system

Abstract

The system according to this embodiment aims to improve the efficiency of fishing activities and realize sustainable fisheries. [Solution] The system according to the embodiment comprises a collection unit, a proposal unit, a prediction unit, and a management unit. The collection unit collects information from satellite data and ocean sensors. The proposal unit analyzes the information collected by the collection unit and proposes the optimal fishing grounds. The prediction unit integrates weather forecasts, ocean currents, and tidal information to support safe and efficient navigation. The management unit automatically records and analyzes catch data to support resource management and regulatory compliance.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the identification of optimal fishing grounds, safe navigation, and resource management in fishing activities are not sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to streamline fishing activities and achieve sustainable fishing.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a proposal unit, a forecast unit, and a management unit. The collection unit collects information from satellite data and ocean sensors. The proposal unit analyzes the information collected by the collection unit and proposes the optimal fishing grounds. The forecast unit integrates weather forecasts, ocean currents, and tidal information to support safe and efficient navigation. The management unit automatically records and analyzes catch data to support resource management and regulatory compliance. [Effects of the Invention]

[0007] The system according to this embodiment can streamline fishing activities and realize sustainable fisheries. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] 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.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] 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.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system for supporting fishermen. This system is designed to improve fishing efficiency and realize sustainable fisheries. The AI ​​agent system analyzes vast amounts of ocean data and provides support in all aspects of fishing activities, such as suggesting optimal fishing grounds, predicting weather and sea conditions, and managing catch volumes. This allows fishermen to maximize profits while reducing their environmental impact. For example, the AI ​​agent system analyzes information collected from satellite data and ocean sensors and suggests optimal fishing grounds in real time. Furthermore, it integrates weather forecasts, ocean currents, and tidal information to support safe and efficient navigation. It also automatically records and analyzes catch data to support resource management and regulatory compliance. In addition, it makes suggestions to prevent overfishing and recommends environmentally friendly fishing methods. Finally, it facilitates information sharing with other fishermen and onshore staff. This allows fishermen to maximize profits while reducing their environmental impact. In this way, the AI ​​agent system can support fishermen and realize improved fishing efficiency and sustainable fisheries.

[0029] The AI ​​agent system according to this embodiment comprises a data collection unit, a proposal unit, a prediction unit, and a management unit. The data collection unit collects information from satellite data and ocean sensors. For example, the data collection unit can collect weather satellite data and ocean observation satellite data. The data collection unit can also collect data from ocean sensors such as water temperature sensors and salinity sensors. The proposal unit analyzes the information collected by the data collection unit and proposes the optimal fishing grounds. For example, the proposal unit analyzes data such as seawater temperature, salinity, and plankton distribution to predict the location of fish schools. The proposal unit can use AI to propose the optimal fishing grounds based on the collected data. The prediction unit integrates weather forecasts, ocean currents, and tidal information to support safe and efficient navigation. For example, the prediction unit analyzes weather data and proposes the optimal route to fishermen. The prediction unit can use AI to support navigation based on weather forecasts, ocean currents, and tidal information. The management unit automatically records and analyzes catch data to support resource management and regulatory compliance. The management department, for example, records catch amounts in real time and makes suggestions to prevent overfishing. The management department can use AI to analyze catch data and support resource management and regulatory compliance. In this way, the AI ​​agent system according to the embodiment can support fishermen and realize improved fishing efficiency and sustainable fisheries.

[0030] The data collection unit gathers information from satellite data and ocean sensors. Specifically, it can collect weather satellite data and ocean observation satellite data. Weather satellite data provides information on the Earth's atmosphere, cloud movements, and precipitation, while ocean observation satellite data provides information on sea surface temperature, ocean currents, and sea ice distribution. The data collection unit can also collect data from ocean sensors such as water temperature sensors and salinity sensors. These sensors monitor changes in the ocean environment in real time and detect fluctuations in seawater temperature and salinity. Furthermore, the data collection unit can utilize biosensors to understand the distribution of plankton and the movements of marine organisms. This allows the data collection unit to collect a wide range of data and understand the detailed state of the marine environment. The collected data is transmitted to a central database, making it accessible to other departments. The frequency and accuracy of data collection are adjusted according to specific situations and conditions, enabling efficient and effective data collection. This allows the data collection unit to provide accurate and up-to-date information to fishermen and support the planning and execution of fishing activities.

[0031] The proposal department analyzes the information collected by the data collection department and proposes optimal fishing grounds. Specifically, it analyzes data such as seawater temperature, salinity, and plankton distribution to predict the location of fish schools. The proposal department can use AI to propose optimal fishing grounds based on the collected data. The AI ​​uses machine learning algorithms to compare past and current data and analyze fish school movement patterns and habitats. For example, if it is known that a particular fish species tends to gather when the seawater temperature is within a certain range, the proposal department will use this information to propose the optimal fishing grounds. In addition, salinity and plankton distribution also affect the location of fish schools, so by comprehensively analyzing this data, it becomes possible to propose more accurate fishing grounds. The proposal department provides the analysis results to fishermen to support efficient fishing activities. Furthermore, the proposal department can collect feedback from fishermen and continuously improve the accuracy and effectiveness of its proposals. In this way, the proposal department can propose optimal fishing grounds to fishermen and improve fishing efficiency.

[0032] The forecasting unit integrates weather forecasts, ocean currents, and tidal information to support safe and efficient navigation. Specifically, it analyzes meteorological data and proposes optimal routes for fishermen. The forecasting unit can support navigation based on weather forecasts, ocean currents, and tidal information using AI. The AI ​​analyzes meteorological data and proposes suitable times and routes for navigation based on information such as wind speed, wind direction, and precipitation. It also analyzes ocean currents and tidal information to calculate the optimal route to improve safety and efficiency during navigation. For example, if strong winds or high waves are expected, the forecasting unit will change the navigation route based on that information to ensure safe navigation. Furthermore, the forecasting unit can continuously revise its forecast results based on data that is updated in real time, allowing it to respond to the latest conditions. In this way, the forecasting unit can support safe and efficient navigation for fishermen and minimize the risks to fishing activities.

[0033] The management department automatically records and analyzes catch data to support resource management and regulatory compliance. Specifically, it records catch volumes in real time and makes suggestions to prevent overfishing. The management department can use AI to analyze catch data and support resource management and regulatory compliance. The AI ​​analyzes catch data to understand the distribution of catch volumes and species and assesses the risk of overfishing. It also proposes catch limits and protection of specific fish species based on fishing regulations. For example, if a particular fish species is being overfished, the management department can use this information to propose catch limits to fishermen and promote the sustainable use of resources. Furthermore, the management department can collect feedback from fishermen and continuously improve the accuracy and effectiveness of management methods. In this way, the management department can support the sustainable management of fishery resources and regulatory compliance, and ensure the long-term stability of fishing activities.

