System

Abstract

An object of a system according to an embodiment is to analyze a behavior pattern and a habitat of an aquatic creature, evaluate a maintenance situation, and propose a sustainable fishing activity.SOLUTION: A system includes a behavior pattern analysis part, a habitat analysis part, a maintenance state evaluation part, a fishing activity proposal part, a maintenance plan proposal part, and a maintenance plan support part. The behavior pattern analysis part analyzes a behavior pattern of the aquatic creature. The habitat analysis unit analyzes a habitat of aquatic organisms. The maintenance situation evaluator evaluates a maintenance situation of the aquatic creature. The fishing activity proposal unit proposes a sustainable fishing activity. The maintenance plan proposal unit proposes a maintenance plan to a protection organization. The maintenance plan supporter supports a maintenance plan of the protection organization.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 techniques do not adequately analyze the behavioral patterns and habitats of aquatic organisms in detail, leaving room for improvement in assessing conservation status and proposing sustainable fishing activities.

[0005] The system according to the embodiment aims to analyze the behavioral patterns and habitats of aquatic organisms, evaluate their conservation status, and propose sustainable fishing activities. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavior pattern analysis unit, a habitat analysis unit, a conservation status assessment unit, a fishing activity proposal unit, a conservation measure proposal unit, and a conservation plan support unit. The behavior pattern analysis unit analyzes the behavior patterns of aquatic organisms. The habitat analysis unit analyzes the habitats of aquatic organisms. The conservation status assessment unit evaluates the conservation status of aquatic organisms. The fishing activity proposal unit proposes sustainable fishing activities. The conservation measure proposal unit proposes conservation measures to conservation organizations. The conservation plan support unit supports the conservation plans of conservation organizations. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the behavioral patterns and habitats of aquatic organisms, assess their conservation status, and suggest sustainable fishing activities. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) A conservation assessment system according to an embodiment of the present invention is a system that evaluates the conservation status of aquatic organisms through analysis of their behavioral patterns and habitats, proposes sustainable fishing activities, and proposes conservation measures and supports conservation planning to conservation organizations. This allows the conservation assessment system to accurately evaluate the conservation status of aquatic organisms and propose sustainable fishing activities. Furthermore, by proposing effective conservation measures to conservation organizations and supporting their conservation planning, the system can minimize the burden on ecosystems and realize a sustainable environment.

[0029] A conservation assessment system according to an embodiment includes a behavior pattern analysis unit, a habitat analysis unit, a conservation status assessment unit, a fishing activity proposal unit, a conservation measure proposal unit, and a conservation plan support unit. The behavior pattern analysis unit analyzes the behavior patterns of aquatic organisms. For example, the generation AI analyzes the migration routes, reproductive behavior, and feeding patterns of fish. The generation AI can also use prompts to extract behavior patterns based on behavioral data. The generation AI can also monitor changes in behavior patterns in real time and immediately detect abnormal behavior. The habitat analysis unit analyzes the habitats of aquatic organisms. For example, the generation AI analyzes environmental conditions such as water temperature, water quality, and topography preferred by specific fish species. The generation AI can also use prompts to extract habitat characteristics based on environmental data of the habitat. The generation AI can also monitor changes in the environmental conditions of the habitat in real time and immediately detect abnormalities. The conservation status assessment unit evaluates the conservation status of aquatic organisms. For example, the generation AI analyzes fluctuations in the population and habitat of a specific fish species to evaluate its conservation status. The generation AI can also use prompts to make evaluations based on data on the conservation status. Furthermore, the generation AI can monitor fluctuations in the conservation status in real time and immediately detect abnormalities. The fishing activity suggestion unit proposes sustainable fishing activities. For example, the generation AI can propose catch limits, fishing seasons, and fishing gear improvements. The generation AI can also use prompts to propose sustainable fishing activities based on data on fishing activities. Furthermore, the generation AI can monitor the impact of fishing activities in real time and immediately detect abnormalities. The conservation measure suggestion unit proposes conservation measures to conservation organizations. For example, the generation AI can propose the establishment of protected areas for specific fish species and environmental improvement measures. The generation AI can also use prompts to propose conservation measures based on data on conservation measures. Furthermore, the generation AI can monitor the effectiveness of conservation measures in real time and immediately detect abnormalities. The conservation planning support unit supports conservation organizations in their conservation planning. For example, the generation AI monitors the progress and evaluates the effectiveness of conservation plans. The generative AI can also use data-driven prompts to help with maintenance planning.Furthermore, the generation AI can monitor the progress of conservation plans in real time and immediately detect abnormalities. This allows the conservation assessment system according to the embodiment to accurately assess the conservation status of aquatic organisms and propose sustainable fishing activities. Furthermore, by proposing effective conservation measures to conservation organizations and supporting their conservation plans, it is possible to minimize the burden on ecosystems and realize a sustainable environment. For example, by establishing protected areas for specific fish species, it is possible to increase the population of those fish species. Furthermore, by setting limits on catch volume, it is possible to prevent overfishing and maintain the balance of the ecosystem. Furthermore, by monitoring the progress of conservation plans, it is possible to evaluate the effectiveness of the plans and take improvement measures as necessary.

