System

Abstract

An object of the system according to the embodiment is to learn the experience and intuition of fishermen and propose an optimal fishing ground and fishing method.SOLUTION: A system includes an experience learning unit, a proposal unit, and a management unit. The experience learning unit learns the experience and intuition of the fisherman. The proposal unit proposes an optimum fishing ground or fishing method based on the data learned by the empirical learning unit. The management unit manages the aquaculture industry.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to efficiently utilize the experience and intuition of fishermen and propose optimal fishing grounds and fishing methods.

[0005] The system according to the embodiment aims to learn the experience and intuition of fishermen and propose optimal fishing grounds and fishing methods. [Means for solving the problem]

[0006] The system according to the embodiment includes an experiential learning unit, a proposal unit, and a management unit. The experiential learning unit learns the experience and intuition of fishermen. The proposal unit proposes optimal fishing grounds or fishing methods based on the data learned by the experiential learning unit. The management unit manages the aquaculture business. [Effects of the Invention]

[0007] The system according to the embodiment can learn the experience and intuition of fishermen and suggest optimal fishing grounds and fishing methods. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The fisheries efficiency system according to an embodiment of the present invention is a system that uses AI to learn the experience and intuition of fishermen, proposes optimal fishing grounds and fishing methods, and manages aquaculture. As a result, the fisheries efficiency system can improve the efficiency of the fisheries industry and reduce the number of workers, and also contribute to creating jobs in depopulated areas.

[0029] A fishing efficiency improvement system according to an embodiment includes an experiential learning unit, a proposal unit, and a management unit. The experiential learning unit learns the experience and intuition of fishermen. For example, data is collected on the conditions under which fishermen select fishing grounds and the fishing methods they use, and the generation AI learns from this data. The experiential learning unit also inputs prompts containing specific instructions based on the fishermen's experience and intuition into the generation AI, and the generation AI learns to suggest optimal fishing grounds and fishing methods based on the prompts. The proposal unit proposes optimal fishing grounds and fishing methods based on the data learned by the experiential learning unit. For example, the generation AI analyzes data on weather, ocean currents, fish ecology, etc. to identify fishing grounds that are most suitable for the season and conditions. The generation AI also proposes optimal fishing methods to maximize catches. The management unit manages aquaculture. For example, the generation AI monitors water quality, feed amount, fish health, etc., and proposes optimal management methods. The generation AI proposes optimal management methods based on data on water quality, feed amount, and fish health. As a result, the fisheries efficiency improvement system according to the embodiment can improve the efficiency of the fisheries industry and reduce the number of workers, and can also contribute to creating jobs in depopulated areas.

[0030] The experiential learning unit recreates the fisherman's experience in a 3D simulation environment, allowing the generating AI to learn through fishing activities in a virtual environment. For example, the experiential learning unit creates a 3D simulation environment based on the fisherman's experience, and the generating AI simulates fishing activities in that environment. For example, it selects fishing grounds and practices fishing methods in a virtual ocean. The experiential learning unit also recreates the fisherman's actions and decisions in the 3D simulation environment, allowing the generating AI to learn based on that data. For example, it recreates the fisherman's movement patterns and decision-making criteria in a simulation. The experiential learning unit also allows the generating AI to learn the fisherman's experience and intuition through fishing activities in a virtual environment. For example, it simulates the selection of fishing grounds and the effectiveness of fishing methods under different conditions. This allows the generating AI to efficiently learn the fisherman's experience and intuition through fishing activities in a virtual environment.

[0031] The experience learning unit collects fishermen's experiences as voice data, and the generation AI can learn from them using voice recognition technology. For example, the experience learning unit collects instructions based on fishermen's experience and intuition as voice data, and the generation AI learns from the data using voice recognition technology. For example, the fishermen's verbal reasons for selecting fishing grounds are saved as voice data. The experience learning unit also collects voice instructions given by fishermen during fishing activities in real time, and the generation AI analyzes and learns from the data. For example, the fishermen's voice instructions are converted into text data and input into the generation AI. The experience learning unit also records fishermen's experiences as voice data, and the generation AI analyzes and learns from the data using voice recognition technology. For example, the generation AI learns fishing ground selection and fishing method patterns based on the fishermen's voice instructions. This allows the generation AI to efficiently learn fishermen's experience and intuition using voice data.

[0032] The proposal unit can analyze satellite data and identify optimal fishing grounds in real time. The proposal unit, for example, analyzes satellite data, and the generation AI identifies optimal fishing grounds in real time. For example, fishing grounds are selected based on ocean temperature and current data. The proposal unit also has the generation AI identify optimal fishing grounds based on satellite data and provide that information to fishermen. For example, it analyzes satellite images to identify the location of schools of fish. The proposal unit also builds a system that analyzes satellite data in real time, and the generation AI identifies optimal fishing grounds. For example, it predicts changes in fishing grounds based on satellite data. This makes it possible to identify optimal fishing grounds in real time using satellite data.

[0033] The proposal unit can simulate marine ecosystems and predict long-term fluctuations in fishing grounds. In the proposal unit, for example, the generation AI simulates marine ecosystems and predicts long-term fluctuations in fishing grounds. For example, it simulates changes in ocean temperature and ocean currents. The proposal unit also predicts long-term fluctuations in fishing grounds based on the marine ecosystem simulation using the generation AI, and provides this information to fishermen. For example, it predicts the migration patterns of fish schools. The proposal unit also builds a system in which the generation AI simulates marine ecosystems and predicts long-term fluctuations in fishing grounds. For example, it predicts fluctuations in fishing grounds based on changes in the marine environment. This makes it possible to predict long-term fluctuations in fishing grounds and achieve sustainable fishing.

[0034] The management unit can use an underwater robot to monitor the status of the aquaculture farm and propose management methods in real time. For example, the management unit can use an underwater robot to monitor the status of the aquaculture farm in real time, and the generation AI can propose the optimal management method based on that data. For example, it can adjust the water quality and amount of feed. The management unit can also build a system in which the management unit uses an underwater robot to monitor the status of the aquaculture farm and the generation AI can propose the optimal management method based on that data. For example, it can analyze data collected by the underwater robot in real time. The management unit can also monitor the status of the aquaculture farm using an underwater robot and the generation AI can propose the optimal management method based on that data. For example, it can adjust the aquaculture farm environment based on footage taken by the underwater robot. This makes it possible to use an underwater robot to monitor the status of the aquaculture farm in real time and propose the optimal management method.