[0034] The Communication Support Department facilitates information sharing among fishermen and onshore staff. For example, it provides a platform where fishermen can share information in real time. The Communication Support Department can use AI to improve the efficiency of information sharing. For example, it provides a chat function for fishermen to share catch information and weather information. It can also provide a schedule management function for fishermen to work in coordination with other fishermen and onshore staff. This makes information sharing among fishermen smoother and enables more efficient fishing activities.

[0035] The proposed unit can analyze data such as seawater temperature, salinity, and plankton distribution to predict the location of fish schools. For example, the proposed unit can analyze data obtained from a seawater temperature sensor to predict the location of fish schools. The proposed unit can also analyze data obtained from a salinity sensor to predict the location of fish schools. The proposed unit can also analyze plankton distribution data to predict the location of fish schools. This makes it possible to accurately predict the location of fish schools and improve fishing efficiency. Some or all of the above processing in the proposed unit may be performed using AI or not. For example, the proposed unit can input seawater temperature, salinity, and plankton distribution data into a generating AI and have the generating AI perform the prediction of the location of fish schools.

[0036] The forecasting unit can analyze meteorological data and propose the optimal route to fishermen. For example, the forecasting unit can analyze meteorological data such as wind speed, wind direction, and precipitation to propose the optimal route. The forecasting unit can use AI to propose the optimal route based on meteorological data. For example, the forecasting unit can analyze wind speed data and propose the optimal route. The forecasting unit can also analyze wind direction data and propose the optimal route. The forecasting unit can also analyze precipitation data and propose the optimal route. By proposing the optimal route, it is possible to support safe and efficient navigation. Some or all of the above processing in the forecasting unit may be performed using AI or not. For example, the forecasting unit can input meteorological data into a generating AI and have the generating AI propose the optimal route.

[0037] The management department can record catch data in real time and make suggestions to prevent overfishing. For example, the management department can use sensors to record catch data in real time. The management department can use AI to analyze the real-time recorded catch data and make suggestions to prevent overfishing. For example, the management department can analyze the catch data and issue a warning if there is a high risk of overfishing. Based on the catch data, the management department can also propose fishing limits to fishermen. The management department can also analyze the catch data and make suggestions to achieve sustainable fisheries. This will prevent overfishing and achieve sustainable fisheries. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input real-time recorded catch data into a generating AI and have the generating AI execute suggestions to prevent overfishing.

[0038] The management department can propose restrictions on the catch of specific fish species. For example, the management department can propose catch restrictions for protected fish species. The management department can use AI to analyze catch data for specific fish species and propose catch restrictions. For example, the management department can analyze catch volume data for specific fish species and determine the need for catch restrictions. The management department can also make proposals to promote the protection of specific fish species. Based on catch data for specific fish species, the management department can also propose catch restrictions to fishermen. This can promote the protection of specific fish species and support resource management. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input catch data for specific fish species into a generating AI and have the generating AI execute catch restriction proposals.

[0039] The data collection unit can analyze past fishing data and select the optimal data collection method. For example, the data collection unit can identify data to be collected in a specific season from past fishing data. The data collection unit can also optimize the data collection method in a specific sea area based on past fishing data. The data collection unit can also analyze past fishing data and determine the types of data to be collected. This enables efficient data collection by utilizing past data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past fishing data into a generating AI and have the generating AI select the optimal data collection method.

[0040] The data collection unit can filter data based on the fisherman's current fishing activities and areas of interest during data collection. For example, if a fisherman is interested in a particular fish species, the data collection unit will prioritize collecting data related to that species. If a fisherman fishes in a particular sea area, the data collection unit can also prioritize collecting data related to that sea area. The data collection unit can also filter and collect necessary data based on the fisherman's current fishing activities. This allows for the priority collection of data tailored to the fisherman's interests and efficient information provision. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the fisherman's current fishing activities into a generating AI and have the generating AI perform data filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of fishermen during data collection. For example, if a fisherman is in a specific sea area, the data collection unit will prioritize the collection of data related to that area. The data collection unit can also collect the most relevant data based on the fisherman's current location. The data collection unit can also analyze the fisherman's movement patterns and collect highly relevant data. This allows the data collection to be based on the fisherman's current location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the fisherman's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze the social media activities of fishermen and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by fishermen on social media. The data collection unit can also analyze the social media activities of fishermen and collect data of interest. The data collection unit can also collect data related to topics mentioned by fishermen on social media. This allows for the collection of relevant data based on the social media activities of fishermen. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input fishermen's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0043] The proposal unit can adjust the level of detail of its proposals based on the importance of the fishing grounds. For example, the proposal unit can provide detailed proposals for important fishing grounds, and concise proposals for less important fishing grounds. The proposal unit can also adjust the level of detail of its proposals in stages according to the importance of the fishing grounds. This allows for proposals with an appropriate level of detail according to the importance of the fishing grounds. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input fishing ground importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0044] The proposal unit can apply different proposal algorithms depending on the fishing ground category when making a proposal. For example, the proposal unit can apply a specific algorithm to coastal fishing grounds. The proposal unit can also apply a different algorithm to deep-sea fishing grounds. The proposal unit can also select the optimal proposal algorithm depending on the fishing ground category. This allows the optimal proposal algorithm to be applied according to the fishing ground category. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input fishing ground category data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0045] The proposal unit can determine the priority of proposals based on the timing of data collection for fishing grounds when making a proposal. For example, the proposal unit can prioritize proposals based on recently collected data. The proposal unit can also lower the priority of proposals based on older data. The proposal unit can also adjust the priority of proposals in stages according to the data collection timing. This allows for the provision of high-priority proposals based on the data collection timing. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input data collection timing data into a generating AI and have the generating AI determine the priority of proposals.

[0046] The proposal unit can adjust the order of proposals based on the relevance of the fishing grounds. For example, the proposal unit may prioritize proposing fishing grounds that are most relevant to the fishermen's current fishing activities. The proposal unit may also prioritize proposing highly relevant fishing grounds based on the fishermen's interests. The proposal unit may also adjust the order of proposals in stages according to the relevance of the fishing grounds. This allows for proposals to be made in the optimal order according to the relevance of the fishing grounds. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input fishing ground relevance data into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0047] The forecasting unit can optimize the current forecast by referring to past weather data during the forecasting process. For example, the forecasting unit optimizes the current weather forecast based on past weather data. The forecasting unit can also improve the accuracy of ocean condition forecasts by referring to past weather data. The forecasting unit can also analyze past weather data and reflect it in the current forecast. This allows for improved accuracy of the current forecast by utilizing past weather data. Some or all of the above-described processes in the forecasting unit may be performed using AI or not. For example, the forecasting unit can input past weather data into a generating AI and have the generating AI perform the optimization of the current forecast.