[0030] The behavioral pattern analysis unit monitors fluctuations in behavioral patterns in real time and can immediately detect abnormal behavior. For example, the behavioral pattern analysis unit uses generative AI to track the migration paths of fish in real time and immediately detect abnormal behavior that deviates from normal behavior patterns. For example, if a specific fish species deviates from its normal migration path, the unit detects the abnormality and issues an alert. This allows for immediate detection of abnormal behavior in aquatic organisms, enabling a rapid response.

[0031] The behavioral pattern analysis unit can detect abnormalities by comparing them with past data and identify their causes. For example, using generative AI, the behavioral pattern analysis unit compares past migration route data with current data to detect abnormal migration patterns. For example, if a specific fish species deviates from its normal migration route, the cause can be identified. This makes it easier to identify the cause of the abnormality by comparing it with past data.

[0032] When analyzing the behavioral patterns of aquatic organisms, the behavioral pattern analysis unit can also use audio data and vibration data to perform more detailed behavioral analysis. For example, the behavioral pattern analysis unit uses generative AI to analyze audio data in addition to fish migration route data to detect abnormal behavioral patterns. For example, if a specific fish species deviates from its normal migration route, the abnormality is analyzed along with the audio data. This allows for more detailed behavioral analysis by using audio data and vibration data together.

[0033] The behavioral pattern analysis unit can compare behavioral patterns in different regions and under different environmental conditions to identify region-specific behavioral characteristics. For example, the behavioral pattern analysis unit uses generative AI to compare migration route data of fish from different regions to identify region-specific behavioral characteristics. For example, it analyzes differences in migration patterns in specific regions. This allows region-specific behavioral characteristics to be identified, making it possible to implement appropriate conservation measures for each region.

[0034] The habitat analysis unit can monitor changes in the habitat's environmental conditions in real time and immediately detect any abnormalities. For example, the habitat analysis unit uses generative AI to monitor changes in water temperature and water quality in real time and immediately detect any abnormalities. For example, if the water temperature preferred by a particular fish species changes suddenly, the abnormality will be detected. By monitoring changes in the habitat's environmental conditions in real time, abnormalities can be detected immediately and a prompt response can be made.

[0035] The habitat analysis unit analyzes the habitat's environmental data, compares it with past data to detect abnormalities, and can identify the cause. For example, the habitat analysis unit uses generative AI to compare past water temperature data with current data to detect abnormal fluctuations. For example, if the water temperature falls outside the normal range, it identifies the cause. This makes it easier to identify the cause of the abnormality by comparing it with past data.