[0035] The management unit can analyze the growth data of farmed fish and propose the optimal feed allocation based on the growth data. For example, the management unit analyzes the growth data of farmed fish and the generation AI proposes the optimal feed allocation. For example, it adjusts the amount and type of feed according to the growth rate. The management unit also proposes the optimal feed allocation based on the growth data of farmed fish and provides this information to the fish farmer. For example, it proposes feed allocation according to the growth stage. The management unit also builds a system that analyzes the growth data of farmed fish in real time and the generation AI proposes the optimal feed allocation. For example, it automatically adjusts feed allocation based on the growth data. This makes it possible to propose the optimal feed allocation based on the growth data of farmed fish.

[0036] The management department can integrate data from different farmers and propose optimal aquaculture management methods based on the data. For example, the management department collects data from different farmers, and the generation AI integrates that data to propose optimal aquaculture management methods. For example, it proposes optimal management methods based on data from different farms. The management department can also integrate data from a variety of farmers, and the generation AI proposes optimal aquaculture management methods based on that data. For example, it proposes management methods based on success stories from different farmers. The management department can also build a system that integrates data from different farmers, and the generation AI proposes optimal aquaculture management methods. For example, it proposes optimal management methods based on data from different farms. This makes it possible to integrate data from different farmers and propose optimal aquaculture management methods.

[0037] The management unit can analyze the environmental data of the aquaculture farm and propose the optimal aquaculture environment based on the environmental data. For example, the management unit analyzes the environmental data of the aquaculture farm and the generation AI proposes the optimal aquaculture environment. For example, the environment is adjusted based on data such as water quality, temperature, and oxygen concentration. The management unit also proposes the optimal aquaculture environment based on the environmental data of the aquaculture farm and provides this information to the aquaculture farmer. For example, it proposes the optimal water quality management method based on the environmental data. The management unit also builds a system that analyzes the environmental data of the aquaculture farm in real time and the generation AI proposes the optimal aquaculture environment. For example, it automatically adjusts the aquaculture farm environment based on the environmental data. This makes it possible to propose the optimal aquaculture environment based on the environmental data of the aquaculture farm.

[0038] The proposal unit can maximize the catch by proposing combinations of different fishing methods and implementing multiple fishing methods simultaneously. For example, the proposal unit uses a generation AI to propose combinations of different fishing methods and maximize the catch by implementing multiple fishing methods simultaneously. For example, combining net fishing and angling. The proposal unit also uses a generation AI to propose the optimal fishing method based on the combination of different fishing methods and provides this information to fishermen. For example, combining trawl fishing and fixed net fishing. The proposal unit also builds a system in which the generation AI proposes combinations of different fishing methods and implements multiple fishing methods simultaneously to maximize the catch. For example, it simulates combinations of fishing methods. This allows different combinations of fishing methods to be proposed and the catch to be maximized.

[0039] In selecting fishing grounds, the proposal unit can use a drone to monitor ocean conditions in real time and identify the optimal fishing grounds. For example, the proposal unit uses a drone to monitor ocean conditions in real time, and the generation AI identifies the optimal fishing grounds based on that data. For example, the proposal unit analyzes ocean images taken by the drone. The proposal unit also builds a system in which the proposal unit uses a drone to monitor ocean conditions and the generation AI identifies the optimal fishing grounds based on that data. For example, the proposal unit analyzes data collected by the drone in real time. The proposal unit also uses a drone to monitor ocean conditions and the generation AI identifies the optimal fishing grounds based on that data. For example, the proposal unit selects fishing grounds based on ocean images taken by the drone. This makes it possible to use a drone to monitor ocean conditions in real time and identify the optimal fishing grounds.

[0040] The proposal unit can analyze the data of a fishing organization and propose optimal resource allocation based on the data. For example, the proposal unit analyzes the data of a fishing organization and the generation AI proposes optimal resource allocation. For example, it proposes efficient allocation of fishing vessels and fishing gear. The proposal unit also proposes optimal resource allocation based on the data of a fishing organization and provides that information to the organization. For example, it optimizes fishing vessel operation schedules and fishing gear allocation. The proposal unit also builds a system that analyzes the data of a fishing organization in real time and the generation AI proposes optimal resource allocation. For example, it performs data analysis to improve the efficiency of resource allocation. This makes it possible to propose optimal resource allocation based on the data of a fishing organization.

[0041] The proposal unit can simulate the business processes of a fishing organization and make proposals for improving efficiency. For example, the proposal unit simulates the business processes of a fishing organization, and the generation AI makes proposals for improving efficiency. For example, the proposal unit proposes optimizing the business flow. The proposal unit also makes proposals for improving efficiency based on the business process simulation by the generation AI, and provides this information to the organization. For example, the proposal unit makes proposals for reducing waste in work. The proposal unit also builds a system that simulates business processes in real time, and the generation AI makes proposals for improving efficiency. For example, it identifies areas for improvement in the business processes and makes proposals. This makes it possible to simulate the business processes of a fishing organization and make proposals for improving efficiency.

[0042] The proposal unit can integrate data from different fishing organizations and propose an optimal operation schedule based on the data. For example, the proposal unit collects data from different fishing organizations, and the generation AI integrates the data to propose an optimal operation schedule. For example, the operation schedule is optimized based on data from multiple fishing organizations. The proposal unit also integrates data from a variety of fishing organizations, and the generation AI proposes an optimal operation schedule based on the data. For example, the operation schedules of different fishing organizations are adjusted. The proposal unit also builds a system that integrates data from different fishing organizations, and the generation AI proposes an optimal operation schedule. For example, the operation schedule is optimized based on data from multiple fishing organizations. This makes it possible to integrate data from different fishing organizations and propose an optimal operation schedule.

