emotion data from the user obtained through biosensors and voice analysis. The server inputs prompt sentences into a generation AI model, which then generates feedback appropriate to the user's state. The output includes music and information designed to reduce user stress. Specifically, the server analyzes the user's heart rate and facial expressions to select recommended content for relaxation.

[0175] (Application Example 2)

[0176] 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".

[0177] In fishing activities using autonomous vessels, it is necessary to ensure efficient detection of fish schools and safe autonomous navigation while also understanding the operator's emotions and stress levels to provide a comfortable working environment. However, conventional systems have not optimized the onboard environment, resulting in increased mental burden on operators and decreased work efficiency. Solving this problem is an urgent priority.

[0178] 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.

[0179] In this invention, the server includes information acquisition means for collecting fish school detection information, analysis means for identifying fishing grounds based on the collected information, and feedback generation means for analyzing emotional states and controlling the environment. This enables efficient capture of fish schools as well as the provision of a comfortable environment tailored to the operator's emotional state.

[0180] "Information acquisition means" refers to a device or system used to collect data about a school of fish.

[0181] "Analysis means" refers to a function that performs data processing to identify fishing grounds based on the collected information.

[0182] "Route setting means" refers to means for calculating the optimal sea route to a specified fishing ground.

[0183] "Maneuvering means" refers to the technology for automatically steering a ship based on a calculated route.

[0184] "Monitoring means" refers to a system that provides functions for monitoring fuel levels and adjusting the return route to port.

[0185] A "feedback generation method" is a means of analyzing emotional states and controlling the environment accordingly.

[0186] The system implementing this invention consists of multiple terminals located on board a vessel and a server located in the cloud. The terminals use fish finders and various sensors to collect fish school information and environmental data in the sea area in real time. This includes sensors and devices for acquiring underwater topographic information.

[0187] The server receives data obtained through information acquisition means and analyzes the activity patterns of fish using analysis means. Based on this information, the route setting means calculates the most efficient and safest route. This process involves many data calculations and can be done using Python or data analysis libraries (e.g., NumPy or Pandas).

[0188] The steering system uses an automated control system to steer the vessel along a set route, reducing the need for complex manual operation. Furthermore, the monitoring system constantly checks the fuel level and optimizes the return route if necessary.

[0189] The emotion engine, as a means of generating feedback, includes software for analyzing the emotional state of passengers. This includes facial expression analysis tools (e.g., OpenCV) and speech analysis platforms for processing audio data (e.g., Google Speech-to-Text API). Using this data, the server provides immediate feedback tailored to the emotional state of the passengers.

[0190] For example, if a situation is detected where a passenger is experiencing stress, the server can quickly improve the environment by selecting and playing soothing music and adjusting the ship's lighting.

[0191] As an example of a prompt, you can enter "Emotion Engine, the crew is experiencing stress while working. Please suggest what music to play and how to adjust the environment on board" to prepare the system to generate optimal feedback.

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The terminal uses a fish finder to collect fish school information and environmental data. The input is real-time data of the sea area, and the output is digital fish school information and environmental data. This data includes the location of fish schools, seabed topography, and weather conditions.

[0195] Step 2:

[0196] The server receives information transmitted from the terminal and processes the data using analytical tools. The inputs are digital fish school information and environmental data, and the outputs are fish school activity patterns and information identifying potential fishing grounds. Statistical analysis is then performed using data analysis libraries (e.g., NumPy, Pandas).

[0197] Step 3:

[0198] The server activates the route setting mechanism based on the analysis results. The input is information identifying fishing grounds, and the output is optimized route information. Here, the optimal route is calculated considering weather data and ocean currents.

[0199] Step 4:

[0200] As a means of control, the terminal initiates autopilot according to the calculated route. The input is route information, and the output is the physical motion of the vessel. The automatic control system maintains the predetermined route by adjusting the engine and rudder.

[0201] Step 5:

[0202] As a monitoring tool, the server constantly monitors the fuel level. The input is data from the fuel sensor, and the output is real-time fuel level data. Based on this information, the return route is recalculated as needed.

[0203] Step 6:

[0204] As a means of generating feedback, the server uses an emotion engine to analyze the emotional state of the passengers. The input is audio and video data from the terminal, and the output is the analysis results regarding the passengers' emotional state. Here, a facial expression analysis tool (e.g., OpenCV) and an audio analysis platform (e.g., Google Speech-to-Text API) are used.

[0205] Step 7:

[0206] Based on their emotional state, the server provides appropriate feedback to the passengers. The input is the result of the emotional state analysis, and the output is music selection and environmental control commands. Specifically, the server plays calming music and adjusts the lighting inside the ship.

[0207] Step 8:

[0208] The user uses prompts to input additional commands into the system. The input is a prompt, and the output is a change in feedback from the system. For example, by giving the instruction, "Emotional Engine, the crew is experiencing stress during work. Suggest what music to play and how to adjust the environment," the system will take appropriate action.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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".

[0225] This invention relates to a system for efficiently capturing schools of fish and safely maneuvering a vessel. This system mainly consists of information acquisition means, analysis means, route setting means, maneuvering means, and monitoring means.

[0226] First, in terms of information acquisition methods, the terminal uses fish finders and sensors to collect information on fish schools in the sea area, weather information, and seabed topography data. This allows for the collection of detailed information such as the location and depth of fish schools.

[0227] Next, as an analysis method, the server analyzes the collected data to identify the activity patterns and locations of the fish schools. In this process, the data is evaluated according to the characteristics of the target fish species, and suitable fishing grounds are determined.

[0228] Subsequently, the route setting mechanism calculates the optimal route to the identified fishing grounds. This process takes into account weather conditions and currents to ensure a route that is both safe and efficient.

[0229] In terms of control, the terminal automatically steers the vessel according to a calculated route. The user requires no special operation; the system properly navigates the vessel to the fishing grounds.

[0230] In addition, as a monitoring measure, the server continuously monitors the fuel level and makes adjustments to maintain a safe return route to port. This helps avoid the risk of fuel shortage and allows the ship to return to port safely.

[0231] For example, if the terminal detects a large school of fish at a depth of 15 meters using the fish finder, the server will use this information to set an appropriate route, and the terminal will automatically steer the boat using that route. By minimizing the user's onboard operations and leaving the entire process to the system, efficient fishing becomes possible.

[0232] Thus, the present invention aims to provide a safe fishing experience that is easy for beginners to use and does not require a boat license.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The terminal activates a fish finder, seabed scanner, and visibility sensor to collect real-time information on fish schools, seabed topography, and visibility data for the area. This includes the location and depth of fish schools, seabed topography, and weather conditions.

[0236] Step 2:

[0237] The server receives the collected data and performs analysis based on factors such as the fish school's location, water temperature, and depth. Calculations are then performed to identify activity patterns based on the target fish species.

[0238] Step 3:

[0239] The server identifies the optimal fishing grounds based on the analysis results. Fishing ground selection takes into account the activity patterns of the fish species, safety, and optimal environmental conditions.

[0240] Step 4:

[0241] The server calculates the optimal route to the identified fishing grounds. This calculation includes wind direction, currents, distance, and fuel consumption. At this stage, the most efficient and safest route is determined.

[0242] Step 5:

[0243] The terminal initiates autopilot of the vessel based on the calculated route. The user can review the route and make minor adjustments as needed, but essentially the system fully supports the navigation.

[0244] Step 6:

[0245] The server monitors the fuel level during navigation and adjusts the navigation mode as needed. This ensures safe and efficient navigation according to the fuel situation.

[0246] Step 7:

[0247] The server plans a safe return route to the nearest port. This route plan takes into account fuel level, wind direction, currents, and port location. The system helps ensure a safe return journey.

[0248] (Example 1)

[0249] 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."

[0250] In modern fishing, accurately understanding ocean conditions and selecting the optimal route is essential for efficiently catching schools of fish. However, with current technology, accurately analyzing the location and activity patterns of fish schools, and setting safe and efficient routes that take weather and currents into consideration, has been difficult. Furthermore, there has been a lack of means to efficiently maneuver while considering fuel consumption and to return to port safely.

[0251] 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.

[0252] In this invention, the server includes information acquisition means for collecting fish school detection information, analysis means for analyzing the activity patterns of fish schools based on the collected information to identify fishing grounds, route setting means for calculating the optimal route to the identified fishing grounds considering weather conditions and currents, and monitoring means for continuously monitoring fuel levels to maintain a safe return route. This enables fishing activities to be carried out safely and efficiently, and makes it easy for even beginners to enjoy fishing.

[0253] "Fish school detection information" refers to data that shows the location, density, and movement of schools of fish in the water.

[0254] "Information acquisition means" refers to a device or method for collecting data related to schools of fish using a fish finder, sensor, etc.

[0255] "Analysis means" refers to a device or method for analyzing the behavior and patterns of fish schools using collected data and for identifying fishing grounds.

[0256] "Route setting means" refers to a device or method for calculating the optimal route to the fishing grounds identified through analysis, taking into account safety and efficiency.

[0257] "Maneuvering means" refers to a system or method for guiding a vessel along a set course using automatic steering.

[0258] "Monitoring means" refers to a device or method for continuously checking the remaining fuel level and adjusting the return route to port as necessary.

[0259] "Correction means" refers to a device or method for improving the accuracy of detection information using environmental data.

[0260] "Safety analysis means" refers to a device or method that analyzes meteorological data and tidal current data and incorporates the results to ensure the safety of shipping lanes.

[0261] This system is designed to efficiently capture schools of fish and safely maneuver vessels, and mainly consists of information acquisition means, analysis means, route setting means, steering means, and monitoring means.

[0262] First, the device collects data about the sea area using fish finders and sensors. Specifically, it obtains information such as the location, depth, and density of fish schools from the fish finder, and collects weather information and seabed topography data from sensors. The hardware used in this process includes common fish finders and weather sensors.

[0263] Next, the data collected by the server is analyzed. Using advanced analytical algorithms, the activity patterns and movements of fish schools are predicted, and the optimal fishing grounds are identified based on the target fish species. Dedicated analytical software is used for this process.

[0264] Based on the analysis results, the server calculates the optimal route to the identified fishing grounds. It evaluates the effects of weather conditions and currents to set a route that balances vessel safety and fuel efficiency. High-performance navigation software is used for this route setting.

[0265] Subsequently, the terminal controls the vessel via the autopilot system according to the route information received from the server. The autopilot system follows the set route, requiring no special operation from the user. This allows even those with little experience operating vessels to navigate with confidence.