[0036] When analyzing habitat environmental data, the habitat analysis unit can also use satellite images and drone footage to perform more detailed environmental analysis. For example, the habitat analysis unit uses generative AI to analyze satellite images in addition to water temperature and water quality data to detect abnormal environmental fluctuations. For example, if there is a sudden change in the water temperature preferred by a particular fish species, the abnormality will be analyzed along with satellite images. This allows for more detailed environmental analysis by using satellite images and drone images in combination.

[0037] The habitat analysis unit can compare habitat characteristics in different regions and under different environmental conditions to identify environmental characteristics specific to each region. For example, the habitat analysis unit can use generative AI to compare water temperature data from different regions to identify environmental characteristics specific to each region. For example, it can analyze differences in water temperature in specific regions. This allows the identification of environmental characteristics specific to each region, enabling appropriate conservation measures to be taken for each region.

[0038] The conservation status assessment unit can monitor changes in the conservation status in real time and immediately detect any abnormalities. For example, the conservation status assessment unit can use generative AI to monitor changes in the population of a specific fish species in real time and immediately detect any abnormalities. For example, if the population suddenly decreases, the abnormality will be detected. In this way, by monitoring changes in the conservation status in real time, abnormalities can be immediately detected and a prompt response can be made.

[0039] The conservation status assessment unit analyzes conservation status data, compares it with past data to detect abnormalities, and can identify their causes. For example, the conservation status assessment unit uses generative AI to compare past population data with current data to detect abnormal fluctuations. For example, if the population falls outside the normal range, it identifies the cause. This makes it easier to identify the cause of the abnormality by comparing it with past data.

[0040] When analyzing conservation status data, the conservation status assessment unit can also use the opinions and feedback of local residents to conduct a more detailed assessment. For example, the conservation status assessment unit can use generative AI to analyze the opinions of local residents in addition to the conservation status data to detect abnormal fluctuations. For example, it can evaluate fluctuations in population numbers based on the opinions of residents. In this way, by using the opinions and feedback of local residents in addition to the data, a more detailed assessment of the conservation status becomes possible.

[0041] The conservation status assessment unit can compare conservation status in different regions and under different environmental conditions to identify region-specific conservation issues. For example, the conservation status assessment unit can use generative AI to compare conservation status data from different regions and identify region-specific conservation issues. For example, it can analyze differences in population numbers in specific regions. This can reveal region-specific conservation issues, allowing appropriate conservation measures to be taken for each region.

[0042] The fishing activity proposal unit can monitor the impact of fishing activities in real time and immediately detect any abnormalities. For example, the fishing activity proposal unit uses generative AI to monitor fluctuations in catch volume in real time and immediately detect any abnormalities. For example, if there is a sudden increase in catch volume, the abnormality will be detected. In this way, by monitoring the impact of fishing activities in real time, any abnormalities can be immediately detected and a prompt response can be made.

[0043] The fishing activity suggestion unit can analyze fishing activity data, compare it with past data to detect abnormalities, and identify their causes. For example, the fishing activity suggestion unit uses generative AI to compare past catch data with current data to detect abnormal fluctuations. For example, if the catch falls outside the normal range, the cause is identified. This makes it easier to identify the cause of the abnormality by comparing it with past data.

[0044] When analyzing fishing activity data, the fishing activity proposal unit can also use fishermen's opinions and feedback to make more detailed proposals. For example, the fishing activity proposal unit uses generative AI to analyze fishermen's opinions in addition to fishing activity data to detect abnormal fluctuations. For example, it evaluates fluctuations in catch volume based on fishermen's opinions. In this way, by using fishermen's opinions and feedback in combination, it becomes possible to make more detailed fishing activity proposals.

[0045] The fishing activity proposal unit can compare fishing activities in different regions and under different environmental conditions to identify fishing issues specific to each region. For example, the fishing activity proposal unit can use generative AI to compare fishing activity data from different regions to identify fishing issues specific to each region. For example, it can analyze differences in catch volume in specific regions. This can identify fishing issues specific to each region, making it possible to propose appropriate fishing activities for each region.