[0043] The proposal unit can analyze the data of the fishing organization and propose an optimal sales plan based on the data. For example, the proposal unit analyzes the data of the fishing organization and the generation AI proposes an optimal sales plan. For example, a sales plan is created based on a demand forecast for the catch. The proposal unit also proposes an optimal sales plan based on the data of the fishing organization and provides this information to the organization. For example, a sales plan is created based on price trends for the catch. The proposal unit also builds a system that analyzes the data of the fishing organization in real time and the generation AI proposes an optimal sales plan. For example, it performs data analysis to improve the efficiency of the sales plan. This makes it possible to propose an optimal sales plan based on the data of the fishing organization.

[0044] The proposal unit can analyze economic data for depopulated areas and propose optimal job creation plans based on the data. For example, the proposal unit analyzes economic data for depopulated areas, and the generation AI proposes optimal job creation plans. For example, job opportunities are created based on the industrial structure and economic situation of the region. The proposal unit also proposes optimal job creation plans based on the economic data for depopulated areas using the generation AI, and provides this information to the region. For example, it proposes new business opportunities based on the economic data for the region. The proposal unit also builds a system that analyzes economic data for depopulated areas in real time, and the generation AI proposes optimal job creation plans. For example, it automatically generates job opportunities based on the economic data. This makes it possible to propose optimal job creation plans based on the economic data for depopulated areas.

[0045] The proposal unit can analyze the skill data of residents in depopulated areas and propose the optimal vocational training program based on the data. For example, the proposal unit analyzes the skill data of residents in depopulated areas and the generation AI proposes the optimal vocational training program. For example, the proposal unit proposes a training program based on the residents' skill set. The proposal unit also proposes the optimal vocational training program based on the skill data of residents in depopulated areas and provides this information to the region. For example, the proposal unit proposes a new training program based on the residents' skill data. The proposal unit also builds a system that analyzes the skill data of residents in depopulated areas in real time and the generation AI proposes the optimal vocational training program. For example, the proposal unit automatically generates a training program based on the skill data. This makes it possible to propose the optimal vocational training program based on the skill data of residents in depopulated areas.

[0046] The proposal unit can integrate data from different depopulated areas and propose an optimal job creation plan based on the data. For example, the proposal unit collects data from different depopulated areas, and the generation AI integrates the data to propose an optimal job creation plan. For example, job opportunities are created based on data from multiple depopulated areas. The proposal unit also integrates data from a variety of depopulated areas, and the generation AI proposes an optimal job creation plan based on the data. For example, a job creation plan is proposed based on success stories from different depopulated areas. The proposal unit also builds a system that integrates data from different depopulated areas, and the generation AI proposes an optimal job creation plan. For example, job opportunities are created based on data from multiple depopulated areas. This makes it possible to integrate data from different depopulated areas and propose an optimal job creation plan.

[0047] The proposal unit can analyze data on residents of depopulated areas and propose optimal regional revitalization plans based on the data. For example, the proposal unit analyzes data on residents of depopulated areas, and the generation AI proposes optimal regional revitalization plans. For example, the generation AI proposes regional revitalization plans based on the skills and interests of residents. The proposal unit also proposes optimal regional revitalization plans based on data on residents of depopulated areas, and provides this information to the region. For example, the proposal unit proposes new regional revitalization plans based on resident data. The proposal unit also builds a system that analyzes data on residents of depopulated areas in real time, and the generation AI proposes optimal regional revitalization plans. For example, the generation AI automatically generates regional revitalization plans based on resident data. This makes it possible to propose optimal regional revitalization plans based on data on residents of depopulated areas.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The proposal unit can analyze satellite data and identify optimal fishing grounds in real time. For example, satellite data is analyzed, and the generation AI identifies optimal fishing grounds in real time. For example, fishing grounds are selected based on ocean temperature and current data. The proposal unit also uses satellite data to identify optimal fishing grounds with the generation AI, and provides that information to fishermen. For example, satellite images are analyzed to identify the location of schools of fish. The proposal unit also builds a system that analyzes satellite data in real time, and the generation AI identifies optimal fishing grounds. For example, fluctuations in fishing grounds are predicted based on satellite data. This makes it possible to identify optimal fishing grounds in real time using satellite data.

[0050] The proposal unit can simulate marine ecosystems and predict long-term fluctuations in fishing grounds. For example, the generation AI simulates marine ecosystems and predicts long-term fluctuations in fishing grounds. For example, it simulates changes in ocean temperature and ocean currents. The proposal unit also uses the generation AI to predict long-term fluctuations in fishing grounds based on the marine ecosystem simulation and provides this information to fishermen. For example, it predicts the migration patterns of fish schools. The proposal unit also builds a system in which the generation AI simulates marine ecosystems and predicts long-term fluctuations in fishing grounds. For example, it predicts fluctuations in fishing grounds based on changes in the ocean environment. This makes it possible to predict long-term fluctuations in fishing grounds and achieve sustainable fishing.

[0051] The management department can use underwater robots to monitor the status of the aquaculture farm in real time and propose optimal management methods. For example, an underwater robot can be used to monitor the status of the aquaculture farm in real time, and the generation AI can propose optimal management methods based on that data. For example, adjusting the water quality and amount of feed. The management department can also build a system in which an underwater robot can be used to monitor the status of the aquaculture farm, and the generation AI can propose optimal management methods based on that data. For example, the data collected by the underwater robot can be analyzed in real time. The management department can also monitor the status of the aquaculture farm using underwater robots, and the generation AI can propose optimal management methods based on that data. For example, adjusting the aquaculture farm environment based on footage taken by the underwater robot. This makes it possible to use underwater robots to monitor the status of the aquaculture farm in real time and propose optimal management methods.

[0052] The management department can analyze the growth data of farmed fish and propose the optimal feed distribution based on the growth data. For example, the growth data of farmed fish is analyzed and the generation AI proposes the optimal feed distribution. For example, the amount and type of feed is adjusted according to the growth rate. The management department also proposes the optimal feed distribution based on the growth data of farmed fish and provides this information to the farmer. For example, it proposes feed distribution according to the growth stage. The management department also builds a system that analyzes the growth data of farmed fish in real time and the generation AI proposes the optimal feed distribution. For example, it automatically adjusts feed distribution based on the growth data. This makes it possible to propose the optimal feed distribution based on the growth data of farmed fish.