[0048] The prediction unit can apply different prediction algorithms to each fishing ground category during prediction. For example, the prediction unit can apply a specific prediction algorithm to coastal fishing grounds. It can also apply a different prediction algorithm to deep-sea fishing grounds. The prediction unit can also select the optimal prediction algorithm according to the fishing ground category. This allows the optimal prediction algorithm to be applied according to the fishing ground category. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input fishing ground category data into a generating AI and have the generating AI execute the application of the prediction algorithm.

[0049] The forecasting unit can improve the accuracy of its forecasts based on the timing of meteorological data collection. For example, the forecasting unit can improve the accuracy of its forecasts based on recently collected meteorological data. The forecasting unit can also reduce the accuracy of forecasts based on older meteorological data. The forecasting unit can also adjust the accuracy of its forecasts in stages according to the timing of meteorological data collection. This allows for improved forecast accuracy based on the timing of meteorological data collection. Some or all of the above-described processes in the forecasting unit may be performed using AI or not. For example, the forecasting unit can input meteorological data collection timing data into a generating AI and have the generating AI perform the improvement of forecast accuracy.

[0050] The prediction unit can improve the accuracy of its predictions by referring to relevant oceanographic data during the prediction process. For example, the prediction unit can improve the accuracy of weather forecasts based on oceanographic data. The prediction unit can also improve the accuracy of ocean condition forecasts by referring to oceanographic data. The prediction unit can also improve the accuracy of its predictions by analyzing relevant oceanographic data. This allows for improved prediction accuracy by utilizing relevant oceanographic data. Some or all of the above-described processes in the prediction unit may be performed using AI or not. For example, the prediction unit can input relevant oceanographic data into a generating AI and have the generating AI perform the task of improving the accuracy of the predictions.

[0051] The management department can analyze past catch data to select the optimal management method during management. For example, the management department can select a method to manage the catch volume of a specific fish species based on past catch data. The management department can also analyze past catch data and propose management methods that respond to fluctuations in catch volume. The management department can also refer to past catch data to select management methods that will enable sustainable fisheries. In this way, the optimal management method can be selected by utilizing past catch data. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input past catch data into a generating AI and have the generating AI select the optimal management method.

[0052] The management unit can customize management methods based on the fisherman's current fishing activities during management. For example, if a fisherman targets a specific fish species, the management unit will propose management methods specific to that species. The management unit can also customize the optimal management methods based on the fisherman's current fishing activities. The management unit can also flexibly adjust the management methods according to the fisherman's fishing activity status. This allows the management unit to provide the optimal management methods according to the fisherman's current fishing activities. Some or all of the above processes in the management unit may be performed using AI or not. For example, the management unit can input the fisherman's current fishing activity data into a generating AI and have the generating AI perform the customization of the management methods.

[0053] The management department can select the optimal management method during management, taking into account the geographical location information of fishermen. For example, if a fisherman is in a specific sea area, the management department will select a management method suitable for that area. The management department can also select the most effective management method based on the fisherman's current location. The management department can also analyze the fisherman's movement patterns and select the optimal management method. This allows the management department to select the optimal management method based on the fisherman's geographical location information. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input the fisherman's geographical location information into a generating AI and have the generating AI select the optimal management method.

[0054] The management department can analyze the social media activities of fishermen and propose management measures during the management process. For example, the management department can propose relevant management measures based on information shared by fishermen on social media. The management department can also analyze the social media activities of fishermen and propose management measures of interest. The management department can also propose management measures related to topics mentioned by fishermen on social media. This allows the management department to propose relevant management measures based on the social media activities of fishermen. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input fishermen's social media activity data into a generating AI and have the generating AI execute the proposal of management measures.

[0055] The Communication Support Department can select the optimal support method by referring to past communication history when providing communication support. For example, the Communication Support Department can select the optimal support method based on past communication history. The Communication Support Department can also analyze past communication history and propose effective support methods. The Communication Support Department can also select a support method suitable for fishermen by referring to past communication history. In this way, the optimal support method can be selected by utilizing past communication history. Some or all of the above processes in the Communication Support Department may be performed using AI or not. For example, the Communication Support Department can input past communication history data into a generating AI and have the generating AI perform the selection of the optimal support method.

[0056] The communication support unit can select the optimal support method when providing communication support, taking into account the fisherman's device information. For example, if the fisherman is using a smartphone, the communication support unit can provide a support method that is adapted to the screen size. If the fisherman is using a tablet, the communication support unit can also provide a support method optimized for a larger screen. If the fisherman is using a smartwatch, the communication support unit can also provide a concise and highly visible support method. This allows the unit to provide the optimal support method based on the fisherman's device information. Some or all of the above processing in the communication support unit may be performed using AI or not. For example, the communication support unit can input the fisherman's device information into a generating AI and have the generating AI select the optimal support method.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The data collection unit can prioritize data collection based on the fishermen's current fishing activities and areas of interest. For example, if a fisherman is interested in a particular fish species, data related to that species will be collected preferentially. If a fisherman fishes in a specific sea area, data related to that area can also be collected preferentially. This allows for the collection of data tailored to the fishermen's interests and enables efficient information provision.

[0059] The management department can analyze past fishing data and select the optimal management method. For example, it can select a method for managing the catch volume of a specific fish species based on past fishing data. It can also analyze past fishing data and propose management methods that respond to fluctuations in catch volume. This allows for the selection of the optimal management method by utilizing past fishing data.

[0060] The forecasting unit can optimize current forecasts by referring to past weather data. For example, it optimizes current weather forecasts based on past weather data. It can also improve the accuracy of ocean condition forecasts by referring to past weather data. In this way, the accuracy of current forecasts can be improved by utilizing past weather data.

[0061] The proposal function can apply different proposal algorithms depending on the fishing ground category. For example, a specific algorithm can be applied to coastal fishing grounds, while a different algorithm can be applied to deep-sea fishing grounds. This allows for the application of the most suitable proposal algorithm for each fishing ground category.

[0062] The Communication Support Department can select the optimal support method by considering the fisherman's device information. For example, if a fisherman is using a smartphone, it can provide a support method adapted to the screen size. If a fisherman is using a tablet, it can also provide a support method optimized for the larger screen. This allows the department to provide the most suitable support method based on the fisherman's device information.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The collection unit collects information from satellite data and ocean sensors. For example, it can collect data from weather satellites, ocean observation satellites, water temperature sensors, salinity sensors, and so on. Step 2: The proposal unit analyzes the information collected by the collection unit and proposes the optimal fishing grounds. For example, it analyzes data such as seawater temperature, salinity, and plankton distribution, uses AI to predict the location of fish schools, and proposes the optimal fishing grounds. Step 3: The forecasting unit integrates weather forecasts, ocean currents, and tidal information to support safe and efficient navigation. For example, it analyzes weather data and uses AI to suggest optimal routes to fishermen. Step 4: The management department automatically records and analyzes catch data to support resource management and regulatory compliance. For example, it records catch amounts in real time and makes suggestions to prevent overfishing. It uses AI to analyze catch data to support resource management and regulatory compliance.