[0046] The maintenance measure proposal unit can monitor the effectiveness of maintenance measures in real time and immediately detect any abnormalities. The maintenance measure proposal unit can, for example, use generative AI to monitor the effectiveness of maintenance measures in real time and immediately detect any abnormalities. For example, if a maintenance measure is not being effective, the unit will detect the abnormality. In this way, by monitoring the effectiveness of maintenance measures in real time, any abnormalities can be immediately detected and a prompt response can be made.

[0047] The maintenance measure proposal unit can analyze maintenance measure data, compare it with past data to detect abnormalities, and identify their causes. For example, the maintenance measure proposal unit uses generation AI to compare past maintenance measure data with current data and detect abnormal fluctuations. For example, if a maintenance measure is not effective, it identifies the cause. This makes it easier to identify the cause of the abnormality by comparing it with past data.

[0048] When analyzing conservation measure data, the conservation measure proposal unit can also use the opinions and feedback of local residents to make more detailed proposals. For example, the conservation measure proposal unit can use generative AI to analyze the opinions of local residents in addition to the conservation measure data to detect abnormal fluctuations. For example, it can evaluate the effectiveness of conservation measures based on the opinions of residents. In this way, by taking into account the opinions and feedback of local residents, it becomes possible to propose more detailed conservation measures.

[0049] The conservation measure proposal unit can compare conservation measures in different regions and under different environmental conditions to identify region-specific conservation issues. For example, the conservation measure proposal unit can use generative AI to compare conservation measure data from different regions to identify region-specific conservation issues. For example, it can analyze the differences in the effectiveness of conservation measures in specific regions. This allows region-specific conservation issues to be identified, making it possible to implement appropriate conservation measures for each region.

[0050] The maintenance planning support unit can monitor the progress of the maintenance plan in real time and immediately detect any abnormalities. The maintenance planning support unit can, for example, use generative AI to monitor the progress of the maintenance plan in real time and immediately detect any abnormalities. For example, if the maintenance plan is not progressing as planned, the abnormality will be detected. By monitoring the progress of the maintenance plan in real time, abnormalities can be detected immediately and a prompt response can be made.

[0051] The maintenance planning support unit can analyze maintenance plan data, compare it with past data to detect abnormalities, and identify their causes. For example, the maintenance planning support unit uses generative AI to compare past maintenance plan data with current data to detect abnormal fluctuations. For example, if a maintenance plan is not progressing as planned, the cause is identified. This makes it easier to identify the cause of the abnormality by comparing it with past data.

[0052] When analyzing conservation plan data, the Conservation Planning Support Department can provide more detailed support by incorporating the opinions and feedback of local residents. For example, the Conservation Planning Support Department can use generative AI to analyze the opinions of local residents in addition to the conservation plan data to detect abnormal fluctuations. For example, it can evaluate the progress of the conservation plan based on the opinions of local residents. This allows for more detailed support for conservation plans by incorporating the opinions and feedback of local residents.

[0053] The Conservation Planning Support Department can compare conservation plans in different regions and under different environmental conditions to identify region-specific conservation issues. For example, the Conservation Planning Support Department can use generative AI to compare conservation plan data from different regions to identify region-specific conservation issues. For example, it can analyze differences in the progress of conservation plans in specific regions. This can help identify region-specific conservation issues and support the creation of appropriate conservation plans for each region.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The conservation assessment system can further include a water quality improvement proposal unit. The water quality improvement proposal unit can analyze water quality data from aquatic organism habitats and propose appropriate water quality improvement measures. For example, the system can use generative AI to analyze data such as water temperature, pH, and dissolved oxygen levels to detect water quality abnormalities. If an abnormality is detected, the system will identify the cause and propose appropriate improvement measures. This will optimize the habitat for aquatic organisms and improve conservation conditions.