[0266] The server also continuously monitors the fuel level. Fuel sensors measure the rate of fuel consumption and make necessary adjustments for a safe return to port. For example, if the fuel level falls below a certain amount, the server can suggest an alternative return route to the user.

[0267] As a concrete example, consider the operation when a terminal detects a large school of fish at a depth of 15 meters using a fish finder. The server then sets an appropriate route based on this, and the terminal automatically steers the boat using that route. By entrusting the steering to this system and minimizing onboard operations, the user can enjoy efficient and safe fishing.

[0268] Examples of input prompts for a generative AI model:

[0269] "Based on the data obtained from the fish finder and sensors, calculate the optimal route considering the currents and weather conditions, and set the autopilot system accordingly."

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] The terminal uses a fish finder and sensors to collect data on the location of fish schools, weather information, and seabed topography. In this step, the location and depth of fish schools are determined based on position data (input) obtained from the fish finder, and weather data and seabed topography data (input) are collected from sensors. This information is integrated and output as environmental data to prepare for subsequent analysis. Specifically, the terminal periodically reads the sensors and updates the data in real time.

[0273] Step 2:

[0274] The server analyzes the information received from the terminal. Here, it uses the collected environmental data (input) to identify the activity patterns of fish schools and uses data analysis algorithms to predict the behavior of the target fish species. The result of this analysis is the optimal fishing ground and its location (output), which is used for setting the next fishing route. Specifically, the server runs the data analysis software, fits the input data into the model, and prepares to display the results to the user.

[0275] Step 3:

[0276] The server calculates the optimal route to the fishing grounds based on the analysis results. Inputs include location information of the fishing grounds, weather conditions, and tidal current data. From this data, navigation software is used to set a safe and efficient route (output). Specifically, the server activates the route calculation algorithm and sends the calculation results to the terminal.

[0277] Step 4:

[0278] The terminal controls the autopilot system based on the route information it receives. Following the calculated route (input), the terminal automatically steers the vessel and navigates to the set destination. The output is real-time location information of the vessel. Specifically, the terminal applies automatic steering instructions to steer and adjust speed.

[0279] Step 5:

[0280] The server monitors the fuel level and adjusts the return route as needed. In this step, it continuously analyzes data (input) from the fuel sensor, calculates fuel consumption, and proposes a safe return route. The output is a proposed route that allows for a safe return to port. Specifically, the server monitors the fuel status and notifies the user as appropriate.

[0281] (Application Example 1)

[0282] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0283] In addition to the issues of efficiently capturing fish schools and safely operating ships, there are issues of efficiently managing urban traffic and enabling autonomous vehicles to safely reach their destinations while efficiently using energy. The object of this invention is to improve efficiency and safety in both fishing and urban traffic.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0285] In this invention, the server includes an information acquisition means for collecting detection information on fish schools and traffic, an analysis means for identifying fishing grounds and optimal routes based on the collected information, and a route setting means for calculating routes to the identified fishing grounds and optimal routes. As a result, efficient capture of fish schools in fishing becomes possible, and congestion avoidance and energy efficiency improvement in urban traffic become possible.

[0286] The "information acquisition means" refers to devices and systems installed for collecting detection information on fish schools and urban traffic.

[0287] The "analysis means" refers to functions and processes for analyzing the collected information to identify fishing grounds and optimal routes.

[0288] The "route setting means" refers to a system for planning and calculating routes to the identified fishing grounds and optimal traffic routes.

[0289] The "control means" refers to devices and technologies for automatically operating ships and transportation means based on the calculated routes.

[0290] The "monitoring means" refers to a function for monitoring the remaining fuel amount and energy consumption status and adjusting the return route or route to a charging station as necessary.

[0291] "Correction measures" refer to technologies that adjust and modify environmental data and traffic data in order to improve the detection accuracy of collected information.

[0292] "Safety analysis methods" refer to systems and methods that perform analysis based on weather data and traffic data to provide safe shipping routes.

[0293] The server provides a system for collecting detection information on fish schools and urban traffic. This system utilizes drones and autonomous vehicles equipped with various sensors. The collected information is analyzed on the server and used to identify fishing grounds and plan optimal routes for urban traffic.

[0294] The analysis uses Python-based data analysis libraries (e.g., Pandas and NumPy) to calculate the optimal route based on the identified information. Existing map services such as the Google Maps API are utilized to provide route information for the calculation.

[0295] The terminal distributes calculated route information to the vehicle or vessel and performs autonomous driving. Small devices such as Raspberry Pi are expected to be used in the autonomous driving system. Monitoring measures will also monitor fuel and energy consumption and adjust charging stations and return routes as needed.

[0296] As a concrete example, for an event held in a city on a weekend, a route that avoids traffic congestion is proposed to allow participants to move smoothly, and autonomous vehicles can travel smoothly by following this route.

[0297] As an example of a prompt to the generated AI model, it provides explicit instructions such as, "Explain how to use real-time data and autonomous driving technology to calculate the optimal route to avoid congestion in smart city traffic management."

[0298] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0299] Step 1:

[0300] The server collects data on fish schools and traffic conditions from sensors mounted on drones and autonomous vehicles. As input, there is raw data detected by the sensors. Based on this, the server accumulates information in real time and enables extraction of necessary information. As output, a structured dataset is obtained.

[0301] Step 2:

[0302] The server analyzes the collected data to identify the positions of fish schools and the congestion status of urban traffic. The dataset obtained in Step 1 is used as input. The data is processed using a data analysis library in Python, and the analysis results are output. Based on the identified information, future route planning becomes possible.

[0303] Step 3:

[0304] The server calculates the route to the fishing ground and the optimal route within the city based on the analysis results. The input is the analysis results obtained in Step 2. Using the Google Maps API, the calculated optimal route is output. This generates specific route information for realizing efficient movement.

[0305] Step 4:

[0306] The terminal transmits the route information received from the server to the autonomous driving system, and the vehicle or ship automatically starts moving. The input is the route information calculated in Step 3. The terminal sends a route instruction to the control system of the device, and as a result, the actual operation is started.

[0307] Step 5:

[0308] The server monitors the fuel and energy status of moving vehicles and vessels, and adjusts return-to-port or charging routes as needed. Input is fuel and battery data obtained from the vehicle. The server analyzes this information and issues instructions for returning to port or charging at the optimal time. This maximizes energy efficiency.

[0309] 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.

[0310] This invention provides a system for efficiently capturing schools of fish and for comfortably and safely operating a vessel, incorporating an emotion engine that recognizes user emotions and optimizes the experience. This system, along with information acquisition means, analysis means, route setting means, operation means, and monitoring means, provides an onboard experience tailored to the user's emotions and stress level by incorporating the emotion engine.

[0311] First, in terms of information acquisition, the terminal collects information on fish schools, seabed topography, and weather conditions in the sea area via fish finders and sensors. This information serves as basic data for the system to conduct efficient fishing.

[0312] Next, the analysis system operates on the server and analyzes the collected data. This allows for the identification of the fish school's location and the activity patterns of the target fish species, and analysis is then performed for selecting fishing grounds.

[0313] The route setting method involves a server calculating the optimal route based on the analysis results, and setting a safe and efficient route that takes into account weather conditions and tidal currents.

[0314] In terms of control, the terminal automatically steers the vessel according to a pre-set route. With the system's steering assistance, users can reach fishing grounds without requiring complex operations.

[0315] The monitoring system uses a server to monitor fuel levels and optimize the return route to port. This ensures a safe return to port.

[0316] Furthermore, the emotion engine recognizes the user's emotions in real time and provides feedback tailored to the user's state. For example, if the user is feeling stressed, the emotion engine detects this and activates a function to provide relaxing music or information.

[0317] For example, if a user experiences stress while sailing, the emotion engine detects this state, and the server provides appropriate music or guide information to reduce the user's stress level. This allows the user to enjoy fishing in a comfortable environment.

[0318] Thus, this invention not only improves the efficiency of fishing but also realizes a system that provides an experience that resonates with the user's emotions.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The terminal activates fish finders and sensors to collect real-time information on fish schools, seabed topography, and weather conditions in the area. This provides data that can be used to find the most promising fishing grounds from the ship.

[0322] Step 2:

[0323] The server receives the collected data and analyzes the location of the fish school, water depth, and environmental conditions. Based on the server's analysis, it identifies appropriate fishing grounds for the target fish species.

[0324] Step 3:

[0325] The server calculates the optimal route to the identified fishing grounds based on the analysis results. Here, weather data and ocean currents are taken into consideration to set a safe and efficient navigation route.

[0326] Step 4:

[0327] The terminal automatically steers the vessel according to the calculated route and begins sailing towards the designated fishing grounds. User intervention is essentially unnecessary, as the system automatically performs the optimal steering.

[0328] Step 5:

[0329] The server monitors fuel consumption during navigation and adjusts the navigation mode as needed. This ensures that a fuel plan is maintained that allows for a safe return to port.

[0330] Step 6:

[0331] The emotion engine recognizes the user's emotions from their facial expressions, tone of voice, and other factors. The emotion data is sent to a server, where the user's stress level is analyzed.

[0332] Step 7:

[0333] Based on the analysis results of the emotion engine, the server provides suggestions and feedback tailored to the user's emotional state. For example, if it determines that relaxation is needed, the device will take actions such as playing relaxing music.

[0334] Step 8:

[0335] Upon returning to port, the server recalculates the optimal return route considering the fuel situation and provides instructions or prompts for adjustments to the user as needed, thereby supporting a safe return to port. As a result, the user can complete their voyage with peace of mind.

[0336] (Example 2)

[0337] 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".

[0338] Modern fishing activities require technologies for accurate detection of fish schools and efficient harvesting. At the same time, it is crucial to support operators in safely and comfortably managing their vessels. However, conventional technologies have been insufficient in handling complex data analysis and emotions, making efficient and safe fishing operations difficult.

[0339] 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.

[0340] In this invention, the server includes information acquisition means for collecting detection information of biological populations, analysis means for identifying fishing grounds based on the collected information, and route setting means for calculating a route to the identified fishing grounds. This makes it possible to efficiently capture biological populations while analyzing the user's emotions.

[0341] A "group of organisms" is a collection of marine organisms that exist in a specific body of water and move as a group.

[0342] "Information acquisition means" refers to devices or mechanisms that use fish finders or sensors to acquire information on the populations of organisms and environmental conditions from a body of water.

[0343] "Analysis means" refers to methods and devices for analyzing the location and patterns of biological populations based on collected information, and for identifying optimal fishing grounds.