[0065] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system for supporting fishermen. This system is designed to improve fishing efficiency and realize sustainable fisheries. The AI ​​agent system analyzes vast amounts of ocean data and provides support in all aspects of fishing activities, such as suggesting optimal fishing grounds, predicting weather and sea conditions, and managing catch volumes. This allows fishermen to maximize profits while reducing their environmental impact. For example, the AI ​​agent system analyzes information collected from satellite data and ocean sensors and suggests optimal fishing grounds in real time. Furthermore, it integrates weather forecasts, ocean currents, and tidal information to support safe and efficient navigation. It also automatically records and analyzes catch data to support resource management and regulatory compliance. In addition, it makes suggestions to prevent overfishing and recommends environmentally friendly fishing methods. Finally, it facilitates information sharing with other fishermen and onshore staff. This allows fishermen to maximize profits while reducing their environmental impact. In this way, the AI ​​agent system can support fishermen and realize improved fishing efficiency and sustainable fisheries.

[0066] The AI ​​agent system according to this embodiment comprises a data collection unit, a proposal unit, a prediction unit, and a management unit. The data collection unit collects information from satellite data and ocean sensors. For example, the data collection unit can collect weather satellite data and ocean observation satellite data. The data collection unit can also collect data from ocean sensors such as water temperature sensors and salinity sensors. The proposal unit analyzes the information collected by the data collection unit and proposes the optimal fishing grounds. For example, the proposal unit analyzes data such as seawater temperature, salinity, and plankton distribution to predict the location of fish schools. The proposal unit can use AI to propose the optimal fishing grounds based on the collected data. The prediction unit integrates weather forecasts, ocean currents, and tidal information to support safe and efficient navigation. For example, the prediction unit analyzes weather data and proposes the optimal route to fishermen. The prediction unit can use AI to support navigation based on weather forecasts, ocean currents, and tidal information. The management unit automatically records and analyzes catch data to support resource management and regulatory compliance. The management department, for example, records catch amounts in real time and makes suggestions to prevent overfishing. The management department can use AI to analyze catch data and support resource management and regulatory compliance. In this way, the AI ​​agent system according to the embodiment can support fishermen and realize improved fishing efficiency and sustainable fisheries.

[0067] The data collection unit gathers information from satellite data and ocean sensors. Specifically, it can collect weather satellite data and ocean observation satellite data. Weather satellite data provides information on the Earth's atmosphere, cloud movements, and precipitation, while ocean observation satellite data provides information on sea surface temperature, ocean currents, and sea ice distribution. The data collection unit can also collect data from ocean sensors such as water temperature sensors and salinity sensors. These sensors monitor changes in the ocean environment in real time and detect fluctuations in seawater temperature and salinity. Furthermore, the data collection unit can utilize biosensors to understand the distribution of plankton and the movements of marine organisms. This allows the data collection unit to collect a wide range of data and understand the detailed state of the marine environment. The collected data is transmitted to a central database, making it accessible to other departments. The frequency and accuracy of data collection are adjusted according to specific situations and conditions, enabling efficient and effective data collection. This allows the data collection unit to provide accurate and up-to-date information to fishermen and support the planning and execution of fishing activities.

[0068] The proposal department analyzes the information collected by the data collection department and proposes optimal fishing grounds. Specifically, it analyzes data such as seawater temperature, salinity, and plankton distribution to predict the location of fish schools. The proposal department can use AI to propose optimal fishing grounds based on the collected data. The AI ​​uses machine learning algorithms to compare past and current data and analyze fish school movement patterns and habitats. For example, if it is known that a particular fish species tends to gather when the seawater temperature is within a certain range, the proposal department will use this information to propose the optimal fishing grounds. In addition, salinity and plankton distribution also affect the location of fish schools, so by comprehensively analyzing this data, it becomes possible to propose more accurate fishing grounds. The proposal department provides the analysis results to fishermen to support efficient fishing activities. Furthermore, the proposal department can collect feedback from fishermen and continuously improve the accuracy and effectiveness of its proposals. In this way, the proposal department can propose optimal fishing grounds to fishermen and improve fishing efficiency.

[0069] The forecasting unit integrates weather forecasts, ocean currents, and tidal information to support safe and efficient navigation. Specifically, it analyzes meteorological data and proposes optimal routes for fishermen. The forecasting unit can support navigation based on weather forecasts, ocean currents, and tidal information using AI. The AI ​​analyzes meteorological data and proposes suitable times and routes for navigation based on information such as wind speed, wind direction, and precipitation. It also analyzes ocean currents and tidal information to calculate the optimal route to improve safety and efficiency during navigation. For example, if strong winds or high waves are expected, the forecasting unit will change the navigation route based on that information to ensure safe navigation. Furthermore, the forecasting unit can continuously revise its forecast results based on data that is updated in real time, allowing it to respond to the latest conditions. In this way, the forecasting unit can support safe and efficient navigation for fishermen and minimize the risks to fishing activities.

[0070] The management department automatically records and analyzes catch data to support resource management and regulatory compliance. Specifically, it records catch volumes in real time and makes suggestions to prevent overfishing. The management department can use AI to analyze catch data and support resource management and regulatory compliance. The AI ​​analyzes catch data to understand the distribution of catch volumes and species and assesses the risk of overfishing. It also proposes catch limits and protection of specific fish species based on fishing regulations. For example, if a particular fish species is being overfished, the management department can use this information to propose catch limits to fishermen and promote the sustainable use of resources. Furthermore, the management department can collect feedback from fishermen and continuously improve the accuracy and effectiveness of management methods. In this way, the management department can support the sustainable management of fishery resources and regulatory compliance, and ensure the long-term stability of fishing activities.

[0071] The Communication Support Department facilitates information sharing among fishermen and onshore staff. For example, it provides a platform where fishermen can share information in real time. The Communication Support Department can use AI to improve the efficiency of information sharing. For example, it provides a chat function for fishermen to share catch information and weather information. It can also provide a schedule management function for fishermen to work in coordination with other fishermen and onshore staff. This makes information sharing among fishermen smoother and enables more efficient fishing activities.