[0056] The behavioral pattern analysis unit can compare behavioral patterns under different seasons and climatic conditions to identify seasonal behavioral characteristics. For example, generative AI can be used to compare migration route data of fish species in different seasons to identify seasonal behavioral characteristics. This allows appropriate conservation measures to be taken for each season.

[0057] The behavioral pattern analysis unit can also use light and temperature data to analyze behavioral patterns, allowing for more detailed behavioral analysis. For example, generative AI can be used to analyze light data in addition to fish migration route data to detect abnormal behavioral patterns. This allows for more detailed behavioral analysis by using light and temperature data in combination.

[0058] The behavioral pattern analysis unit compares behavioral patterns under different water depths and current conditions, and can identify behavioral characteristics specific to those depths and currents. For example, using generation AI, it can compare migration route data of fish at different depths and identify behavioral characteristics specific to those depths. This makes it possible to implement appropriate conservation measures for each water depth and current.

[0059] When analyzing habitat environmental data, the habitat analysis unit can also use geological and vegetation data to perform more detailed environmental analysis. For example, using generative AI, it can analyze geological data in addition to water temperature and water quality data to detect abnormal environmental changes. This allows for more detailed environmental analysis by using geological and vegetation data in combination.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The behavioral pattern analysis unit analyzes the behavioral patterns of aquatic organisms. For example, the generation AI analyzes the migration routes, reproductive behavior, and feeding patterns of fish. The generation AI can also use prompts to extract behavioral patterns based on behavioral data. Furthermore, the generation AI can monitor fluctuations in behavioral patterns in real time and immediately detect abnormal behavior. Step 2: The habitat analysis unit analyzes the habitat of aquatic organisms. For example, the AI ​​generator analyzes the environmental conditions, such as water temperature, water quality, and topography, that a particular fish species prefers. The AI ​​generator can also use prompts to extract habitat characteristics based on the habitat's environmental data. Furthermore, the AI ​​generator can monitor changes in the habitat's environmental conditions in real time and immediately detect any abnormalities. Step 3: The conservation status assessment unit assesses the conservation status of aquatic organisms. For example, the AI ​​generator can analyze population fluctuations and habitat changes for a specific fish species to assess the conservation status. The AI ​​generator can also use prompts to make assessments based on data on the conservation status. Furthermore, the AI ​​generator can monitor changes in the conservation status in real time and immediately detect abnormalities. Step 4: The fishing activity suggestion unit proposes sustainable fishing activities. For example, the generation AI can propose catch limits, fishing seasons, and improvements to fishing gear. The generation AI can also use prompts to suggest sustainable fishing activities based on data on fishing activities. Furthermore, the generation AI can monitor the impact of fishing activities in real time and immediately detect any anomalies. Step 5: The conservation proposal unit proposes conservation measures to conservation organizations. For example, the generation AI can propose the establishment of protected areas for specific fish species or environmental improvement measures. The generation AI can also use prompts to suggest conservation measures based on data related to conservation measures. Furthermore, the generation AI can monitor the effectiveness of conservation measures in real time and immediately detect any abnormalities. Step 6: The conservation planning support unit supports conservation organizations in their conservation plans. For example, the generation AI monitors the progress of conservation plans and evaluates their effectiveness. The generation AI can also use prompts to provide support based on data related to the conservation plan. Furthermore, the generation AI can monitor the progress of conservation plans in real time and immediately detect any anomalies.

[0062] (Example 2) A conservation assessment system according to an embodiment of the present invention is a system that evaluates the conservation status of aquatic organisms through analysis of their behavioral patterns and habitats, proposes sustainable fishing activities, and proposes conservation measures and supports conservation planning to conservation organizations. This allows the conservation assessment system to accurately evaluate the conservation status of aquatic organisms and propose sustainable fishing activities. Furthermore, by proposing effective conservation measures to conservation organizations and supporting their conservation planning, the system can minimize the burden on ecosystems and realize a sustainable environment.