[0344] "Route setting means" refers to methods and devices for calculating a safe and efficient route based on analysis results and applying it to a moving object.

[0345] "Maneuvering means" refers to a mechanism or technology for automatically steering a ship or moving object according to a calculated route.

[0346] "Monitoring means" refers to methods or devices for continuously monitoring the remaining fuel level and adjusting the return route to port as needed.

[0347] "Emotional analysis tools" are technologies and systems that analyze the user's emotional state and provide adaptive experiences tailored to that state.

[0348] This invention is a system for efficiently detecting populations of organisms in aquatic environments and enabling appropriate navigation, and it is primarily composed of a server and terminals. The server and terminals work together, coordinating multiple functions to allow users to operate the system comfortably and safely.

[0349] The server receives data from terminals that use fish finders and environmental sensors as means of acquiring information. This data includes the location of organism populations and environmental conditions. Next, the server uses analysis tools to analyze the collected data and identify the optimal fishing grounds. This analysis uses a generative AI model that learns data patterns and predicts the behavior of organisms.

[0350] Once the analysis is complete, the server uses a routing mechanism to calculate the optimal route to the identified fishing grounds. During this process, it utilizes a generative AI model and uses prompts to suggest routes that take into account conditions such as weather and currents. An example of a prompt is, "Please suggest the optimal sailing route considering wind strength."

[0351] The terminal acts as a control device, automatically steering the moving object according to a calculated route. This reduces the complexity of control for the user, enabling efficient arrival at the destination.

[0352] Furthermore, the server constantly monitors the remaining fuel level through monitoring devices and optimizes the return route as needed. This ensures the safe and reliable return of the mobile vehicle.

[0353] Furthermore, the emotion analysis system monitors the user's emotional state in real time and provides appropriate feedback. This feature can be used, for example, to suggest relaxing music if the user feels fatigued.

[0354] As described above, this invention is a system that not only efficiently captures groups of organisms but also provides an experience that takes into consideration the user's emotions.

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] The terminal functions as a means of acquiring information, collecting information on biological populations, seabed topography, and weather conditions from fish finders and sensors. The input consists of biosignals and environmental data obtained from the finders and sensors, which are transmitted to the server. The output is raw data provided to the server. Specifically, the terminal periodically activates sensors and samples data.

[0358] Step 2:

[0359] The server uses analytical tools to analyze data received from the terminal. The input data includes location information, movement patterns, and environmental conditions of biological populations. A generative AI model is used to learn data patterns and identify optimal fishing grounds. As output, recommended fishing ground locations based on the analysis results are generated. During processing, the server uses numerous datasets and implements a feedback loop to improve model accuracy.

[0360] Step 3:

[0361] The server uses a route setting mechanism to calculate the optimal route to the recommended fishing grounds. Input includes the fishing ground locations identified by the analysis mechanism and the latest weather and tidal current data. The server uses a generating AI model and simulates the optimal route based on prompt messages. The optimal route data is sent to the terminal as output. Specific operations include updating the simulation as needed in response to weather changes.

[0362] Step 4:

[0363] The terminal acts as the control system, automatically steering the vehicle according to route data provided by the server. The input is the optimal route information received from the server. The terminal integrates with GPS to adjust the direction of travel along the route in real time. The output is the vehicle moving along the correct navigation route. Specifically, the terminal autonomously performs position confirmation and obstacle avoidance maneuvers during navigation.

[0364] Step 5:

[0365] The server receives fuel level and location information transmitted from the terminal via monitoring devices to ensure safe navigation. Inputs are fuel level data and current location data. If a return to port is necessary, the server provides the terminal with a recalculated return route. Output is optimized return route information. Specifically, a fuel consumption prediction model is used to propose an optimized route early on.

[0366] Step 6:

[0367] The server uses emotion analysis tools to acquire user emotion data in real time. Input is emotion data from the user obtained through biosensors and voice analysis. The server inputs prompt sentences into a generation AI model, which then generates feedback appropriate to the user's state. The output includes music and information designed to reduce user stress. Specifically, the server analyzes the user's heart rate and facial expressions to select recommended content for relaxation.

[0368] (Application Example 2)

[0369] 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".

[0370] In fishing activities using autonomous vessels, it is necessary to ensure efficient detection of fish schools and safe autonomous navigation while also understanding the operator's emotions and stress levels to provide a comfortable working environment. However, conventional systems have not optimized the onboard environment, resulting in increased mental burden on operators and decreased work efficiency. Solving this problem is an urgent priority.

[0371] 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.

[0372] In this invention, the server includes information acquisition means for collecting fish school detection information, analysis means for identifying fishing grounds based on the collected information, and feedback generation means for analyzing emotional states and controlling the environment. This enables efficient capture of fish schools as well as the provision of a comfortable environment tailored to the operator's emotional state.

[0373] "Information acquisition means" refers to a device or system used to collect data about a school of fish.

[0374] "Analysis means" refers to a function that performs data processing to identify fishing grounds based on the collected information.

[0375] "Route setting means" refers to means for calculating the optimal sea route to a specified fishing ground.

[0376] "Maneuvering means" refers to the technology for automatically steering a ship based on a calculated route.

[0377] "Monitoring means" refers to a system that provides functions for monitoring fuel levels and adjusting the return route to port.

[0378] A "feedback generation method" is a means of analyzing emotional states and controlling the environment accordingly.

[0379] The system implementing this invention consists of multiple terminals located on board a vessel and a server located in the cloud. The terminals use fish finders and various sensors to collect fish school information and environmental data in the sea area in real time. This includes sensors and devices for acquiring underwater topographic information.

[0380] The server receives data obtained through information acquisition means and analyzes the activity patterns of fish using analysis means. Based on this information, the route setting means calculates the most efficient and safest route. This process involves many data calculations and can be done using Python or data analysis libraries (e.g., NumPy or Pandas).

[0381] The steering system uses an automated control system to steer the vessel along a set route, reducing the need for complex manual operation. Furthermore, the monitoring system constantly checks the fuel level and optimizes the return route if necessary.

[0382] The emotion engine, as a means of generating feedback, includes software for analyzing the emotional state of passengers. This includes facial expression analysis tools (e.g., OpenCV) and speech analysis platforms for processing audio data (e.g., Google Speech-to-Text API). Using this data, the server provides immediate feedback tailored to the emotional state of the passengers.

[0383] For example, if a situation is detected where a passenger is experiencing stress, the server can quickly improve the environment by selecting and playing soothing music and adjusting the ship's lighting.

[0384] As an example of a prompt, you can enter "Emotion Engine, the crew is experiencing stress while working. Please suggest what music to play and how to adjust the environment on board" to prepare the system to generate optimal feedback.

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] The terminal uses a fish finder to collect fish school information and environmental data. The input is real-time data of the sea area, and the output is digital fish school information and environmental data. This data includes the location of fish schools, seabed topography, and weather conditions.

[0388] Step 2:

[0389] The server receives information transmitted from the terminal and processes the data using analytical tools. The inputs are digital fish school information and environmental data, and the outputs are fish school activity patterns and information identifying potential fishing grounds. Statistical analysis is then performed using data analysis libraries (e.g., NumPy, Pandas).

[0390] Step 3:

[0391] The server activates the route setting mechanism based on the analysis results. The input is information identifying fishing grounds, and the output is optimized route information. Here, the optimal route is calculated considering weather data and ocean currents.

[0392] Step 4:

[0393] As a means of control, the terminal initiates autopilot according to the calculated route. The input is route information, and the output is the physical motion of the vessel. The automatic control system maintains the predetermined route by adjusting the engine and rudder.

[0394] Step 5:

[0395] As a monitoring tool, the server constantly monitors the fuel level. The input is data from the fuel sensor, and the output is real-time fuel level data. Based on this information, the return route is recalculated as needed.

[0396] Step 6:

[0397] As a means of generating feedback, the server uses an emotion engine to analyze the emotional state of the passengers. The input is audio and video data from the terminal, and the output is the analysis results regarding the passengers' emotional state. Here, a facial expression analysis tool (e.g., OpenCV) and an audio analysis platform (e.g., Google Speech-to-Text API) are used.

[0398] Step 7:

[0399] Based on their emotional state, the server provides appropriate feedback to the passengers. The input is the result of the emotional state analysis, and the output is music selection and environmental control commands. Specifically, the server plays calming music and adjusts the lighting inside the ship.

[0400] Step 8:

[0401] The user uses prompts to input additional commands into the system. The input is a prompt, and the output is a change in feedback from the system. For example, by giving the instruction, "Emotional Engine, the crew is experiencing stress during work. Suggest what music to play and how to adjust the environment," the system will take appropriate action.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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".

[0418] This invention relates to a system for efficiently capturing schools of fish and safely maneuvering a vessel. This system mainly consists of information acquisition means, analysis means, route setting means, maneuvering means, and monitoring means.

[0419] First, in terms of information acquisition methods, the terminal uses fish finders and sensors to collect information on fish schools in the sea area, weather information, and seabed topography data. This allows for the collection of detailed information such as the location and depth of fish schools.

[0420] Next, as an analysis method, the server analyzes the collected data to identify the activity patterns and locations of the fish schools. In this process, the data is evaluated according to the characteristics of the target fish species, and suitable fishing grounds are determined.

[0421] Subsequently, the route setting mechanism calculates the optimal route to the identified fishing grounds. This process takes into account weather conditions and currents to ensure a route that is both safe and efficient.

[0422] In terms of control, the terminal automatically steers the vessel according to a calculated route. The user requires no special operation; the system properly navigates the vessel to the fishing grounds.

[0423] In addition, as a monitoring measure, the server continuously monitors the fuel level and makes adjustments to maintain a safe return route to port. This helps avoid the risk of fuel shortage and allows the ship to return to port safely.

[0424] For example, if the terminal detects a large school of fish at a depth of 15 meters using the fish finder, the server will use this information to set an appropriate route, and the terminal will automatically steer the boat using that route. By minimizing the user's onboard operations and leaving the entire process to the system, efficient fishing becomes possible.

[0425] Thus, the present invention aims to provide a safe fishing experience that is easy for beginners to use and does not require a boat license.

[0426] The following describes the processing flow.

[0427] Step 1:

[0428] The terminal activates a fish finder, seabed scanner, and visibility sensor to collect real-time information on fish schools, seabed topography, and visibility data for the area. This includes the location and depth of fish schools, seabed topography, and weather conditions.

[0429] Step 2:

[0430] The server receives the collected data and performs analysis based on factors such as the fish school's location, water temperature, and depth. Calculations are then performed to identify activity patterns based on the target fish species.