[0072] The proposed unit can analyze data such as seawater temperature, salinity, and plankton distribution to predict the location of fish schools. For example, the proposed unit can analyze data obtained from a seawater temperature sensor to predict the location of fish schools. The proposed unit can also analyze data obtained from a salinity sensor to predict the location of fish schools. The proposed unit can also analyze plankton distribution data to predict the location of fish schools. This makes it possible to accurately predict the location of fish schools and improve fishing efficiency. Some or all of the above processing in the proposed unit may be performed using AI or not. For example, the proposed unit can input seawater temperature, salinity, and plankton distribution data into a generating AI and have the generating AI perform the prediction of the location of fish schools.

[0073] The forecasting unit can analyze meteorological data and propose the optimal route to fishermen. For example, the forecasting unit can analyze meteorological data such as wind speed, wind direction, and precipitation to propose the optimal route. The forecasting unit can use AI to propose the optimal route based on meteorological data. For example, the forecasting unit can analyze wind speed data and propose the optimal route. The forecasting unit can also analyze wind direction data and propose the optimal route. The forecasting unit can also analyze precipitation data and propose the optimal route. By proposing the optimal route, it is possible to support safe and efficient navigation. Some or all of the above processing in the forecasting unit may be performed using AI or not. For example, the forecasting unit can input meteorological data into a generating AI and have the generating AI propose the optimal route.

[0074] The management department can record catch data in real time and make suggestions to prevent overfishing. For example, the management department can use sensors to record catch data in real time. The management department can use AI to analyze the real-time recorded catch data and make suggestions to prevent overfishing. For example, the management department can analyze the catch data and issue a warning if there is a high risk of overfishing. Based on the catch data, the management department can also propose fishing limits to fishermen. The management department can also analyze the catch data and make suggestions to achieve sustainable fisheries. This will prevent overfishing and achieve sustainable fisheries. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input real-time recorded catch data into a generating AI and have the generating AI execute suggestions to prevent overfishing.

[0075] The management department can propose restrictions on the catch of specific fish species. For example, the management department can propose catch restrictions for protected fish species. The management department can use AI to analyze catch data for specific fish species and propose catch restrictions. For example, the management department can analyze catch volume data for specific fish species and determine the need for catch restrictions. The management department can also make proposals to promote the protection of specific fish species. Based on catch data for specific fish species, the management department can also propose catch restrictions to fishermen. This can promote the protection of specific fish species and support resource management. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input catch data for specific fish species into a generating AI and have the generating AI execute catch restriction proposals.

[0076] The data collection unit can estimate the emotions of fishermen and adjust the timing of data collection based on the estimated emotions. For example, if a fisherman is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden on the fisherman. If a fisherman is relaxed, the data collection unit can also increase the frequency of data collection to provide more detailed information. If a fisherman is in a hurry, the data collection unit can prioritize the collection of only the most important data. This allows the timing of data collection to be adjusted according to the emotions of the fishermen, thereby reducing their burden. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input fishermen's emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0077] The data collection unit can analyze past fishing data and select the optimal data collection method. For example, the data collection unit can identify data to be collected in a specific season from past fishing data. The data collection unit can also optimize the data collection method in a specific sea area based on past fishing data. The data collection unit can also analyze past fishing data and determine the types of data to be collected. This enables efficient data collection by utilizing past data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past fishing data into a generating AI and have the generating AI select the optimal data collection method.

[0078] The data collection unit can filter data based on the fisherman's current fishing activities and areas of interest during data collection. For example, if a fisherman is interested in a particular fish species, the data collection unit will prioritize collecting data related to that species. If a fisherman fishes in a particular sea area, the data collection unit can also prioritize collecting data related to that sea area. The data collection unit can also filter and collect necessary data based on the fisherman's current fishing activities. This allows for the priority collection of data tailored to the fisherman's interests and efficient information provision. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the fisherman's current fishing activities into a generating AI and have the generating AI perform data filtering.

[0079] The data collection unit can estimate the emotions of fishermen and determine the priority of data to collect based on the estimated emotions. For example, if a fisherman is stressed, the data collection unit may prioritize collecting only important data. If a fisherman is relaxed, the data collection unit may also prioritize collecting detailed data. If a fisherman is in a hurry, the data collection unit may also prioritize collecting data that can be collected quickly. This allows for efficient data collection by prioritizing data according to the emotions of the fishermen. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input fishermen's emotion data into a generative AI and have the generative AI determine the priority of the data.

[0080] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of fishermen during data collection. For example, if a fisherman is in a specific sea area, the data collection unit will prioritize the collection of data related to that area. The data collection unit can also collect the most relevant data based on the fisherman's current location. The data collection unit can also analyze the fisherman's movement patterns and collect highly relevant data. This allows the data collection to be based on the fisherman's current location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the fisherman's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0081] The data collection unit can analyze the social media activities of fishermen and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by fishermen on social media. The data collection unit can also analyze the social media activities of fishermen and collect data of interest. The data collection unit can also collect data related to topics mentioned by fishermen on social media. This allows for the collection of relevant data based on the social media activities of fishermen. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input fishermen's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0082] The suggestion unit can estimate the fisherman's emotions and adjust the way the suggestion is presented based on the estimated emotions. For example, if the fisherman is stressed, the suggestion unit will make a simple and easy-to-understand suggestion. If the fisherman is relaxed, the suggestion unit may also make a suggestion that includes detailed information. If the fisherman is in a hurry, the suggestion unit may also make a concise and quick suggestion. This allows the suggestion unit to adjust the way the suggestion is presented according to the fisherman's emotions, making the suggestion easy to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input fisherman's emotion data into a generative AI and have the generative AI adjust the way the suggestion is presented.

[0083] The proposal unit can adjust the level of detail of its proposals based on the importance of the fishing grounds. For example, the proposal unit can provide detailed proposals for important fishing grounds, and concise proposals for less important fishing grounds. The proposal unit can also adjust the level of detail of its proposals in stages according to the importance of the fishing grounds. This allows for proposals with an appropriate level of detail according to the importance of the fishing grounds. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input fishing ground importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0084] The proposal unit can apply different proposal algorithms depending on the fishing ground category when making a proposal. For example, the proposal unit can apply a specific algorithm to coastal fishing grounds. The proposal unit can also apply a different algorithm to deep-sea fishing grounds. The proposal unit can also select the optimal proposal algorithm depending on the fishing ground category. This allows the optimal proposal algorithm to be applied according to the fishing ground category. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input fishing ground category data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0085] The suggestion unit can estimate the fisherman's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the fisherman is stressed, the suggestion unit can make a short, concise suggestion. If the fisherman is relaxed, the suggestion unit can make a longer suggestion with more detailed explanations. If the fisherman is in a hurry, the suggestion unit can make a short, easily understandable suggestion. This allows the length of the suggestion to be adjusted according to the fisherman's emotions, making the suggestion easy to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input fisherman emotion data into a generative AI and have the generative AI adjust the length of the suggestion.