[0063] A conservation assessment system according to an embodiment includes a behavior pattern analysis unit, a habitat analysis unit, a conservation status assessment unit, a fishing activity proposal unit, a conservation measure proposal unit, and a conservation plan support unit. The behavior pattern analysis unit analyzes the behavior patterns of aquatic organisms. For example, the generation AI analyzes the migration routes, reproductive behavior, and feeding patterns of fish. The generation AI can also use prompts to extract behavior patterns based on behavioral data. The generation AI can also monitor changes in behavior patterns in real time and immediately detect abnormal behavior. The habitat analysis unit analyzes the habitats of aquatic organisms. For example, the generation AI analyzes environmental conditions such as water temperature, water quality, and topography preferred by specific fish species. The generation AI can also use prompts to extract habitat characteristics based on environmental data of the habitat. The generation AI can also monitor changes in the environmental conditions of the habitat in real time and immediately detect abnormalities. The conservation status assessment unit evaluates the conservation status of aquatic organisms. For example, the generation AI analyzes fluctuations in the population and habitat of a specific fish species to evaluate its conservation status. The generation AI can also use prompts to make evaluations based on data on the conservation status. Furthermore, the generation AI can monitor fluctuations in the conservation status in real time and immediately detect abnormalities. The fishing activity suggestion unit proposes sustainable fishing activities. For example, the generation AI can propose catch limits, fishing seasons, and fishing gear improvements. The generation AI can also use prompts to propose sustainable fishing activities based on data on fishing activities. Furthermore, the generation AI can monitor the impact of fishing activities in real time and immediately detect abnormalities. The conservation measure suggestion unit proposes conservation measures to conservation organizations. For example, the generation AI can propose the establishment of protected areas for specific fish species and environmental improvement measures. The generation AI can also use prompts to propose conservation measures based on data on conservation measures. Furthermore, the generation AI can monitor the effectiveness of conservation measures in real time and immediately detect abnormalities. The conservation planning support unit supports conservation organizations in their conservation planning. For example, the generation AI monitors the progress and evaluates the effectiveness of conservation plans. The generative AI can also use data-driven prompts to help with maintenance planning.Furthermore, the generation AI can monitor the progress of conservation plans in real time and immediately detect abnormalities. This allows the conservation assessment system according to the embodiment to accurately assess the conservation status of aquatic organisms and propose sustainable fishing activities. Furthermore, by proposing effective conservation measures to conservation organizations and supporting their conservation plans, it is possible to minimize the burden on ecosystems and realize a sustainable environment. For example, by establishing protected areas for specific fish species, it is possible to increase the population of those fish species. Furthermore, by setting limits on catch volume, it is possible to prevent overfishing and maintain the balance of the ecosystem. Furthermore, by monitoring the progress of conservation plans, it is possible to evaluate the effectiveness of the plans and take improvement measures as necessary.

[0064] The behavioral pattern analysis unit monitors fluctuations in behavioral patterns in real time and can immediately detect abnormal behavior. For example, the behavioral pattern analysis unit uses generative AI to track the migration paths of fish in real time and immediately detect abnormal behavior that deviates from normal behavior patterns. For example, if a specific fish species deviates from its normal migration path, the unit detects the abnormality and issues an alert. This allows for immediate detection of abnormal behavior in aquatic organisms, enabling a rapid response.

[0065] The behavioral pattern analysis unit can detect abnormalities by comparing them with past data and identify their causes. For example, using generative AI, the behavioral pattern analysis unit compares past migration route data with current data to detect abnormal migration patterns. For example, if a specific fish species deviates from its normal migration route, the cause can be identified. This makes it easier to identify the cause of the abnormality by comparing it with past data.

[0066] The behavioral pattern analysis unit can use the emotion estimation function to estimate stress levels from the behavior of aquatic organisms and identify the causes of stress. For example, the behavioral pattern analysis unit uses generative AI to analyze fish behavioral data and estimate stress levels. For example, it can detect abnormal movement patterns or feeding behavior and identify the causes of stress. By identifying the causes of stress for aquatic organisms, appropriate conservation measures can be taken.