[0053] The proposal unit can analyze the data of a fishing organization and propose optimal resource allocation based on the data. For example, the data of a fishing organization is analyzed and the generation AI proposes optimal resource allocation. For example, it proposes efficient allocation of fishing vessels and fishing gear. The proposal unit also proposes optimal resource allocation based on the data of a fishing organization and provides that information to the organization. For example, it optimizes fishing vessel operation schedules and fishing gear allocation. The proposal unit also builds a system that analyzes the data of a fishing organization in real time and the generation AI proposes optimal resource allocation. For example, it performs data analysis to improve the efficiency of resource allocation. This makes it possible to propose optimal resource allocation based on the data of a fishing organization.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The experience learning unit learns the experience and intuition of fishermen. For example, data is collected on the conditions under which fishermen choose which fishing grounds and fishing methods they use, and this data is then used by the generation AI to learn from it. The experience learning unit also inputs prompts containing specific instructions based on the fishermen's experience and intuition into the generation AI, and the generation AI learns to suggest optimal fishing grounds and fishing methods based on these prompts. Step 2: The proposal unit proposes optimal fishing grounds and fishing methods based on the data learned by the experience learning unit. For example, the generation AI analyzes data on weather, ocean currents, fish ecology, etc. to identify the fishing grounds that are most suitable for the time and conditions. The generation AI also aims to maximize the catch by proposing the optimal fishing method. Step 3: The management department manages the aquaculture. For example, the generation AI monitors the water quality, feed amount, and fish health, and proposes the optimal management method. The generation AI proposes the optimal management method based on data on water quality, feed amount, and fish health.

[0056] (Example 2) The fisheries efficiency system according to an embodiment of the present invention is a system that uses AI to learn the experience and intuition of fishermen, proposes optimal fishing grounds and fishing methods, and manages aquaculture. As a result, the fisheries efficiency system can improve the efficiency of the fisheries industry and reduce the number of workers, and also contribute to creating jobs in depopulated areas.

[0057] A fishing efficiency improvement system according to an embodiment includes an experiential learning unit, a proposal unit, and a management unit. The experiential learning unit learns the experience and intuition of fishermen. For example, data is collected on the conditions under which fishermen select fishing grounds and the fishing methods they use, and the generation AI learns from this data. The experiential learning unit also inputs prompts containing specific instructions based on the fishermen's experience and intuition into the generation AI, and the generation AI learns to suggest optimal fishing grounds and fishing methods based on the prompts. The proposal unit proposes optimal fishing grounds and fishing methods based on the data learned by the experiential learning unit. For example, the generation AI analyzes data on weather, ocean currents, fish ecology, etc. to identify fishing grounds that are most suitable for the season and conditions. The generation AI also proposes optimal fishing methods to maximize catches. The management unit manages aquaculture. For example, the generation AI monitors water quality, feed amount, fish health, etc., and proposes optimal management methods. The generation AI proposes optimal management methods based on data on water quality, feed amount, and fish health. As a result, the fisheries efficiency improvement system according to the embodiment can improve the efficiency of the fisheries industry and reduce the number of workers, and can also contribute to creating jobs in depopulated areas.

[0058] The experience learning unit can estimate the emotional state of fishermen in real time and learn to make decisions based on their emotional state. For example, the experience learning unit estimates the emotional state of fishermen when selecting fishing grounds in real time and has the generation AI learn that data. For example, it learns patterns in selecting fishing grounds that make fishermen feel satisfied. The experience learning unit also collects emotional data when fishermen select fishing methods and has the generation AI learn to make decisions based on those emotions. For example, it prioritizes learning fishing methods that make fishermen feel safe. The experience learning unit also monitors the emotional state of fishermen in real time and feeds that data back to the generation AI, allowing it to learn optimal fishing ground selection and fishing methods based on emotions. This makes it possible to learn optimal fishing ground selection and fishing methods based on the fishermen's emotions.

[0059] The experiential learning unit recreates the fisherman's experience in a 3D simulation environment, allowing the generating AI to learn through fishing activities in a virtual environment. For example, the experiential learning unit creates a 3D simulation environment based on the fisherman's experience, and the generating AI simulates fishing activities in that environment. For example, it selects fishing grounds and practices fishing methods in a virtual ocean. The experiential learning unit also recreates the fisherman's actions and decisions in the 3D simulation environment, allowing the generating AI to learn based on that data. For example, it recreates the fisherman's movement patterns and decision-making criteria in a simulation. The experiential learning unit also allows the generating AI to learn the fisherman's experience and intuition through fishing activities in a virtual environment. For example, it simulates the selection of fishing grounds and the effectiveness of fishing methods under different conditions. This allows the generating AI to efficiently learn the fisherman's experience and intuition through fishing activities in a virtual environment.

[0060] The experience learning unit collects fishermen's experiences as voice data, and the generation AI can learn from them using voice recognition technology. For example, the experience learning unit collects instructions based on fishermen's experience and intuition as voice data, and the generation AI learns from the data using voice recognition technology. For example, the fishermen's verbal reasons for selecting fishing grounds are saved as voice data. The experience learning unit also collects voice instructions given by fishermen during fishing activities in real time, and the generation AI analyzes and learns from the data. For example, the fishermen's voice instructions are converted into text data and input into the generation AI. The experience learning unit also records fishermen's experiences as voice data, and the generation AI analyzes and learns from the data using voice recognition technology. For example, the generation AI learns fishing ground selection and fishing method patterns based on the fishermen's voice instructions. This allows the generation AI to efficiently learn fishermen's experience and intuition using voice data.

[0061] The proposal unit can analyze satellite data and identify optimal fishing grounds in real time. The proposal unit, for example, analyzes satellite data, and the generation AI identifies optimal fishing grounds in real time. For example, fishing grounds are selected based on ocean temperature and current data. The proposal unit also has the generation AI identify optimal fishing grounds based on satellite data and provide that information to fishermen. For example, it analyzes satellite images to identify the location of schools of fish. The proposal unit also builds a system that analyzes satellite data in real time, and the generation AI identifies optimal fishing grounds. For example, it predicts changes in fishing grounds based on satellite data. This makes it possible to identify optimal fishing grounds in real time using satellite data.