[0431] Step 3:

[0432] The server identifies the optimal fishing grounds based on the analysis results. Fishing ground selection takes into account the activity patterns of the fish species, safety, and optimal environmental conditions.

[0433] Step 4:

[0434] The server calculates the optimal route to the identified fishing grounds. This calculation includes wind direction, currents, distance, and fuel consumption. At this stage, the most efficient and safest route is determined.

[0435] Step 5:

[0436] The terminal initiates autopilot of the vessel based on the calculated route. The user can review the route and make minor adjustments as needed, but essentially the system fully supports the navigation.

[0437] Step 6:

[0438] The server monitors the fuel level during navigation and adjusts the navigation mode as needed. This ensures safe and efficient navigation according to the fuel situation.

[0439] Step 7:

[0440] The server plans a safe return route to the nearest port. This route plan takes into account fuel level, wind direction, currents, and port location. The system helps ensure a safe return journey.

[0441] (Example 1)

[0442] 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."

[0443] In modern fishing, accurately understanding ocean conditions and selecting the optimal route is essential for efficiently catching schools of fish. However, with current technology, accurately analyzing the location and activity patterns of fish schools, and setting safe and efficient routes that take weather and currents into consideration, has been difficult. Furthermore, there has been a lack of means to efficiently maneuver while considering fuel consumption and to return to port safely.

[0444] 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.

[0445] In this invention, the server includes information acquisition means for collecting fish school detection information, analysis means for analyzing the activity patterns of fish schools based on the collected information to identify fishing grounds, route setting means for calculating the optimal route to the identified fishing grounds considering weather conditions and currents, and monitoring means for continuously monitoring fuel levels to maintain a safe return route. This enables fishing activities to be carried out safely and efficiently, and makes it easy for even beginners to enjoy fishing.

[0446] "Fish school detection information" refers to data that shows the location, density, and movement of schools of fish in the water.

[0447] "Information acquisition means" refers to a device or method for collecting data related to schools of fish using a fish finder, sensor, etc.

[0448] "Analysis means" refers to a device or method for analyzing the behavior and patterns of fish schools using collected data and for identifying fishing grounds.

[0449] "Route setting means" refers to a device or method for calculating the optimal route to the fishing grounds identified through analysis, taking into account safety and efficiency.

[0450] "Maneuvering means" refers to a system or method for guiding a vessel along a set course using automatic steering.

[0451] "Monitoring means" refers to a device or method for continuously checking the remaining fuel level and adjusting the return route to port as necessary.

[0452] "Correction means" refers to a device or method for improving the accuracy of detection information using environmental data.

[0453] "Safety analysis means" refers to a device or method that analyzes meteorological data and tidal current data and incorporates the results to ensure the safety of shipping lanes.

[0454] This system is designed to efficiently capture schools of fish and safely maneuver vessels, and mainly consists of information acquisition means, analysis means, route setting means, steering means, and monitoring means.

[0455] First, the device collects data about the sea area using fish finders and sensors. Specifically, it obtains information such as the location, depth, and density of fish schools from the fish finder, and collects weather information and seabed topography data from sensors. The hardware used in this process includes common fish finders and weather sensors.

[0456] Next, the data collected by the server is analyzed. Using advanced analytical algorithms, the activity patterns and movements of fish schools are predicted, and the optimal fishing grounds are identified based on the target fish species. Dedicated analytical software is used for this process.

[0457] Based on the analysis results, the server calculates the optimal route to the identified fishing grounds. It evaluates the effects of weather conditions and currents to set a route that balances vessel safety and fuel efficiency. High-performance navigation software is used for this route setting.

[0458] Subsequently, the terminal controls the vessel via the autopilot system according to the route information received from the server. The autopilot system follows the set route, requiring no special operation from the user. This allows even those with little experience operating vessels to navigate with confidence.

[0459] The server also continuously monitors the fuel level. Fuel sensors measure the rate of fuel consumption and make necessary adjustments for a safe return to port. For example, if the fuel level falls below a certain amount, the server can suggest an alternative return route to the user.

[0460] As a concrete example, consider the operation when a terminal detects a large school of fish at a depth of 15 meters using a fish finder. The server then sets an appropriate route based on this, and the terminal automatically steers the boat using that route. By entrusting the steering to this system and minimizing onboard operations, the user can enjoy efficient and safe fishing.

[0461] Examples of input prompts for a generative AI model:

[0462] "Based on the data obtained from the fish finder and sensors, calculate the optimal route considering the currents and weather conditions, and set the autopilot system accordingly."

[0463] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0464] Step 1:

[0465] The terminal uses a fish finder and sensors to collect data on the location of fish schools, weather information, and seabed topography. In this step, the location and depth of fish schools are determined based on position data (input) obtained from the fish finder, and weather data and seabed topography data (input) are collected from sensors. This information is integrated and output as environmental data to prepare for subsequent analysis. Specifically, the terminal periodically reads the sensors and updates the data in real time.

[0466] Step 2:

[0467] The server analyzes the information received from the terminal. Here, it uses the collected environmental data (input) to identify the activity patterns of fish schools and uses data analysis algorithms to predict the behavior of the target fish species. The result of this analysis is the optimal fishing ground and its location (output), which is used for setting the next fishing route. Specifically, the server runs the data analysis software, fits the input data into the model, and prepares to display the results to the user.

[0468] Step 3:

[0469] The server calculates the optimal route to the fishing grounds based on the analysis results. Inputs include location information of the fishing grounds, weather conditions, and tidal current data. From this data, navigation software is used to set a safe and efficient route (output). Specifically, the server activates the route calculation algorithm and sends the calculation results to the terminal.

[0470] Step 4:

[0471] The terminal controls the autopilot system based on the route information it receives. Following the calculated route (input), the terminal automatically steers the vessel and navigates to the set destination. The output is real-time location information of the vessel. Specifically, the terminal applies automatic steering instructions to steer and adjust speed.

[0472] Step 5:

[0473] The server monitors the fuel level and adjusts the return route as needed. In this step, it continuously analyzes data (input) from the fuel sensor, calculates fuel consumption, and proposes a safe return route. The output is a proposed route that allows for a safe return to port. Specifically, the server monitors the fuel status and notifies the user as appropriate.

[0474] (Application Example 1)

[0475] 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."

[0476] In addition to the challenges of efficiently catching schools of fish and safely navigating vessels, there are also challenges in efficiently managing urban traffic and enabling autonomous vehicles to reach their destinations safely while using energy efficiently. This invention aims to improve efficiency and safety in both fisheries and urban traffic.

[0477] 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.

[0478] In this invention, the server includes information acquisition means for collecting detection information of fish schools and traffic, analysis means for identifying fishing grounds and optimal routes based on the collected information, and route setting means for calculating routes to the identified fishing grounds and optimal routes. This enables efficient fish capture in fisheries, as well as congestion avoidance and improved energy efficiency in urban traffic.

[0479] "Information acquisition means" refers to devices and systems installed to collect detection information related to schools of fish and urban traffic.

[0480] "Analysis means" refers to the functions and processes used to analyze collected information and identify fishing grounds and optimal routes.

[0481] "Route setting means" refers to a system for planning and calculating sea routes to specific fishing grounds or optimal transportation routes.

[0482] "Operational means" refers to devices and technologies for automatically operating ships or means of transport based on a calculated route.

[0483] "Monitoring means" refers to functions that monitor fuel levels and energy consumption, and adjust the return route or the route to charging stations as needed.

[0484] "Correction measures" refer to technologies that adjust and modify environmental data and traffic data in order to improve the detection accuracy of collected information.

[0485] "Safety analysis methods" refer to systems and methods that perform analysis based on weather data and traffic data to provide safe shipping routes.

[0486] The server provides a system for collecting detection information on fish schools and urban traffic. This system utilizes drones and autonomous vehicles equipped with various sensors. The collected information is analyzed on the server and used to identify fishing grounds and plan optimal routes for urban traffic.

[0487] The analysis uses Python-based data analysis libraries (e.g., Pandas and NumPy) to calculate the optimal route based on the identified information. Existing map services such as the Google Maps API are utilized to provide route information for the calculation.

[0488] The terminal distributes calculated route information to the vehicle or vessel and performs autonomous driving. Small devices such as Raspberry Pi are expected to be used in the autonomous driving system. Monitoring measures will also monitor fuel and energy consumption and adjust charging stations and return routes as needed.

[0489] As a concrete example, for an event held in a city on a weekend, a route that avoids traffic congestion is proposed to allow participants to move smoothly, and autonomous vehicles can travel smoothly by following this route.

[0490] As an example of a prompt to the generated AI model, it provides explicit instructions such as, "Explain how to use real-time data and autonomous driving technology to calculate the optimal route to avoid congestion in smart city traffic management."

[0491] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0492] Step 1:

[0493] The server collects data on fish schools and traffic conditions from sensors mounted on drones and autonomous vehicles. The input is raw data detected by the sensors. Based on this, the server accumulates information in real time and can extract the necessary information. The output is a structured dataset.

[0494] Step 2:

[0495] The server analyzes the collected data to identify the location of fish schools and urban traffic congestion. The dataset obtained in Step 1 is used as input. The data is processed using a Python data analysis library, and the analysis results are output. The identified information enables future route planning.

[0496] Step 3:

[0497] The server calculates the optimal route to the fishing grounds and the optimal path within the city based on the analysis results. The input is the analysis results obtained in step 2. Using the Google Maps API, the calculated optimal route is output. This generates specific route information to achieve efficient travel.

[0498] Step 4:

[0499] The terminal transmits route information received from the server to the autonomous driving system, and the vehicle or vessel automatically begins to move. The input is the route information calculated in step 3. The terminal sends route instructions to the device's control system, and the actual operation begins as the output.

[0500] Step 5:

[0501] The server monitors the fuel and energy status of moving vehicles and vessels, and adjusts return-to-port or charging routes as needed. Input is fuel and battery data obtained from the vehicle. The server analyzes this information and issues instructions for returning to port or charging at the optimal time. This maximizes energy efficiency.

[0502] 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.

[0503] This invention provides a system for efficiently capturing schools of fish and for comfortably and safely operating a vessel, incorporating an emotion engine that recognizes user emotions and optimizes the experience. This system, along with information acquisition means, analysis means, route setting means, operation means, and monitoring means, provides an onboard experience tailored to the user's emotions and stress level by incorporating the emotion engine.

[0504] First, in terms of information acquisition, the terminal collects information on fish schools, seabed topography, and weather conditions in the sea area via fish finders and sensors. This information serves as basic data for the system to conduct efficient fishing.