[0067] When analyzing the behavioral patterns of aquatic organisms, the behavioral pattern analysis unit can also use audio data and vibration data to perform more detailed behavioral analysis. For example, the behavioral pattern analysis unit uses generative AI to analyze audio data in addition to fish migration route data to detect abnormal behavioral patterns. For example, if a specific fish species deviates from its normal migration route, the abnormality is analyzed along with the audio data. This allows for more detailed behavioral analysis by using audio data and vibration data together.

[0068] The behavioral pattern analysis unit can compare behavioral patterns in different regions and under different environmental conditions to identify region-specific behavioral characteristics. For example, the behavioral pattern analysis unit uses generative AI to compare migration route data of fish from different regions to identify region-specific behavioral characteristics. For example, it analyzes differences in migration patterns in specific regions. This allows region-specific behavioral characteristics to be identified, making it possible to implement appropriate conservation measures for each region.

[0069] The behavioral pattern analysis unit uses the emotion estimation function to analyze human emotional responses to changes in behavioral patterns, making it possible to evaluate the effectiveness of conservation activities. The behavioral pattern analysis unit, for example, uses generative AI to analyze human emotional responses to changes in the behavioral patterns of aquatic organisms. For example, it analyzes human emotional responses when abnormal behavioral patterns are detected and evaluates the effectiveness of conservation activities. In this way, by analyzing human emotional responses, it is possible to evaluate the effectiveness of conservation activities and take measures to improve them.

[0070] The habitat analysis unit can monitor changes in the habitat's environmental conditions in real time and immediately detect any abnormalities. For example, the habitat analysis unit uses generative AI to monitor changes in water temperature and water quality in real time and immediately detect any abnormalities. For example, if the water temperature preferred by a particular fish species changes suddenly, the abnormality will be detected. By monitoring changes in the habitat's environmental conditions in real time, abnormalities can be detected immediately and a prompt response can be made.

[0071] The habitat analysis unit analyzes the habitat's environmental data, compares it with past data to detect abnormalities, and can identify the cause. For example, the habitat analysis unit uses generative AI to compare past water temperature data with current data to detect abnormal fluctuations. For example, if the water temperature falls outside the normal range, it identifies the cause. This makes it easier to identify the cause of the abnormality by comparing it with past data.

[0072] The habitat analysis unit can use the emotion estimation function to estimate the stress level of aquatic organisms in response to changes in their habitat and identify the stress factors. For example, the habitat analysis unit can use generative AI to estimate the stress level of aquatic organisms in response to fluctuations in water temperature. For example, if the water temperature changes suddenly, the stress factor can be identified. By identifying the stress factors of aquatic organisms, appropriate conservation measures can be taken.

[0073] When analyzing habitat environmental data, the habitat analysis unit can also use satellite images and drone footage to perform more detailed environmental analysis. For example, the habitat analysis unit uses generative AI to analyze satellite images in addition to water temperature and water quality data to detect abnormal environmental fluctuations. For example, if there is a sudden change in the water temperature preferred by a particular fish species, the abnormality will be analyzed along with satellite images. This allows for more detailed environmental analysis by using satellite images and drone images in combination.

[0074] The habitat analysis unit can compare habitat characteristics in different regions and under different environmental conditions to identify environmental characteristics specific to each region. For example, the habitat analysis unit can use generative AI to compare water temperature data from different regions to identify environmental characteristics specific to each region. For example, it can analyze differences in water temperature in specific regions. This allows the identification of environmental characteristics specific to each region, enabling appropriate conservation measures to be taken for each region.

[0075] The habitat analysis unit can use the emotion estimation function to analyze human emotional responses to habitat changes and evaluate the effectiveness of conservation efforts. The habitat analysis unit, for example, uses generative AI to analyze human emotional responses to habitat changes. For example, it can analyze human emotional responses to sudden changes in water temperature and evaluate the effectiveness of conservation efforts. In this way, by analyzing human emotional responses, the effectiveness of conservation efforts can be evaluated and improvement measures can be taken.