[0086] The proposal unit can determine the priority of proposals based on the timing of data collection for fishing grounds when making a proposal. For example, the proposal unit can prioritize proposals based on recently collected data. The proposal unit can also lower the priority of proposals based on older data. The proposal unit can also adjust the priority of proposals in stages according to the data collection timing. This allows for the provision of high-priority proposals based on the data collection timing. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input data collection timing data into a generating AI and have the generating AI determine the priority of proposals.

[0087] The proposal unit can adjust the order of proposals based on the relevance of the fishing grounds. For example, the proposal unit may prioritize proposing fishing grounds that are most relevant to the fishermen's current fishing activities. The proposal unit may also prioritize proposing highly relevant fishing grounds based on the fishermen's interests. The proposal unit may also adjust the order of proposals in stages according to the relevance of the fishing grounds. This allows for proposals to be made in the optimal order according to the relevance of the fishing grounds. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input fishing ground relevance data into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0088] The prediction unit can estimate the emotions of fishermen and adjust the display method of the prediction based on the estimated emotions of the fishermen. For example, if a fisherman is tense, the prediction unit provides a simple and highly visible display method. If a fisherman is relaxed, the prediction unit can also provide a display method that includes detailed information. If a fisherman is in a hurry, the prediction unit can also provide a concise display method. This allows the display method of the prediction to be adjusted according to the emotions of the fishermen, improving visibility. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input fisherman emotion data into a generative AI and have the generative AI adjust the display method of the prediction.

[0089] The forecasting unit can optimize the current forecast by referring to past weather data during the forecasting process. For example, the forecasting unit optimizes the current weather forecast based on past weather data. The forecasting unit can also improve the accuracy of ocean condition forecasts by referring to past weather data. The forecasting unit can also analyze past weather data and reflect it in the current forecast. This allows for improved accuracy of the current forecast by utilizing past weather data. Some or all of the above-described processes in the forecasting unit may be performed using AI or not. For example, the forecasting unit can input past weather data into a generating AI and have the generating AI perform the optimization of the current forecast.

[0090] The prediction unit can apply different prediction algorithms to each fishing ground category during prediction. For example, the prediction unit can apply a specific prediction algorithm to coastal fishing grounds. It can also apply a different prediction algorithm to deep-sea fishing grounds. The prediction unit can also select the optimal prediction algorithm according to the fishing ground category. This allows the optimal prediction algorithm to be applied according to the fishing ground category. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input fishing ground category data into a generating AI and have the generating AI execute the application of the prediction algorithm.

[0091] The prediction unit can estimate the emotions of fishermen and adjust the importance of predictions based on the estimated emotions. For example, if a fisherman is stressed, the prediction unit will prioritize displaying only important predictions. If a fisherman is relaxed, the prediction unit can also display detailed prediction information. If a fisherman is in a hurry, the prediction unit can also display prediction information that can be quickly understood. This allows the importance of predictions to be adjusted according to the emotions of the fishermen, and important information to be prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input fisherman emotion data into a generative AI and have the generative AI adjust the importance of predictions.

[0092] The forecasting unit can improve the accuracy of its forecasts based on the timing of meteorological data collection. For example, the forecasting unit can improve the accuracy of its forecasts based on recently collected meteorological data. The forecasting unit can also reduce the accuracy of forecasts based on older meteorological data. The forecasting unit can also adjust the accuracy of its forecasts in stages according to the timing of meteorological data collection. This allows for improved forecast accuracy based on the timing of meteorological data collection. Some or all of the above-described processes in the forecasting unit may be performed using AI or not. For example, the forecasting unit can input meteorological data collection timing data into a generating AI and have the generating AI perform the improvement of forecast accuracy.

[0093] The prediction unit can improve the accuracy of its predictions by referring to relevant oceanographic data during the prediction process. For example, the prediction unit can improve the accuracy of weather forecasts based on oceanographic data. The prediction unit can also improve the accuracy of ocean condition forecasts by referring to oceanographic data. The prediction unit can also improve the accuracy of its predictions by analyzing relevant oceanographic data. This allows for improved prediction accuracy by utilizing relevant oceanographic data. Some or all of the above-described processes in the prediction unit may be performed using AI or not. For example, the prediction unit can input relevant oceanographic data into a generating AI and have the generating AI perform the task of improving the accuracy of the predictions.

[0094] The management department can estimate the emotions of fishermen and adjust management methods based on the estimated emotions. For example, if a fisherman is stressed, the management department can suggest a simple management method. If a fisherman is relaxed, the management department can also suggest a detailed management method. If a fisherman is in a hurry, the management department can also suggest a management method that can be implemented quickly. This allows for efficient management by adjusting management methods according to the emotions of the fishermen. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input fishermen's emotion data into a generative AI and have the generative AI adjust management methods.

[0095] The management department can analyze past catch data to select the optimal management method during management. For example, the management department can select a method to manage the catch volume of a specific fish species based on past catch data. The management department can also analyze past catch data and propose management methods that respond to fluctuations in catch volume. The management department can also refer to past catch data to select management methods that will enable sustainable fisheries. In this way, the optimal management method can be selected by utilizing past catch data. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input past catch data into a generating AI and have the generating AI select the optimal management method.

[0096] The management unit can customize management methods based on the fisherman's current fishing activities during management. For example, if a fisherman targets a specific fish species, the management unit will propose management methods specific to that species. The management unit can also customize the optimal management methods based on the fisherman's current fishing activities. The management unit can also flexibly adjust the management methods according to the fisherman's fishing activity status. This allows the management unit to provide the optimal management methods according to the fisherman's current fishing activities. Some or all of the above processes in the management unit may be performed using AI or not. For example, the management unit can input the fisherman's current fishing activity data into a generating AI and have the generating AI perform the customization of the management methods.

[0097] The management department can estimate the emotions of fishermen and determine management priorities based on the estimated emotions. For example, if a fisherman is stressed, the management department can prioritize only the most important management items. If a fisherman is relaxed, the management department can also prioritize detailed management items. If a fisherman is in a hurry, the management department can also prioritize management items that can be executed quickly. This allows for the determination of management priorities according to the emotions of fishermen, and the prioritization of important management items. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input fisherman emotion data into a generative AI and have the generative AI determine management priorities.

[0098] The management department can select the optimal management method during management, taking into account the geographical location information of fishermen. For example, if a fisherman is in a specific sea area, the management department will select a management method suitable for that area. The management department can also select the most effective management method based on the fisherman's current location. The management department can also analyze the fisherman's movement patterns and select the optimal management method. This allows the management department to select the optimal management method based on the fisherman's geographical location information. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input the fisherman's geographical location information into a generating AI and have the generating AI select the optimal management method.