[0062] The proposal unit can simulate marine ecosystems and predict long-term fluctuations in fishing grounds. In the proposal unit, for example, the generation AI simulates marine ecosystems and predicts long-term fluctuations in fishing grounds. For example, it simulates changes in ocean temperature and ocean currents. The proposal unit also predicts long-term fluctuations in fishing grounds based on the marine ecosystem simulation using the generation AI, and provides this information to fishermen. For example, it predicts the migration patterns of fish schools. The proposal unit also builds a system in which the generation AI simulates marine ecosystems and predicts long-term fluctuations in fishing grounds. For example, it predicts fluctuations in fishing grounds based on changes in the marine environment. This makes it possible to predict long-term fluctuations in fishing grounds and achieve sustainable fishing.

[0063] The management unit can estimate the emotional state of farmed fish and propose optimal farming management methods based on their emotional states. For example, the management unit estimates the emotional state of farmed fish, and the generation AI proposes optimal farming management methods based on that data. For example, if farmed fish are feeling stressed, the management unit makes appropriate environmental adjustments. The management unit also collects emotional data on farmed fish, and the generation AI proposes emotional farming management methods based on that data. For example, it makes suggestions for maintaining an environment in which farmed fish are relaxed. The management unit also monitors the emotional state of farmed fish in real time and feeds that data back to the generation AI to propose optimal farming management methods based on their emotions. This makes it possible to propose optimal farming management methods based on the emotions of farmed fish.

[0064] The management unit can use an underwater robot to monitor the status of the aquaculture farm and propose management methods in real time. For example, the management unit can use an underwater robot to monitor the status of the aquaculture farm in real time, and the generation AI can propose the optimal management method based on that data. For example, it can adjust the water quality and amount of feed. The management unit can also build a system in which the management unit uses an underwater robot to monitor the status of the aquaculture farm and the generation AI can propose the optimal management method based on that data. For example, it can analyze data collected by the underwater robot in real time. The management unit can also monitor the status of the aquaculture farm using an underwater robot and the generation AI can propose the optimal management method based on that data. For example, it can adjust the aquaculture farm environment based on footage taken by the underwater robot. This makes it possible to use an underwater robot to monitor the status of the aquaculture farm in real time and propose the optimal management method.

[0065] The management unit can analyze the growth data of farmed fish and propose the optimal feed allocation based on the growth data. For example, the management unit analyzes the growth data of farmed fish and the generation AI proposes the optimal feed allocation. For example, it adjusts the amount and type of feed according to the growth rate. The management unit also proposes the optimal feed allocation based on the growth data of farmed fish and provides this information to the fish farmer. For example, it proposes feed allocation according to the growth stage. The management unit also builds a system that analyzes the growth data of farmed fish in real time and the generation AI proposes the optimal feed allocation. For example, it automatically adjusts feed allocation based on the growth data. This makes it possible to propose the optimal feed allocation based on the growth data of farmed fish.

[0066] The management department can integrate data from different farmers and propose optimal aquaculture management methods based on the data. For example, the management department collects data from different farmers, and the generation AI integrates that data to propose optimal aquaculture management methods. For example, it proposes optimal management methods based on data from different farms. The management department can also integrate data from a variety of farmers, and the generation AI proposes optimal aquaculture management methods based on that data. For example, it proposes management methods based on success stories from different farmers. The management department can also build a system that integrates data from different farmers, and the generation AI proposes optimal aquaculture management methods. For example, it proposes optimal management methods based on data from different farms. This makes it possible to integrate data from different farmers and propose optimal aquaculture management methods.

[0067] The management unit can use the emotion estimation function for farmed fish to propose aquaculture management methods based on their emotions. For example, the management unit estimates the emotional state of farmed fish, and the generation AI proposes optimal farming management methods based on that data. For example, if farmed fish are feeling stressed, the management unit makes appropriate environmental adjustments. The management unit also collects emotional data on farmed fish, and the generation AI proposes emotion-based farming management methods based on that data. For example, it makes suggestions for maintaining an environment in which farmed fish are relaxed. The management unit also monitors the emotional state of farmed fish in real time and feeds that data back to the generation AI to propose optimal farming management methods based on their emotions. This makes it possible to propose optimal farming management methods based on the emotions of farmed fish.

[0068] The management unit can analyze the environmental data of the aquaculture farm and propose the optimal aquaculture environment based on the environmental data. For example, the management unit analyzes the environmental data of the aquaculture farm and the generation AI proposes the optimal aquaculture environment. For example, the environment is adjusted based on data such as water quality, temperature, and oxygen concentration. The management unit also proposes the optimal aquaculture environment based on the environmental data of the aquaculture farm and provides this information to the aquaculture farmer. For example, it proposes the optimal water quality management method based on the environmental data. The management unit also builds a system that analyzes the environmental data of the aquaculture farm in real time and the generation AI proposes the optimal aquaculture environment. For example, it automatically adjusts the aquaculture farm environment based on the environmental data. This makes it possible to propose the optimal aquaculture environment based on the environmental data of the aquaculture farm.

[0069] The proposal unit can maximize the catch by proposing combinations of different fishing methods and implementing multiple fishing methods simultaneously. For example, the proposal unit uses a generation AI to propose combinations of different fishing methods and maximize the catch by implementing multiple fishing methods simultaneously. For example, combining net fishing and angling. The proposal unit also uses a generation AI to propose the optimal fishing method based on the combination of different fishing methods and provides this information to fishermen. For example, combining trawl fishing and fixed net fishing. The proposal unit also builds a system in which the generation AI proposes combinations of different fishing methods and implements multiple fishing methods simultaneously to maximize the catch. For example, it simulates combinations of fishing methods. This allows different combinations of fishing methods to be proposed and the catch to be maximized.