[0505] Next, the analysis system operates on the server and analyzes the collected data. This allows for the identification of the fish school's location and the activity patterns of the target fish species, and analysis is then performed for selecting fishing grounds.

[0506] The route setting method involves a server calculating the optimal route based on the analysis results, and setting a safe and efficient route that takes into account weather conditions and tidal currents.

[0507] In terms of control, the terminal automatically steers the vessel according to a pre-set route. With the system's steering assistance, users can reach fishing grounds without requiring complex operations.

[0508] The monitoring system uses a server to monitor fuel levels and optimize the return route to port. This ensures a safe return to port.

[0509] Furthermore, the emotion engine recognizes the user's emotions in real time and provides feedback tailored to the user's state. For example, if the user is feeling stressed, the emotion engine detects this and activates a function to provide relaxing music or information.

[0510] For example, if a user experiences stress while sailing, the emotion engine detects this state, and the server provides appropriate music or guide information to reduce the user's stress level. This allows the user to enjoy fishing in a comfortable environment.

[0511] Thus, this invention not only improves the efficiency of fishing but also realizes a system that provides an experience that resonates with the user's emotions.

[0512] The following describes the processing flow.

[0513] Step 1:

[0514] The terminal activates fish finders and sensors to collect real-time information on fish schools, seabed topography, and weather conditions in the area. This provides data that can be used to find the most promising fishing grounds from the ship.

[0515] Step 2:

[0516] The server receives the collected data and analyzes the location of the fish school, water depth, and environmental conditions. Based on the server's analysis, it identifies appropriate fishing grounds for the target fish species.

[0517] Step 3:

[0518] The server calculates the optimal route to the identified fishing grounds based on the analysis results. Here, weather data and ocean currents are taken into consideration to set a safe and efficient navigation route.

[0519] Step 4:

[0520] The terminal automatically steers the vessel according to the calculated route and begins sailing towards the designated fishing grounds. User intervention is essentially unnecessary, as the system automatically performs the optimal steering.

[0521] Step 5:

[0522] The server monitors fuel consumption during navigation and adjusts the navigation mode as needed. This ensures that a fuel plan is maintained that allows for a safe return to port.

[0523] Step 6:

[0524] The emotion engine recognizes the user's emotions from their facial expressions, tone of voice, and other factors. The emotion data is sent to a server, where the user's stress level is analyzed.

[0525] Step 7:

[0526] Based on the analysis results of the emotion engine, the server provides suggestions and feedback tailored to the user's emotional state. For example, if it determines that relaxation is needed, the device will take actions such as playing relaxing music.

[0527] Step 8:

[0528] Upon returning to port, the server recalculates the optimal return route considering the fuel situation and provides instructions or prompts for adjustments to the user as needed, thereby supporting a safe return to port. As a result, the user can complete their voyage with peace of mind.

[0529] (Example 2)

[0530] 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."

[0531] Modern fishing activities require technologies for accurate detection of fish schools and efficient harvesting. At the same time, it is crucial to support operators in safely and comfortably managing their vessels. However, conventional technologies have been insufficient in handling complex data analysis and emotions, making efficient and safe fishing operations difficult.

[0532] 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.

[0533] In this invention, the server includes information acquisition means for collecting detection information of biological populations, analysis means for identifying fishing grounds based on the collected information, and route setting means for calculating a route to the identified fishing grounds. This makes it possible to efficiently capture biological populations while analyzing the user's emotions.

[0534] A "group of organisms" is a collection of marine organisms that exist in a specific body of water and move as a group.

[0535] "Information acquisition means" refers to devices or mechanisms that use fish finders or sensors to acquire information on the populations of organisms and environmental conditions from a body of water.

[0536] "Analysis means" refers to methods and devices for analyzing the location and patterns of biological populations based on collected information, and for identifying optimal fishing grounds.

[0537] "Route setting means" refers to methods and devices for calculating a safe and efficient route based on analysis results and applying it to a moving object.

[0538] "Maneuvering means" refers to a mechanism or technology for automatically steering a ship or moving object according to a calculated route.

[0539] "Monitoring means" refers to methods or devices for continuously monitoring the remaining fuel level and adjusting the return route to port as needed.

[0540] "Emotional analysis tools" are technologies and systems that analyze the user's emotional state and provide adaptive experiences tailored to that state.

[0541] This invention is a system for efficiently detecting populations of organisms in aquatic environments and enabling appropriate navigation, and it is primarily composed of a server and terminals. The server and terminals work together, coordinating multiple functions to allow users to operate the system comfortably and safely.

[0542] The server receives data from terminals that use fish finders and environmental sensors as means of acquiring information. This data includes the location of organism populations and environmental conditions. Next, the server uses analysis tools to analyze the collected data and identify the optimal fishing grounds. This analysis uses a generative AI model that learns data patterns and predicts the behavior of organisms.

[0543] Once the analysis is complete, the server uses a routing mechanism to calculate the optimal route to the identified fishing grounds. During this process, it utilizes a generative AI model and uses prompts to suggest routes that take into account conditions such as weather and currents. An example of a prompt is, "Please suggest the optimal sailing route considering wind strength."

[0544] The terminal acts as a control device, automatically steering the moving object according to a calculated route. This reduces the complexity of control for the user, enabling efficient arrival at the destination.

[0545] Furthermore, the server constantly monitors the remaining fuel level through monitoring devices and optimizes the return route as needed. This ensures the safe and reliable return of the mobile vehicle.

[0546] Furthermore, the emotion analysis system monitors the user's emotional state in real time and provides appropriate feedback. This feature can be used, for example, to suggest relaxing music if the user feels fatigued.

[0547] As described above, this invention is a system that not only efficiently captures groups of organisms but also provides an experience that takes into consideration the user's emotions.

[0548] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0549] Step 1:

[0550] The terminal functions as a means of acquiring information, collecting information on biological populations, seabed topography, and weather conditions from fish finders and sensors. The input consists of biosignals and environmental data obtained from the finders and sensors, which are transmitted to the server. The output is raw data provided to the server. Specifically, the terminal periodically activates sensors and samples data.

[0551] Step 2:

[0552] The server uses analytical tools to analyze data received from the terminal. The input data includes location information, movement patterns, and environmental conditions of biological populations. A generative AI model is used to learn data patterns and identify optimal fishing grounds. As output, recommended fishing ground locations based on the analysis results are generated. During processing, the server uses numerous datasets and implements a feedback loop to improve model accuracy.

[0553] Step 3:

[0554] The server uses a route setting mechanism to calculate the optimal route to the recommended fishing grounds. Input includes the fishing ground locations identified by the analysis mechanism and the latest weather and tidal current data. The server uses a generating AI model and simulates the optimal route based on prompt messages. The optimal route data is sent to the terminal as output. Specific operations include updating the simulation as needed in response to weather changes.

[0555] Step 4:

[0556] The terminal acts as the control system, automatically steering the vehicle according to route data provided by the server. The input is the optimal route information received from the server. The terminal integrates with GPS to adjust the direction of travel along the route in real time. The output is the vehicle moving along the correct navigation route. Specifically, the terminal autonomously performs position confirmation and obstacle avoidance maneuvers during navigation.

[0557] Step 5:

[0558] The server receives fuel level and location information transmitted from the terminal via monitoring devices to ensure safe navigation. Inputs are fuel level data and current location data. If a return to port is necessary, the server provides the terminal with a recalculated return route. Output is optimized return route information. Specifically, a fuel consumption prediction model is used to propose an optimized route early on.

[0559] Step 6:

[0560] The server uses emotion analysis tools to acquire user emotion data in real time. Input is emotion data from the user obtained through biosensors and voice analysis. The server inputs prompt sentences into a generation AI model, which then generates feedback appropriate to the user's state. The output includes music and information designed to reduce user stress. Specifically, the server analyzes the user's heart rate and facial expressions to select recommended content for relaxation.

[0561] (Application Example 2)

[0562] 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."

[0563] In fishing activities using autonomous vessels, it is necessary to ensure efficient detection of fish schools and safe autonomous navigation while also understanding the operator's emotions and stress levels to provide a comfortable working environment. However, conventional systems have not optimized the onboard environment, resulting in increased mental burden on operators and decreased work efficiency. Solving this problem is an urgent priority.

[0564] 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.

[0565] In this invention, the server includes information acquisition means for collecting fish school detection information, analysis means for identifying fishing grounds based on the collected information, and feedback generation means for analyzing emotional states and controlling the environment. This enables efficient capture of fish schools as well as the provision of a comfortable environment tailored to the operator's emotional state.

[0566] "Information acquisition means" refers to a device or system used to collect data about a school of fish.

[0567] "Analysis means" refers to a function that performs data processing to identify fishing grounds based on the collected information.

[0568] "Route setting means" refers to means for calculating the optimal sea route to a specified fishing ground.

[0569] "Maneuvering means" refers to the technology for automatically steering a ship based on a calculated route.

[0570] "Monitoring means" refers to a system that provides functions for monitoring fuel levels and adjusting the return route to port.

[0571] A "feedback generation method" is a means of analyzing emotional states and controlling the environment accordingly.

[0572] The system implementing this invention consists of multiple terminals located on board a vessel and a server located in the cloud. The terminals use fish finders and various sensors to collect fish school information and environmental data in the sea area in real time. This includes sensors and devices for acquiring underwater topographic information.

[0573] The server receives data obtained through information acquisition means and analyzes the activity patterns of fish using analysis means. Based on this information, the route setting means calculates the most efficient and safest route. This process involves many data calculations and can be done using Python or data analysis libraries (e.g., NumPy or Pandas).

[0574] The steering system uses an automated control system to steer the vessel along a set route, reducing the need for complex manual operation. Furthermore, the monitoring system constantly checks the fuel level and optimizes the return route if necessary.

[0575] The emotion engine, as a means of generating feedback, includes software for analyzing the emotional state of passengers. This includes facial expression analysis tools (e.g., OpenCV) and speech analysis platforms for processing audio data (e.g., Google Speech-to-Text API). Using this data, the server provides immediate feedback tailored to the emotional state of the passengers.

[0576] For example, if a situation is detected where a passenger is experiencing stress, the server can quickly improve the environment by selecting and playing soothing music and adjusting the ship's lighting.

[0577] As an example of a prompt, you can enter "Emotion Engine, the crew is experiencing stress while working. Please suggest what music to play and how to adjust the environment on board" to prepare the system to generate optimal feedback.