[0099] The management department can analyze the social media activities of fishermen and propose management measures during the management process. For example, the management department can propose relevant management measures based on information shared by fishermen on social media. The management department can also analyze the social media activities of fishermen and propose management measures of interest. The management department can also propose management measures related to topics mentioned by fishermen on social media. This allows the management department to propose relevant management measures based on the social media activities of fishermen. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input fishermen's social media activity data into a generating AI and have the generating AI execute the proposal of management measures.

[0100] The communication support unit can estimate the emotions of fishermen and adjust its communication methods based on those estimated emotions. For example, if a fisherman is stressed, the communication support unit can provide a simple and easy-to-understand communication method. If a fisherman is relaxed, the communication support unit can also provide a communication method that includes detailed information. If a fisherman is in a hurry, the communication support unit can also provide a communication method that can be quickly understood. This allows for adjustment of communication methods according to the fisherman's emotions, enabling effective information sharing. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication support unit may be performed using AI or not. For example, the communication support unit can input fisherman emotion data into a generative AI and have the generative AI adjust the communication method.

[0101] The Communication Support Department can select the optimal support method by referring to past communication history when providing communication support. For example, the Communication Support Department can select the optimal support method based on past communication history. The Communication Support Department can also analyze past communication history and propose effective support methods. The Communication Support Department can also select a support method suitable for fishermen by referring to past communication history. In this way, the optimal support method can be selected by utilizing past communication history. Some or all of the above processes in the Communication Support Department may be performed using AI or not. For example, the Communication Support Department can input past communication history data into a generating AI and have the generating AI perform the selection of the optimal support method.

[0102] The communication support unit can estimate the emotions of fishermen and determine communication priorities based on the estimated emotions. For example, if a fisherman is stressed, the communication support unit can prioritize conveying only important information. If a fisherman is relaxed, the communication support unit can also prioritize conveying detailed information. If a fisherman is in a hurry, the communication support unit can also prioritize conveying information that can be quickly understood. This allows the communication priorities to be determined according to the fisherman's emotions, and important information to be conveyed preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication support unit may be performed using AI or not. For example, the communication support unit can input fisherman emotion data into a generative AI and have the generative AI determine the communication priorities.

[0103] The communication support unit can select the optimal support method when providing communication support, taking into account the fisherman's device information. For example, if the fisherman is using a smartphone, the communication support unit can provide a support method that is adapted to the screen size. If the fisherman is using a tablet, the communication support unit can also provide a support method optimized for a larger screen. If the fisherman is using a smartwatch, the communication support unit can also provide a concise and highly visible support method. This allows the unit to provide the optimal support method based on the fisherman's device information. Some or all of the above processing in the communication support unit may be performed using AI or not. For example, the communication support unit can input the fisherman's device information into a generating AI and have the generating AI select the optimal support method.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The proposal system can estimate the emotions of fishermen and adjust the timing of proposals based on those estimates. For example, if a fisherman is stressed, the frequency of proposals can be reduced to lessen their burden. If a fisherman is relaxed, the frequency of proposals can be increased, and more detailed information can be provided. If a fisherman is in a hurry, only important proposals can be prioritized. This allows for efficient information delivery by adjusting the timing of proposals according to the fisherman's emotions.

[0106] The management department can estimate the emotions of fishermen and adjust management methods based on those estimates. For example, if a fisherman is stressed, a simple management method can be suggested. If a fisherman is relaxed, a more detailed management method can be suggested. If a fisherman is in a hurry, a management method that can be implemented quickly can be suggested. This allows for efficient management by adjusting management methods according to the emotions of the fishermen.

[0107] The prediction unit can estimate the fisherman's emotions and adjust the display method of the prediction based on the estimated emotions. For example, if the fisherman is tense, a simple and highly visible display method can be provided. If the fisherman is relaxed, a display method including detailed information can be provided. If the fisherman is in a hurry, a display method that gets straight to the point can be provided. This allows the display method of the prediction to be adjusted according to the fisherman's emotions, improving visibility.

[0108] The Communication Support Department can estimate the emotions of fishermen and adjust communication methods based on those estimates. For example, if a fisherman is stressed, it can provide a simple and easy-to-understand communication method. If a fisherman is relaxed, it can provide a communication method that includes detailed information. If a fisherman is in a hurry, it can provide a communication method that can be quickly understood. This allows for effective information sharing by adjusting communication methods according to the fisherman's emotions.

[0109] The proposal team can estimate the emotions of fishermen and adjust the way the proposal is presented based on those estimates. For example, if a fisherman is stressed, a simple and easy-to-understand proposal can be made. If the fisherman is relaxed, a proposal with more detailed information can be made. If the fisherman is in a hurry, a concise and quick proposal can be made. This allows the proposal to be presented in a way that is easy to understand, tailored to the fisherman's emotions.

[0110] The data collection unit can prioritize data collection based on the fishermen's current fishing activities and areas of interest. For example, if a fisherman is interested in a particular fish species, data related to that species will be collected preferentially. If a fisherman fishes in a specific sea area, data related to that area can also be collected preferentially. This allows for the collection of data tailored to the fishermen's interests and enables efficient information provision.

[0111] The management department can analyze past fishing data and select the optimal management method. For example, it can select a method for managing the catch volume of a specific fish species based on past fishing data. It can also analyze past fishing data and propose management methods that respond to fluctuations in catch volume. This allows for the selection of the optimal management method by utilizing past fishing data.

[0112] The forecasting unit can optimize current forecasts by referring to past weather data. For example, it optimizes current weather forecasts based on past weather data. It can also improve the accuracy of ocean condition forecasts by referring to past weather data. In this way, the accuracy of current forecasts can be improved by utilizing past weather data.

[0113] The proposal function can apply different proposal algorithms depending on the fishing ground category. For example, a specific algorithm can be applied to coastal fishing grounds, while a different algorithm can be applied to deep-sea fishing grounds. This allows for the application of the most suitable proposal algorithm for each fishing ground category.

[0114] The Communication Support Department can select the optimal support method by considering the fisherman's device information. For example, if a fisherman is using a smartphone, it can provide a support method adapted to the screen size. If a fisherman is using a tablet, it can also provide a support method optimized for the larger screen. This allows the department to provide the most suitable support method based on the fisherman's device information.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The collection unit collects information from satellite data and ocean sensors. For example, it can collect data from weather satellites, ocean observation satellites, water temperature sensors, salinity sensors, and so on. Step 2: The proposal unit analyzes the information collected by the collection unit and proposes the optimal fishing grounds. For example, it analyzes data such as seawater temperature, salinity, and plankton distribution, uses AI to predict the location of fish schools, and proposes the optimal fishing grounds. Step 3: The forecasting unit integrates weather forecasts, ocean currents, and tidal information to support safe and efficient navigation. For example, it analyzes weather data and uses AI to suggest optimal routes to fishermen. Step 4: The management department automatically records and analyzes catch data to support resource management and regulatory compliance. For example, it records catch amounts in real time and makes suggestions to prevent overfishing. It uses AI to analyze catch data to support resource management and regulatory compliance.