[0070] The proposal unit can use the emotion estimation function to propose fishing methods based on the fisherman's emotions when selecting fishing grounds. For example, the proposal unit estimates the fisherman's emotional state, and the generation AI proposes the optimal fishing method based on that data. For example, it prioritizes proposing fishing methods that make the fisherman feel satisfied. The proposal unit also collects fisherman's emotional data, and the generation AI proposes fishing methods based on the fisherman's emotions based on that data. For example, it proposes fishing methods that make the fisherman feel safe. The proposal unit also monitors the fisherman's emotional state in real time and feeds that data back to the generation AI to propose the optimal fishing method based on the fisherman's emotions. This makes it possible to propose the optimal fishing method based on the fisherman's emotions.

[0071] In selecting fishing grounds, the proposal unit can use a drone to monitor ocean conditions in real time and identify the optimal fishing grounds. For example, the proposal unit uses a drone to monitor ocean conditions in real time, and the generation AI identifies the optimal fishing grounds based on that data. For example, the proposal unit analyzes ocean images taken by the drone. The proposal unit also builds a system in which the proposal unit uses a drone to monitor ocean conditions and the generation AI identifies the optimal fishing grounds based on that data. For example, the proposal unit analyzes data collected by the drone in real time. The proposal unit also uses a drone to monitor ocean conditions and the generation AI identifies the optimal fishing grounds based on that data. For example, the proposal unit selects fishing grounds based on ocean images taken by the drone. This makes it possible to use a drone to monitor ocean conditions in real time and identify the optimal fishing grounds.

[0072] The proposal unit can estimate the emotional state of the members of the fishing organization and propose an optimal operation schedule based on their emotional state. For example, the proposal unit estimates the emotional state of the members of the fishing organization, and the generation AI proposes an optimal operation schedule based on that data. For example, the operation is planned to take place during times when the members are relaxed. The proposal unit also collects emotional data of the members of the fishing organization, and the generation AI proposes an operation schedule based on their emotions based on that data. For example, it proposes a schedule that does not cause members stress. The proposal unit also monitors the emotional state of the members of the fishing organization in real time and feeds that data back to the generation AI to propose an optimal operation schedule based on their emotions. This makes it possible to propose an optimal operation schedule based on the emotions of the members of the fishing organization.

[0073] The proposal unit can analyze the data of a fishing organization and propose optimal resource allocation based on the data. For example, the proposal unit analyzes the data of a fishing organization and the generation AI proposes optimal resource allocation. For example, it proposes efficient allocation of fishing vessels and fishing gear. The proposal unit also proposes optimal resource allocation based on the data of a fishing organization and provides that information to the organization. For example, it optimizes fishing vessel operation schedules and fishing gear allocation. The proposal unit also builds a system that analyzes the data of a fishing organization in real time and the generation AI proposes optimal resource allocation. For example, it performs data analysis to improve the efficiency of resource allocation. This makes it possible to propose optimal resource allocation based on the data of a fishing organization.

[0074] The proposal unit can simulate the business processes of a fishing organization and make proposals for improving efficiency. For example, the proposal unit simulates the business processes of a fishing organization, and the generation AI makes proposals for improving efficiency. For example, the proposal unit proposes optimizing the business flow. The proposal unit also makes proposals for improving efficiency based on the business process simulation by the generation AI, and provides this information to the organization. For example, the proposal unit makes proposals for reducing waste in work. The proposal unit also builds a system that simulates business processes in real time, and the generation AI makes proposals for improving efficiency. For example, it identifies areas for improvement in the business processes and makes proposals. This makes it possible to simulate the business processes of a fishing organization and make proposals for improving efficiency.

[0075] The proposal unit can integrate data from different fishing organizations and propose an optimal operation schedule based on the data. For example, the proposal unit collects data from different fishing organizations, and the generation AI integrates the data to propose an optimal operation schedule. For example, the operation schedule is optimized based on data from multiple fishing organizations. The proposal unit also integrates data from a variety of fishing organizations, and the generation AI proposes an optimal operation schedule based on the data. For example, the operation schedules of different fishing organizations are adjusted. The proposal unit also builds a system that integrates data from different fishing organizations, and the generation AI proposes an optimal operation schedule. For example, the operation schedule is optimized based on data from multiple fishing organizations. This makes it possible to integrate data from different fishing organizations and propose an optimal operation schedule.

[0076] The proposal unit can use the emotion estimation function of the fishing organization members to propose optimal business processes based on their emotions. For example, the proposal unit estimates the emotional state of the fishing organization members, and the generation AI proposes optimal business processes based on that data. For example, work is scheduled for times when the members are relaxed. The proposal unit also collects emotional data of the fishing organization members, and the generation AI proposes emotion-based business processes based on that data. For example, it proposes business processes that do not cause members stress. The proposal unit also monitors the emotional state of the fishing organization members in real time and feeds that data back to the generation AI to propose optimal business processes based on their emotions. This makes it possible to propose optimal business processes based on the emotions of the fishing organization members.

[0077] The proposal unit can analyze the data of the fishing organization and propose an optimal sales plan based on the data. For example, the proposal unit analyzes the data of the fishing organization and the generation AI proposes an optimal sales plan. For example, a sales plan is created based on a demand forecast for the catch. The proposal unit also proposes an optimal sales plan based on the data of the fishing organization and provides this information to the organization. For example, a sales plan is created based on price trends for the catch. The proposal unit also builds a system that analyzes the data of the fishing organization in real time and the generation AI proposes an optimal sales plan. For example, it performs data analysis to improve the efficiency of the sales plan. This makes it possible to propose an optimal sales plan based on the data of the fishing organization.

[0078] The suggestion unit can estimate the emotional state of residents in depopulated areas and suggest optimal employment opportunities based on their emotional states. For example, the suggestion unit estimates the emotional state of residents in depopulated areas, and the generation AI suggests optimal employment opportunities based on that data. For example, it prioritizes suggesting employment opportunities that make residents feel satisfied. The suggestion unit also collects emotional data on residents in depopulated areas, and the generation AI suggests employment opportunities based on the emotions based on that data. For example, it suggests employment opportunities that make residents feel secure. The suggestion unit also monitors the emotional state of residents in depopulated areas in real time and feeds that data back to the generation AI to suggest optimal employment opportunities based on their emotions. This makes it possible to suggest optimal employment opportunities based on the emotions of residents in depopulated areas.