[0578] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0579] Step 1:

[0580] The terminal uses a fish finder to collect fish school information and environmental data. The input is real-time data of the sea area, and the output is digital fish school information and environmental data. This data includes the location of fish schools, seabed topography, and weather conditions.

[0581] Step 2:

[0582] The server receives information transmitted from the terminal and processes the data using analytical tools. The inputs are digital fish school information and environmental data, and the outputs are fish school activity patterns and information identifying potential fishing grounds. Statistical analysis is then performed using data analysis libraries (e.g., NumPy, Pandas).

[0583] Step 3:

[0584] The server activates the route setting mechanism based on the analysis results. The input is information identifying fishing grounds, and the output is optimized route information. Here, the optimal route is calculated considering weather data and ocean currents.

[0585] Step 4:

[0586] As a means of control, the terminal initiates autopilot according to the calculated route. The input is route information, and the output is the physical motion of the vessel. The automatic control system maintains the predetermined route by adjusting the engine and rudder.

[0587] Step 5:

[0588] As a monitoring tool, the server constantly monitors the fuel level. The input is data from the fuel sensor, and the output is real-time fuel level data. Based on this information, the return route is recalculated as needed.

[0589] Step 6:

[0590] As a means of generating feedback, the server uses an emotion engine to analyze the emotional state of the passengers. The input is audio and video data from the terminal, and the output is the analysis results regarding the passengers' emotional state. Here, a facial expression analysis tool (e.g., OpenCV) and an audio analysis platform (e.g., Google Speech-to-Text API) are used.

[0591] Step 7:

[0592] Based on their emotional state, the server provides appropriate feedback to the passengers. The input is the result of the emotional state analysis, and the output is music selection and environmental control commands. Specifically, the server plays calming music and adjusts the lighting inside the ship.

[0593] Step 8:

[0594] The user uses prompts to input additional commands into the system. The input is a prompt, and the output is a change in feedback from the system. For example, by giving the instruction, "Emotional Engine, the crew is experiencing stress during work. Suggest what music to play and how to adjust the environment," the system will take appropriate action.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] [Fourth Embodiment]

[0599] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0600] 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.

[0601] 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).

[0602] 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.

[0603] 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.

[0604] 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).

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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".

[0612] This invention relates to a system for efficiently capturing schools of fish and safely maneuvering a vessel. This system mainly consists of information acquisition means, analysis means, route setting means, maneuvering means, and monitoring means.

[0613] First, in terms of information acquisition methods, the terminal uses fish finders and sensors to collect information on fish schools in the sea area, weather information, and seabed topography data. This allows for the collection of detailed information such as the location and depth of fish schools.

[0614] Next, as an analysis method, the server analyzes the collected data to identify the activity patterns and locations of the fish schools. In this process, the data is evaluated according to the characteristics of the target fish species, and suitable fishing grounds are determined.

[0615] Subsequently, the route setting mechanism calculates the optimal route to the identified fishing grounds. This process takes into account weather conditions and currents to ensure a route that is both safe and efficient.

[0616] In terms of control, the terminal automatically steers the vessel according to a calculated route. The user requires no special operation; the system properly navigates the vessel to the fishing grounds.

[0617] In addition, as a monitoring measure, the server continuously monitors the fuel level and makes adjustments to maintain a safe return route to port. This helps avoid the risk of fuel shortage and allows the ship to return to port safely.

[0618] For example, if the terminal detects a large school of fish at a depth of 15 meters using the fish finder, the server will use this information to set an appropriate route, and the terminal will automatically steer the boat using that route. By minimizing the user's onboard operations and leaving the entire process to the system, efficient fishing becomes possible.

[0619] Thus, the present invention aims to provide a safe fishing experience that is easy for beginners to use and does not require a boat license.

[0620] The following describes the processing flow.

[0621] Step 1:

[0622] The terminal activates a fish finder, seabed scanner, and visibility sensor to collect real-time information on fish schools, seabed topography, and visibility data for the area. This includes the location and depth of fish schools, seabed topography, and weather conditions.

[0623] Step 2:

[0624] The server receives the collected data and performs analysis based on factors such as the fish school's location, water temperature, and depth. Calculations are then performed to identify activity patterns based on the target fish species.

[0625] Step 3:

[0626] The server identifies the optimal fishing grounds based on the analysis results. Fishing ground selection takes into account the activity patterns of the fish species, safety, and optimal environmental conditions.

[0627] Step 4:

[0628] The server calculates the optimal route to the identified fishing grounds. This calculation includes wind direction, currents, distance, and fuel consumption. At this stage, the most efficient and safest route is determined.

[0629] Step 5:

[0630] The terminal initiates autopilot of the vessel based on the calculated route. The user can review the route and make minor adjustments as needed, but essentially the system fully supports the navigation.

[0631] Step 6:

[0632] The server monitors the fuel level during navigation and adjusts the navigation mode as needed. This ensures safe and efficient navigation according to the fuel situation.

[0633] Step 7:

[0634] The server plans a safe return route to the nearest port. This route plan takes into account fuel level, wind direction, currents, and port location. The system helps ensure a safe return journey.

[0635] (Example 1)

[0636] 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".

[0637] In modern fishing, accurately understanding ocean conditions and selecting the optimal route is essential for efficiently catching schools of fish. However, with current technology, accurately analyzing the location and activity patterns of fish schools, and setting safe and efficient routes that take weather and currents into consideration, has been difficult. Furthermore, there has been a lack of means to efficiently maneuver while considering fuel consumption and to return to port safely.

[0638] 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.

[0639] In this invention, the server includes information acquisition means for collecting fish school detection information, analysis means for analyzing the activity patterns of fish schools based on the collected information to identify fishing grounds, route setting means for calculating the optimal route to the identified fishing grounds considering weather conditions and currents, and monitoring means for continuously monitoring fuel levels to maintain a safe return route. This enables fishing activities to be carried out safely and efficiently, and makes it easy for even beginners to enjoy fishing.

[0640] "Fish school detection information" refers to data that shows the location, density, and movement of schools of fish in the water.

[0641] "Information acquisition means" refers to a device or method for collecting data related to schools of fish using a fish finder, sensor, etc.

[0642] "Analysis means" refers to a device or method for analyzing the behavior and patterns of fish schools using collected data and for identifying fishing grounds.

[0643] "Route setting means" refers to a device or method for calculating the optimal route to the fishing grounds identified through analysis, taking into account safety and efficiency.

[0644] "Maneuvering means" refers to a system or method for guiding a vessel along a set course using automatic steering.

[0645] "Monitoring means" refers to a device or method for continuously checking the remaining fuel level and adjusting the return route to port as necessary.

[0646] "Correction means" refers to a device or method for improving the accuracy of detection information using environmental data.

[0647] "Safety analysis means" refers to a device or method that analyzes meteorological data and tidal current data and incorporates the results to ensure the safety of shipping lanes.

[0648] This system is designed to efficiently capture schools of fish and safely maneuver vessels, and mainly consists of information acquisition means, analysis means, route setting means, steering means, and monitoring means.

[0649] First, the device collects data about the sea area using fish finders and sensors. Specifically, it obtains information such as the location, depth, and density of fish schools from the fish finder, and collects weather information and seabed topography data from sensors. The hardware used in this process includes common fish finders and weather sensors.

[0650] Next, the data collected by the server is analyzed. Using advanced analytical algorithms, the activity patterns and movements of fish schools are predicted, and the optimal fishing grounds are identified based on the target fish species. Dedicated analytical software is used for this process.

[0651] Based on the analysis results, the server calculates the optimal route to the identified fishing grounds. It evaluates the effects of weather conditions and currents to set a route that balances vessel safety and fuel efficiency. High-performance navigation software is used for this route setting.

[0652] Subsequently, the terminal controls the vessel via the autopilot system according to the route information received from the server. The autopilot system follows the set route, requiring no special operation from the user. This allows even those with little experience operating vessels to navigate with confidence.

[0653] The server also continuously monitors the fuel level. Fuel sensors measure the rate of fuel consumption and make necessary adjustments for a safe return to port. For example, if the fuel level falls below a certain amount, the server can suggest an alternative return route to the user.

[0654] As a concrete example, consider the operation when a terminal detects a large school of fish at a depth of 15 meters using a fish finder. The server then sets an appropriate route based on this, and the terminal automatically steers the boat using that route. By entrusting the steering to this system and minimizing onboard operations, the user can enjoy efficient and safe fishing.

[0655] Examples of input prompts for a generative AI model:

[0656] "Based on the data obtained from the fish finder and sensors, calculate the optimal route considering the currents and weather conditions, and set the autopilot system accordingly."

[0657] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0658] Step 1:

[0659] The terminal uses a fish finder and sensors to collect data on the location of fish schools, weather information, and seabed topography. In this step, the location and depth of fish schools are determined based on position data (input) obtained from the fish finder, and weather data and seabed topography data (input) are collected from sensors. This information is integrated and output as environmental data to prepare for subsequent analysis. Specifically, the terminal periodically reads the sensors and updates the data in real time.

[0660] Step 2:

[0661] The server analyzes the information received from the terminal. Here, it uses the collected environmental data (input) to identify the activity patterns of fish schools and uses data analysis algorithms to predict the behavior of the target fish species. The result of this analysis is the optimal fishing ground and its location (output), which is used for setting the next fishing route. Specifically, the server runs the data analysis software, fits the input data into the model, and prepares to display the results to the user.

[0662] Step 3:

[0663] The server calculates the optimal route to the fishing grounds based on the analysis results. Inputs include location information of the fishing grounds, weather conditions, and tidal current data. From this data, navigation software is used to set a safe and efficient route (output). Specifically, the server activates the route calculation algorithm and sends the calculation results to the terminal.

[0664] Step 4:

[0665] The terminal controls the autopilot system based on the route information it receives. Following the calculated route (input), the terminal automatically steers the vessel and navigates to the set destination. The output is real-time location information of the vessel. Specifically, the terminal applies automatic steering instructions to steer and adjust speed.

[0666] Step 5:

[0667] The server monitors the fuel level and adjusts the return route as needed. In this step, it continuously analyzes data (input) from the fuel sensor, calculates fuel consumption, and proposes a safe return route. The output is a proposed route that allows for a safe return to port. Specifically, the server monitors the fuel status and notifies the user as appropriate.

[0668] (Application Example 1)

[0669] 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".

[0670] In addition to the challenges of efficiently catching schools of fish and safely navigating vessels, there are also challenges in efficiently managing urban traffic and enabling autonomous vehicles to reach their destinations safely while using energy efficiently. This invention aims to improve efficiency and safety in both fisheries and urban traffic.