[0117] 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.

[0118] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the data collection unit, proposal unit, forecasting unit, management unit, and communication support unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects ocean data using the camera 42 and sensors of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal fishing grounds based on the collected data. The forecasting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports navigation by integrating weather forecasts, ocean currents, and tidal information. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and records and analyzes catch data to support resource management and regulatory compliance. The communication support unit is implemented by, for example, the control unit 46A of the smart device 14 and facilitates information sharing with other fishermen and onshore staff. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0124] 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.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0126] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] 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.

[0128] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the collection unit, proposal unit, forecast unit, management unit, and communication support unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects ocean data using the camera 42 and sensors of the smart glasses 214 and analyzes it by the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal fishing grounds based on the collected data. The forecast unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and supports navigation by integrating weather forecasts, ocean currents, and tidal information. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and records and analyzes catch data to support resource management and regulatory compliance. The communication support unit is implemented, for example, by the control unit 46A of the smart glasses 214 and facilitates information sharing with other fishermen and shore staff. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0140] 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.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0142] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] 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.

[0144] 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.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the data collection unit, proposal unit, forecasting unit, management unit, and communication support unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects ocean data using the camera 42 and sensors of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal fishing grounds based on the collected data. The forecasting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports navigation by integrating weather forecasts, ocean currents, and tidal information. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and records and analyzes catch data to support resource management and regulatory compliance. The communication support unit is implemented by, for example, the control unit 46A of the headset terminal 314 and facilitates information sharing with other fishermen and shore staff. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0156] 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.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0158] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] 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.

[0160] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] 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.

[0162] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the collection unit, proposal unit, forecast unit, management unit, and communication support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects ocean data using the camera 42 and sensors of the robot 414 and analyzes it by the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal fishing grounds based on the collected data. The forecast unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports navigation by integrating weather forecasts, ocean currents, and tidal information. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and records and analyzes catch data to support resource management and regulatory compliance. The communication support unit is implemented by, for example, the control unit 46A of the robot 414 and facilitates information sharing with other fishermen and land-based staff. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0170] 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.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] 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.

[0173] 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.

[0174] 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, and motorcycles, 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 based, for example, 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.

[0175] 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."

[0176] 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.

[0177] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] 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 other things 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.

[0187] 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.

[0188] (Note 1) The collection unit collects information from satellite data and ocean sensors, The collection unit analyzes the information collected and proposes the optimal fishing grounds, The forecasting unit integrates weather forecasts, ocean currents, and tidal information to support safe and efficient navigation. The management department automatically records and analyzes catch data to support resource management and regulatory compliance, Equipped with A system characterized by the following features. (Note 2) It includes a communication support department to facilitate information sharing with other fishermen and shore staff. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, By analyzing data such as seawater temperature, salinity, and plankton distribution, the location of fish schools can be predicted. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, We analyze weather data and propose optimal routes to fishermen. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, We record catch amounts in real time and make suggestions to prevent overfishing. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, I propose restricting the fishing of specific fish species. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the sentiment of fishermen and adjust the timing of data collection based on the estimated sentiment of the fishermen. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past fishing data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the fishermen's current fishing activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate the sentiments of fishermen and prioritize the data to collect based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the geographical location information of fishermen is taken into consideration to prioritize the collection of highly relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, analyze the social media activity of fishermen and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, We estimate the feelings of the fishermen and adjust the way the proposal is presented based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the fishing grounds. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the fishing ground category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, Estimate the sentiment of the fishermen and adjust the length of the proposal based on the estimated sentiment of the fishermen. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making proposals, prioritize them based on the timing of data collection for fishing grounds. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the fishing grounds. The system described in Appendix 1, characterized by the features described herein. (Note 19) The prediction unit, The system estimates the sentiment of fishermen and adjusts the display method of predictions based on the estimated sentiment of the fishermen. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, When making a forecast, we optimize the current forecast by referring to past weather data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, When making predictions, different prediction algorithms are applied for each category of fishing ground. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, We estimate the sentiment of fishermen and adjust the importance of predictions based on the estimated sentiment of fishermen. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, When making predictions, improve the accuracy of the forecast based on when the weather data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, When making predictions, we refer to relevant ocean data to improve the accuracy of the predictions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, Estimate the sentiments of fishermen and adjust management methods based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, During management, past catch data is analyzed to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, During management, customize management methods based on the fishermen's current fishing activities. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, Estimate the sentiments of fishermen and determine management priorities based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, During management, the optimal management method will be selected considering the geographical location information of the fishermen. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, During management, we analyze the social media activities of fishermen and propose management strategies. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned Communication Support Department Estimate the emotions of fishermen and adjust communication methods based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned Communication Support Department When providing communication support, the most suitable support method is selected by referring to past communication history. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned Communication Support Department The system estimates the emotions of fishermen and determines communication priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned Communication Support Department When providing communication support, the optimal support method is selected by considering the fishermen's device information. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection unit collects information from satellite data and ocean sensors, The collection unit analyzes the information collected and proposes the optimal fishing grounds, The forecasting unit integrates weather forecasts, ocean currents, and tidal information to support safe and efficient navigation. The management department automatically records and analyzes catch data to support resource management and regulatory compliance, Equipped with A system characterized by the following features.

2. It includes a communication support department to facilitate information sharing with other fishermen and shore staff. The system according to feature 1.

3. The aforementioned proposal section is, By analyzing data such as seawater temperature, salinity, and plankton distribution, the location of fish schools can be predicted. The system according to feature 1.

4. The prediction unit, We analyze weather data and propose optimal routes to fishermen. The system according to feature 1.

5. The aforementioned management department, We record catch amounts in real time and make suggestions to prevent overfishing. The system according to feature 1.

6. The aforementioned management department, I propose restricting the fishing of specific fish species. The system according to feature 1.

7. The aforementioned collection unit is We estimate the sentiment of fishermen and adjust the timing of data collection based on the estimated sentiment of the fishermen. The system according to feature 1.

8. The aforementioned collection unit is Analyze past fishing data and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is During data collection, filtering is performed based on the fishermen's current fishing activities and areas of interest. The system according to feature 1.

Images