[0079] The proposal unit can analyze economic data for depopulated areas and propose optimal job creation plans based on the data. For example, the proposal unit analyzes economic data for depopulated areas, and the generation AI proposes optimal job creation plans. For example, job opportunities are created based on the industrial structure and economic situation of the region. The proposal unit also proposes optimal job creation plans based on the economic data for depopulated areas using the generation AI, and provides this information to the region. For example, it proposes new business opportunities based on the economic data for the region. The proposal unit also builds a system that analyzes economic data for depopulated areas in real time, and the generation AI proposes optimal job creation plans. For example, it automatically generates job opportunities based on the economic data. This makes it possible to propose optimal job creation plans based on the economic data for depopulated areas.

[0080] The proposal unit can analyze the skill data of residents in depopulated areas and propose the optimal vocational training program based on the data. For example, the proposal unit analyzes the skill data of residents in depopulated areas and the generation AI proposes the optimal vocational training program. For example, the proposal unit proposes a training program based on the residents' skill set. The proposal unit also proposes the optimal vocational training program based on the skill data of residents in depopulated areas and provides this information to the region. For example, the proposal unit proposes a new training program based on the residents' skill data. The proposal unit also builds a system that analyzes the skill data of residents in depopulated areas in real time and the generation AI proposes the optimal vocational training program. For example, the proposal unit automatically generates a training program based on the skill data. This makes it possible to propose the optimal vocational training program based on the skill data of residents in depopulated areas.

[0081] The proposal unit can integrate data from different depopulated areas and propose an optimal job creation plan based on the data. For example, the proposal unit collects data from different depopulated areas, and the generation AI integrates the data to propose an optimal job creation plan. For example, job opportunities are created based on data from multiple depopulated areas. The proposal unit also integrates data from a variety of depopulated areas, and the generation AI proposes an optimal job creation plan based on the data. For example, a job creation plan is proposed based on success stories from different depopulated areas. The proposal unit also builds a system that integrates data from different depopulated areas, and the generation AI proposes an optimal job creation plan. For example, job opportunities are created based on data from multiple depopulated areas. This makes it possible to integrate data from different depopulated areas and propose an optimal job creation plan.

[0082] The proposal unit can use the emotion estimation function of residents in depopulated areas to propose optimal employment opportunities based on their emotions. For example, the proposal unit estimates the emotional state of residents in depopulated areas, and the generation AI proposes optimal employment opportunities based on that data. For example, it prioritizes proposals of employment opportunities that make residents feel satisfied. The proposal unit also collects emotional data of residents in depopulated areas, and the generation AI proposes employment opportunities based on that data. For example, it proposes employment opportunities that make residents feel secure. The proposal unit also monitors the emotional state of residents in depopulated areas in real time, and feeds that data back to the generation AI to propose optimal employment opportunities based on their emotions. This makes it possible to propose optimal employment opportunities based on the emotions of residents in depopulated areas.

[0083] The proposal unit can analyze data on residents of depopulated areas and propose optimal regional revitalization plans based on the data. For example, the proposal unit analyzes data on residents of depopulated areas, and the generation AI proposes optimal regional revitalization plans. For example, the generation AI proposes regional revitalization plans based on the skills and interests of residents. The proposal unit also proposes optimal regional revitalization plans based on data on residents of depopulated areas, and provides this information to the region. For example, the proposal unit proposes new regional revitalization plans based on resident data. The proposal unit also builds a system that analyzes data on residents of depopulated areas in real time, and the generation AI proposes optimal regional revitalization plans. For example, the generation AI automatically generates regional revitalization plans based on resident data. This makes it possible to propose optimal regional revitalization plans based on data on residents of depopulated areas.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The proposal unit estimates the fisherman's emotional state, and the generation AI can suggest the optimal fishing method based on that data. For example, it will prioritize suggesting fishing methods that make the fisherman feel satisfied. The proposal unit also collects the fisherman's emotional data, and the generation AI will suggest fishing methods based on that data. For example, it will suggest fishing methods that make the fisherman feel safe. The proposal unit also monitors the fisherman's emotional state in real time and feeds that data back to the generation AI, which then suggests the optimal fishing method based on the fisherman's emotions. This makes it possible to suggest the optimal fishing method based on the fisherman's emotions.

[0086] The management unit estimates the emotional state of the farmed fish, and the generation AI can use that data to propose optimal farming management methods. For example, if the farmed fish are feeling stressed, appropriate environmental adjustments will be made. The management unit also collects emotional data from the farmed fish, and the generation AI uses that data to propose farming management methods based on their emotions. For example, it makes suggestions for maintaining an environment in which the farmed fish are relaxed. The management unit also monitors the emotional state of the farmed fish in real time and feeds that data back to the generation AI, which then proposes optimal farming management methods based on their emotions. This makes it possible to propose optimal farming management methods based on the emotions of the farmed fish.

[0087] The proposal unit estimates the emotional state of the fishing organization members, and the generation AI can propose an optimal operation schedule based on that data. For example, it can plan operations during times when the members are relaxed. The proposal unit also collects emotional data from the fishing organization members, and the generation AI can propose an emotion-based operation schedule based on that data. For example, it can propose a schedule that does not cause members stress. The proposal unit also monitors the emotional state of the fishing organization members in real time and feeds that data back to the generation AI to propose an optimal operation schedule based on their emotions. This makes it possible to propose an optimal operation schedule based on the emotions of the fishing organization members.

[0088] The proposal unit estimates the emotional state of residents in depopulated areas, and the generation AI can propose optimal employment opportunities based on that data. For example, it prioritizes proposals for employment opportunities that make residents feel satisfied. The proposal unit also collects emotional data on residents in depopulated areas, and the generation AI proposes employment opportunities based on emotions based on that data. For example, it proposes employment opportunities that make residents feel secure. The proposal unit also monitors the emotional state of residents in depopulated areas in real time, and feeds that data back to the generation AI to propose optimal employment opportunities based on emotions. This makes it possible to propose optimal employment opportunities based on the emotions of residents in depopulated areas.