[0671] 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.

[0672] In this invention, the server includes information acquisition means for collecting detection information of fish schools and traffic, analysis means for identifying fishing grounds and optimal routes based on the collected information, and route setting means for calculating routes to the identified fishing grounds and optimal routes. This enables efficient fish capture in fisheries, as well as congestion avoidance and improved energy efficiency in urban traffic.

[0673] "Information acquisition means" refers to devices and systems installed to collect detection information related to schools of fish and urban traffic.

[0674] "Analysis means" refers to the functions and processes used to analyze collected information and identify fishing grounds and optimal routes.

[0675] "Route setting means" refers to a system for planning and calculating sea routes to specific fishing grounds or optimal transportation routes.

[0676] "Operational means" refers to devices and technologies for automatically operating ships or means of transport based on a calculated route.

[0677] "Monitoring means" refers to functions that monitor fuel levels and energy consumption, and adjust the return route or the route to charging stations as needed.

[0678] "Correction measures" refer to technologies that adjust and modify environmental data and traffic data in order to improve the detection accuracy of collected information.

[0679] "Safety analysis methods" refer to systems and methods that perform analysis based on weather data and traffic data to provide safe shipping routes.

[0680] The server provides a system for collecting detection information on fish schools and urban traffic. This system utilizes drones and autonomous vehicles equipped with various sensors. The collected information is analyzed on the server and used to identify fishing grounds and plan optimal routes for urban traffic.

[0681] The analysis uses Python-based data analysis libraries (e.g., Pandas and NumPy) to calculate the optimal route based on the identified information. Existing map services such as the Google Maps API are utilized to provide route information for the calculation.

[0682] The terminal distributes calculated route information to the vehicle or vessel and performs autonomous driving. Small devices such as Raspberry Pi are expected to be used in the autonomous driving system. Monitoring measures will also monitor fuel and energy consumption and adjust charging stations and return routes as needed.

[0683] As a concrete example, for an event held in a city on a weekend, a route that avoids traffic congestion is proposed to allow participants to move smoothly, and autonomous vehicles can travel smoothly by following this route.

[0684] As an example of a prompt to the generated AI model, it provides explicit instructions such as, "Explain how to use real-time data and autonomous driving technology to calculate the optimal route to avoid congestion in smart city traffic management."

[0685] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0686] Step 1:

[0687] The server collects data on fish schools and traffic conditions from sensors mounted on drones and autonomous vehicles. The input is raw data detected by the sensors. Based on this, the server accumulates information in real time and can extract the necessary information. The output is a structured dataset.

[0688] Step 2:

[0689] The server analyzes the collected data to identify the location of fish schools and urban traffic congestion. The dataset obtained in Step 1 is used as input. The data is processed using a Python data analysis library, and the analysis results are output. The identified information enables future route planning.

[0690] Step 3:

[0691] The server calculates the optimal route to the fishing grounds and the optimal path within the city based on the analysis results. The input is the analysis results obtained in step 2. Using the Google Maps API, the calculated optimal route is output. This generates specific route information to achieve efficient travel.

[0692] Step 4:

[0693] The terminal transmits route information received from the server to the autonomous driving system, and the vehicle or vessel automatically begins to move. The input is the route information calculated in step 3. The terminal sends route instructions to the device's control system, and the actual operation begins as the output.

[0694] Step 5:

[0695] The server monitors the fuel and energy status of moving vehicles and vessels, and adjusts return-to-port or charging routes as needed. Input is fuel and battery data obtained from the vehicle. The server analyzes this information and issues instructions for returning to port or charging at the optimal time. This maximizes energy efficiency.

[0696] 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.

[0697] This invention provides a system for efficiently capturing schools of fish and for comfortably and safely operating a vessel, incorporating an emotion engine that recognizes user emotions and optimizes the experience. This system, along with information acquisition means, analysis means, route setting means, operation means, and monitoring means, provides an onboard experience tailored to the user's emotions and stress level by incorporating the emotion engine.

[0698] First, in terms of information acquisition, the terminal collects information on fish schools, seabed topography, and weather conditions in the sea area via fish finders and sensors. This information serves as basic data for the system to conduct efficient fishing.

[0699] Next, the analysis system operates on the server and analyzes the collected data. This allows for the identification of the fish school's location and the activity patterns of the target fish species, and analysis is then performed for selecting fishing grounds.

[0700] The route setting method involves a server calculating the optimal route based on the analysis results, and setting a safe and efficient route that takes into account weather conditions and tidal currents.

[0701] In terms of control, the terminal automatically steers the vessel according to a pre-set route. With the system's steering assistance, users can reach fishing grounds without requiring complex operations.

[0702] The monitoring system uses a server to monitor fuel levels and optimize the return route to port. This ensures a safe return to port.

[0703] Furthermore, the emotion engine recognizes the user's emotions in real time and provides feedback tailored to the user's state. For example, if the user is feeling stressed, the emotion engine detects this and activates a function to provide relaxing music or information.

[0704] For example, if a user experiences stress while sailing, the emotion engine detects this state, and the server provides appropriate music or guide information to reduce the user's stress level. This allows the user to enjoy fishing in a comfortable environment.

[0705] Thus, this invention not only improves the efficiency of fishing but also realizes a system that provides an experience that resonates with the user's emotions.

[0706] The following describes the processing flow.

[0707] Step 1:

[0708] The terminal activates fish finders and sensors to collect real-time information on fish schools, seabed topography, and weather conditions in the area. This provides data that can be used to find the most promising fishing grounds from the ship.

[0709] Step 2:

[0710] The server receives the collected data and analyzes the location of the fish school, water depth, and environmental conditions. Based on the server's analysis, it identifies appropriate fishing grounds for the target fish species.

[0711] Step 3:

[0712] The server calculates the optimal route to the identified fishing grounds based on the analysis results. Here, weather data and ocean currents are taken into consideration to set a safe and efficient navigation route.

[0713] Step 4:

[0714] The terminal automatically steers the vessel according to the calculated route and begins sailing towards the designated fishing grounds. User intervention is essentially unnecessary, as the system automatically performs the optimal steering.

[0715] Step 5:

[0716] The server monitors fuel consumption during navigation and adjusts the navigation mode as needed. This ensures that a fuel plan is maintained that allows for a safe return to port.

[0717] Step 6:

[0718] The emotion engine recognizes the user's emotions from their facial expressions, tone of voice, and other factors. The emotion data is sent to a server, where the user's stress level is analyzed.

[0719] Step 7:

[0720] Based on the analysis results of the emotion engine, the server provides suggestions and feedback tailored to the user's emotional state. For example, if it determines that relaxation is needed, the device will take actions such as playing relaxing music.

[0721] Step 8:

[0722] Upon returning to port, the server recalculates the optimal return route considering the fuel situation and provides instructions or prompts for adjustments to the user as needed, thereby supporting a safe return to port. As a result, the user can complete their voyage with peace of mind.

[0723] (Example 2)

[0724] 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".

[0725] Modern fishing activities require technologies for accurate detection of fish schools and efficient harvesting. At the same time, it is crucial to support operators in safely and comfortably managing their vessels. However, conventional technologies have been insufficient in handling complex data analysis and emotions, making efficient and safe fishing operations difficult.

[0726] 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.

[0727] In this invention, the server includes information acquisition means for collecting detection information of biological populations, analysis means for identifying fishing grounds based on the collected information, and route setting means for calculating a route to the identified fishing grounds. This makes it possible to efficiently capture biological populations while analyzing the user's emotions.

[0728] A "group of organisms" is a collection of marine organisms that exist in a specific body of water and move as a group.

[0729] "Information acquisition means" refers to devices or mechanisms that use fish finders or sensors to acquire information on the populations of organisms and environmental conditions from a body of water.

[0730] "Analysis means" refers to methods and devices for analyzing the location and patterns of biological populations based on collected information, and for identifying optimal fishing grounds.

[0731] "Route setting means" refers to methods and devices for calculating a safe and efficient route based on analysis results and applying it to a moving object.

[0732] "Maneuvering means" refers to a mechanism or technology for automatically steering a ship or moving object according to a calculated route.

[0733] "Monitoring means" refers to methods or devices for continuously monitoring the remaining fuel level and adjusting the return route to port as needed.

[0734] "Emotional analysis tools" are technologies and systems that analyze the user's emotional state and provide adaptive experiences tailored to that state.

[0735] This invention is a system for efficiently detecting populations of organisms in aquatic environments and enabling appropriate navigation, and it is primarily composed of a server and terminals. The server and terminals work together, coordinating multiple functions to allow users to operate the system comfortably and safely.

[0736] The server receives data from terminals that use fish finders and environmental sensors as means of acquiring information. This data includes the location of organism populations and environmental conditions. Next, the server uses analysis tools to analyze the collected data and identify the optimal fishing grounds. This analysis uses a generative AI model that learns data patterns and predicts the behavior of organisms.

[0737] Once the analysis is complete, the server uses a routing mechanism to calculate the optimal route to the identified fishing grounds. During this process, it utilizes a generative AI model and uses prompts to suggest routes that take into account conditions such as weather and currents. An example of a prompt is, "Please suggest the optimal sailing route considering wind strength."

[0738] The terminal acts as a control device, automatically steering the moving object according to a calculated route. This reduces the complexity of control for the user, enabling efficient arrival at the destination.

[0739] Furthermore, the server constantly monitors the remaining fuel level through monitoring devices and optimizes the return route as needed. This ensures the safe and reliable return of the mobile vehicle.

[0740] Furthermore, the emotion analysis system monitors the user's emotional state in real time and provides appropriate feedback. This feature can be used, for example, to suggest relaxing music if the user feels fatigued.

[0741] As described above, this invention is a system that not only efficiently captures groups of organisms but also provides an experience that takes into consideration the user's emotions.

[0742] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0743] Step 1:

[0744] The terminal functions as a means of acquiring information, collecting information on biological populations, seabed topography, and weather conditions from fish finders and sensors. The input consists of biosignals and environmental data obtained from the finders and sensors, which are transmitted to the server. The output is raw data provided to the server. Specifically, the terminal periodically activates sensors and samples data.

[0745] Step 2:

[0746] The server uses analytical tools to analyze data received from the terminal. The input data includes location information, movement patterns, and environmental conditions of biological populations. A generative AI model is used to learn data patterns and identify optimal fishing grounds. As output, recommended fishing ground locations based on the analysis results are generated. During processing, the server uses numerous datasets and implements a feedback loop to improve model accuracy.

[0747] Step 3:

[0748] The server uses a route setting mechanism to calculate the optimal route to the recommended fishing grounds. Input includes the fishing ground locations identified by the analysis mechanism and the latest weather and tidal current data. The server uses a generating AI model and simulates the optimal route based on prompt messages. The optimal route data is sent to the terminal as output. Specific operations include updating the simulation as needed in response to weather changes.

[0749] Step 4:

[0750] The terminal acts as the control system, automatically steering the vehicle according to route data provided by the server. The input is the optimal route information received from the server. The terminal integrates with GPS to adjust the direction of travel along the route in real time. The output is the vehicle moving along the correct navigation route. Specifically, the terminal autonomously performs position confirmation and obstacle avoidance maneuvers during navigation.

[0751] Step 5:

[0752] The server receives fuel level and location information transmitted from the terminal via monitoring devices to ensure safe navigation. Inputs are fuel level data and current location data. If a return to port is necessary, the server provides the terminal with a recalculated return route. Output is optimized return route information. Specifically, a fuel consumption prediction model is used to propose an optimized route early on.

[0753] Step 6:

[0754] The server uses emotion analysis tools to acquire user emotion data in real time. Input is emotion data from the user obtained through biosensors and voice analysis. The server inputs prompt sentences into a generation AI model, which then generates feedback appropriate to the user's state. The output includes music and information designed to reduce user stress. Specifically, the server analyzes the user's heart rate and facial expressions to select recommended content for relaxation.

[0755] (Application Example 2)

[0756] 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".

[0757] In fishing activities using autonomous vessels, it is necessary to ensure efficient detection of fish schools and safe autonomous navigation while also understanding the operator's emotions and stress levels to provide a comfortable working environment. However, conventional systems have not optimized the onboard environment, resulting in increased mental burden on operators and decreased work efficiency. Solving this problem is an urgent priority.

[0758] 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.

[0759] In this invention, the server includes information acquisition means for collecting fish school detection information, analysis means for identifying fishing grounds based on the collected information, and feedback generation means for analyzing emotional states and controlling the environment. This enables efficient capture of fish schools as well as the provision of a comfortable environment tailored to the operator's emotional state.

[0760] "Information acquisition means" refers to a device or system used to collect data about a school of fish.

[0761] "Analysis means" refers to a function that performs data processing to identify fishing grounds based on the collected information.

[0762] "Route setting means" refers to means for calculating the optimal sea route to a specified fishing ground.

[0763] "Maneuvering means" refers to the technology for automatically steering a ship based on a calculated route.

[0764] "Monitoring means" refers to a system that provides functions for monitoring fuel levels and adjusting the return route to port.

[0765] A "feedback generation method" is a means of analyzing emotional states and controlling the environment accordingly.

[0766] The system implementing this invention consists of multiple terminals located on board a vessel and a server located in the cloud. The terminals use fish finders and various sensors to collect fish school information and environmental data in the sea area in real time. This includes sensors and devices for acquiring underwater topographic information.

[0767] The server receives data obtained through information acquisition means and analyzes the activity patterns of fish using analysis means. Based on this information, the route setting means calculates the most efficient and safest route. This process involves many data calculations and can be done using Python or data analysis libraries (e.g., NumPy or Pandas).

[0768] The steering system uses an automated control system to steer the vessel along a set route, reducing the need for complex manual operation. Furthermore, the monitoring system constantly checks the fuel level and optimizes the return route if necessary.

[0769] The emotion engine, as a means of generating feedback, includes software for analyzing the emotional state of passengers. This includes facial expression analysis tools (e.g., OpenCV) and speech analysis platforms for processing audio data (e.g., Google Speech-to-Text API). Using this data, the server provides immediate feedback tailored to the emotional state of the passengers.

[0770] For example, if a situation is detected where a passenger is experiencing stress, the server can quickly improve the environment by selecting and playing soothing music and adjusting the ship's lighting.

[0771] As an example of a prompt, you can enter "Emotion Engine, the crew is experiencing stress while working. Please suggest what music to play and how to adjust the environment on board" to prepare the system to generate optimal feedback.

[0772] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0773] Step 1:

[0774] The terminal uses a fish finder to collect fish school information and environmental data. The input is real-time data of the sea area, and the output is digital fish school information and environmental data. This data includes the location of fish schools, seabed topography, and weather conditions.

[0775] Step 2:

[0776] The server receives information transmitted from the terminal and processes the data using analytical tools. The inputs are digital fish school information and environmental data, and the outputs are fish school activity patterns and information identifying potential fishing grounds. Statistical analysis is then performed using data analysis libraries (e.g., NumPy, Pandas).

[0777] Step 3:

[0778] The server activates the route setting mechanism based on the analysis results. The input is information identifying fishing grounds, and the output is optimized route information. Here, the optimal route is calculated considering weather data and ocean currents.

[0779] Step 4:

[0780] As a means of control, the terminal initiates autopilot according to the calculated route. The input is route information, and the output is the physical motion of the vessel. The automatic control system maintains the predetermined route by adjusting the engine and rudder.

[0781] Step 5:

[0782] As a monitoring tool, the server constantly monitors the fuel level. The input is data from the fuel sensor, and the output is real-time fuel level data. Based on this information, the return route is recalculated as needed.

[0783] Step 6:

[0784] As a means of generating feedback, the server uses an emotion engine to analyze the emotional state of the passengers. The input is audio and video data from the terminal, and the output is the analysis results regarding the passengers' emotional state. Here, a facial expression analysis tool (e.g., OpenCV) and an audio analysis platform (e.g., Google Speech-to-Text API) are used.

[0785] Step 7:

[0786] Based on their emotional state, the server provides appropriate feedback to the passengers. The input is the result of the emotional state analysis, and the output is music selection and environmental control commands. Specifically, the server plays calming music and adjusts the lighting inside the ship.

[0787] Step 8:

[0788] The user uses prompts to input additional commands into the system. The input is a prompt, and the output is a change in feedback from the system. For example, by giving the instruction, "Emotional Engine, the crew is experiencing stress during work. Suggest what music to play and how to adjust the environment," the system will take appropriate action.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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."

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] The following is further disclosed regarding the embodiments described above.

[0811] (Claim 1)

[0812] Information acquisition means for collecting fish school detection information,

[0813] An analytical method for identifying fishing grounds based on collected information,

[0814] A route setting means for calculating a route to a specified fishing ground,

[0815] A steering system that automatically steers a ship according to a calculated route,

[0816] A monitoring system that monitors fuel levels and adjusts the return route,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, further comprising correction means for correcting environmental data in order to improve the detection accuracy of collected information.

[0820] (Claim 3)

[0821] The system according to claim 1, further comprising a safety analysis means for analyzing weather data and incorporating it into the shipping route in order to ensure safety when navigating to a designated fishing ground.

[0822] "Example 1"

[0823] (Claim 1)

[0824] Information acquisition means for collecting fish school detection information,

[0825] An analytical method for identifying fishing grounds by analyzing the activity patterns of fish schools based on collected information,

[0826] A route setting means for calculating the optimal route to a specified fishing ground, taking into account weather conditions and currents,

[0827] A steering system that automatically steers a ship according to a calculated route,

[0828] A monitoring system that continuously monitors fuel levels to maintain a safe return route to port,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, further comprising correction means for correcting environmental data in order to improve the detection accuracy of collected information.

[0832] (Claim 3)

[0833] The system according to claim 1, further comprising a safety analysis means for analyzing weather data and tidal current data and reflecting them in the shipping route in order to ensure safety when navigating to a designated fishing ground.

[0834] "Application Example 1"

[0835] (Claim 1)

[0836] Information acquisition means for collecting detection information on fish schools and traffic,

[0837] An analytical means for identifying fishing grounds and optimal routes based on collected information,

[0838] Route setting means for calculating a route to a specified fishing ground and the optimal route,

[0839] A control system for automatically steering ships and means of transport according to a calculated route,

[0840] A monitoring means that monitors fuel and energy levels and adjusts the return to port and charging routes,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, further comprising correction means for correcting environmental and traffic data in order to improve the detection accuracy of the collected information.

[0844] (Claim 3)

[0845] The system according to claim 1, further comprising safety analysis means for analyzing weather and traffic data and reflecting it in the shipping route in order to ensure safety when navigating to a specified fishing ground and route.

[0846] "Example 2 of combining an emotion engine"

[0847] (Claim 1)

[0848] Information acquisition means for collecting detection information on populations of organisms,

[0849] An analytical method for identifying fishing grounds based on collected information,

[0850] A route setting means for calculating a route to a specified fishing ground,

[0851] A control system that automatically steers a moving object according to a calculated route,

[0852] A monitoring system that monitors fuel levels and adjusts the return route,

[0853] An emotion analysis tool that analyzes the user's emotions and provides an adaptive experience,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, further comprising adjustment means for adjusting environmental data to improve the detection performance of collected information.

[0857] (Claim 3)

[0858] The system according to claim 1, further comprising a safety analysis means for analyzing environmental condition data and reflecting it in the shipping route in order to ensure safety when navigating to a designated fishing ground.

[0859] "Application example 2 when combining with an emotional engine"

[0860] (Claim 1)

[0861] Information acquisition means for collecting fish school detection information,

[0862] An analytical method for identifying fishing grounds based on collected information,

[0863] A route setting means for calculating a route to a specified fishing ground,

[0864] A steering system that automatically steers a ship according to a calculated route,

[0865] A monitoring system that monitors fuel levels and adjusts the return route,

[0866] A feedback generation means that analyzes emotional states and controls the environment,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, further comprising correction means for correcting environmental data in order to improve the detection accuracy of collected information.

[0870] (Claim 3)

[0871] The system according to claim 1, further comprising a safety analysis means for analyzing weather data and incorporating it into the shipping route in order to ensure safety when navigating to a designated fishing ground. [Explanation of symbols]

[0872] 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. Information acquisition means for collecting detection information on fish schools and traffic, An analytical means for identifying fishing grounds and optimal routes based on collected information, Route setting means for calculating a route to a specified fishing ground and the optimal route, A control system for automatically steering ships and means of transport according to a calculated route, A monitoring means that monitors fuel and energy levels and adjusts the return to port and charging routes, A system that includes this.

2. The system according to claim 1, further comprising correction means for correcting environmental and traffic data in order to improve the detection accuracy of collected information.

3. The system according to claim 1, further comprising safety analysis means for analyzing weather and traffic data and reflecting it in the shipping route in order to ensure safety when navigating to a specified fishing ground and route.

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