[0089] The proposal unit can use the emotion estimation function of the fishing organization members to propose optimal business processes based on their emotions. For example, the emotional state of the fishing organization members can be estimated, and the generation AI can propose optimal business processes based on that data. For example, work can be scheduled for times when members are relaxed. The proposal unit also collects emotional data of the fishing organization members, and the generation AI can propose emotion-based business processes based on that data. For example, it can propose business processes that do not cause members stress. The proposal unit also monitors the emotional state of the fishing organization members in real time, and feeds that data back to the generation AI to propose optimal business processes based on their emotions. This makes it possible to propose optimal business processes based on the emotions of the fishing organization members.

[0090] The proposal unit can analyze satellite data and identify optimal fishing grounds in real time. For example, satellite data is analyzed, and the generation AI identifies optimal fishing grounds in real time. For example, fishing grounds are selected based on ocean temperature and current data. The proposal unit also uses satellite data to identify optimal fishing grounds with the generation AI, and provides that information to fishermen. For example, satellite images are analyzed to identify the location of schools of fish. The proposal unit also builds a system that analyzes satellite data in real time, and the generation AI identifies optimal fishing grounds. For example, fluctuations in fishing grounds are predicted based on satellite data. This makes it possible to identify optimal fishing grounds in real time using satellite data.

[0091] The proposal unit can simulate marine ecosystems and predict long-term fluctuations in fishing grounds. For example, the generation AI simulates marine ecosystems and predicts long-term fluctuations in fishing grounds. For example, it simulates changes in ocean temperature and ocean currents. The proposal unit also uses the generation AI to predict long-term fluctuations in fishing grounds based on the marine ecosystem simulation and provides this information to fishermen. For example, it predicts the migration patterns of fish schools. The proposal unit also builds a system in which the generation AI simulates marine ecosystems and predicts long-term fluctuations in fishing grounds. For example, it predicts fluctuations in fishing grounds based on changes in the ocean environment. This makes it possible to predict long-term fluctuations in fishing grounds and achieve sustainable fishing.

[0092] The management department can use underwater robots to monitor the status of the aquaculture farm in real time and propose optimal management methods. For example, an underwater robot can be used to monitor the status of the aquaculture farm in real time, and the generation AI can propose optimal management methods based on that data. For example, adjusting the water quality and amount of feed. The management department can also build a system in which an underwater robot can be used to monitor the status of the aquaculture farm, and the generation AI can propose optimal management methods based on that data. For example, the data collected by the underwater robot can be analyzed in real time. The management department can also monitor the status of the aquaculture farm using underwater robots, and the generation AI can propose optimal management methods based on that data. For example, adjusting the aquaculture farm environment based on footage taken by the underwater robot. This makes it possible to use underwater robots to monitor the status of the aquaculture farm in real time and propose optimal management methods.

[0093] The management department can analyze the growth data of farmed fish and propose the optimal feed distribution based on the growth data. For example, the growth data of farmed fish is analyzed and the generation AI proposes the optimal feed distribution. For example, the amount and type of feed is adjusted according to the growth rate. The management department also proposes the optimal feed distribution based on the growth data of farmed fish and provides this information to the farmer. For example, it proposes feed distribution according to the growth stage. The management department also builds a system that analyzes the growth data of farmed fish in real time and the generation AI proposes the optimal feed distribution. For example, it automatically adjusts feed distribution based on the growth data. This makes it possible to propose the optimal feed distribution based on the growth data of farmed fish.

[0094] The proposal unit can analyze the data of a fishing organization and propose optimal resource allocation based on the data. For example, the data of a fishing organization is analyzed and the generation AI proposes optimal resource allocation. For example, it proposes efficient allocation of fishing vessels and fishing gear. The proposal unit also proposes optimal resource allocation based on the data of a fishing organization and provides that information to the organization. For example, it optimizes fishing vessel operation schedules and fishing gear allocation. The proposal unit also builds a system that analyzes the data of a fishing organization in real time and the generation AI proposes optimal resource allocation. For example, it performs data analysis to improve the efficiency of resource allocation. This makes it possible to propose optimal resource allocation based on the data of a fishing organization.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The experience learning unit learns the experience and intuition of fishermen. For example, data is collected on the conditions under which fishermen choose which fishing grounds and fishing methods they use, and this data is then used by the generation AI to learn from it. The experience learning unit also inputs prompts containing specific instructions based on the fishermen's experience and intuition into the generation AI, and the generation AI learns to suggest optimal fishing grounds and fishing methods based on these prompts. Step 2: The proposal unit proposes optimal fishing grounds and fishing methods based on the data learned by the experience learning unit. For example, the generation AI analyzes data on weather, ocean currents, fish ecology, etc. to identify the fishing grounds that are most suitable for the time and conditions. The generation AI also aims to maximize the catch by proposing the optimal fishing method. Step 3: The management department manages the aquaculture. For example, the generation AI monitors the water quality, feed amount, and fish health, and proposes the optimal management method. The generation AI proposes the optimal management method based on data on water quality, feed amount, and fish health.

[0097] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0111] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0117] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] 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.

[0156] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0164] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The experiential learning department learns the experience and intuition of fishermen, a suggestion unit that suggests optimal fishing grounds or fishing methods based on the data learned by the experience learning unit; A management department that manages the aquaculture business. A system characterized by:

2. The experiential learning unit Estimate the fisherman's emotional state in real time and learn to make decisions based on said emotional state.

2. The system of claim 1.

3. The experiential learning unit The fisherman's experience is replicated in a 3D simulation environment, and the generative AI learns through fishing activities in the virtual environment.

2. The system of claim 1.

4. The experiential learning unit Fishermen's experiences are collected as voice data, and the generative AI learns from it using voice recognition technology.

2. The system of claim 1.

5. The proposal unit Analyzing satellite data to identify optimal fishing grounds in real time 2. The system of claim 1.

6. The proposal unit Simulating marine ecosystems to predict long-term changes in fishing grounds 2. The system of claim 1.

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