[0076] The conservation status assessment unit can monitor changes in the conservation status in real time and immediately detect any abnormalities. For example, the conservation status assessment unit can use generative AI to monitor changes in the population of a specific fish species in real time and immediately detect any abnormalities. For example, if the population suddenly decreases, the abnormality will be detected. In this way, by monitoring changes in the conservation status in real time, abnormalities can be immediately detected and a prompt response can be made.

[0077] The conservation status assessment unit analyzes conservation status data, compares it with past data to detect abnormalities, and can identify their causes. For example, the conservation status assessment unit uses generative AI to compare past population data with current data to detect abnormal fluctuations. For example, if the population falls outside the normal range, it identifies the cause. This makes it easier to identify the cause of the abnormality by comparing it with past data.

[0078] The conservation status assessment unit can use the emotion estimation function to estimate the stress level of aquatic organisms in response to changes in conservation status and identify the stress factors. For example, the conservation status assessment unit can use generative AI to estimate the stress level of aquatic organisms in response to changes in population. For example, if the population suddenly decreases, the stress factors can be identified. By identifying the stress factors of aquatic organisms, appropriate conservation measures can be taken.

[0079] When analyzing conservation status data, the conservation status assessment unit can also use the opinions and feedback of local residents to conduct a more detailed assessment. For example, the conservation status assessment unit can use generative AI to analyze the opinions of local residents in addition to the conservation status data to detect abnormal fluctuations. For example, it can evaluate fluctuations in population numbers based on the opinions of residents. In this way, by using the opinions and feedback of local residents in addition to the data, a more detailed assessment of the conservation status becomes possible.

[0080] The conservation status assessment unit can compare conservation status in different regions and under different environmental conditions to identify region-specific conservation issues. For example, the conservation status assessment unit can use generative AI to compare conservation status data from different regions and identify region-specific conservation issues. For example, it can analyze differences in population numbers in specific regions. This can reveal region-specific conservation issues, allowing appropriate conservation measures to be taken for each region.

[0081] The conservation status assessment unit can use the emotion estimation function to analyze human emotional responses to changes in the conservation status and evaluate the effectiveness of conservation activities. The conservation status assessment unit can, for example, use generative AI to analyze human emotional responses to changes in the conservation status. For example, it can analyze human emotional responses when a population suddenly declines and evaluate the effectiveness of conservation activities. In this way, by analyzing human emotional responses, the effectiveness of conservation activities can be evaluated and improvement measures can be taken.

[0082] The fishing activity proposal unit can monitor the impact of fishing activities in real time and immediately detect any abnormalities. For example, the fishing activity proposal unit uses generative AI to monitor fluctuations in catch volume in real time and immediately detect any abnormalities. For example, if there is a sudden increase in catch volume, the abnormality will be detected. In this way, by monitoring the impact of fishing activities in real time, any abnormalities can be immediately detected and a prompt response can be made.

[0083] The fishing activity suggestion unit can analyze fishing activity data, compare it with past data to detect abnormalities, and identify their causes. For example, the fishing activity suggestion unit uses generative AI to compare past catch data with current data to detect abnormal fluctuations. For example, if the catch falls outside the normal range, the cause is identified. This makes it easier to identify the cause of the abnormality by comparing it with past data.

[0084] The fishing activity suggestion unit can use the emotion estimation function to estimate the stress level of aquatic organisms in response to changes in fishing activities and identify the stress factors. The fishing activity suggestion unit, for example, uses a generative AI to estimate the stress level of aquatic organisms in response to fluctuations in catch volume. For example, if the catch volume increases suddenly, the stress factor is identified. This allows the unit to suggest appropriate fishing activities by identifying the stress factors of aquatic organisms.

[0085] When analyzing fishing activity data, the fishing activity proposal unit can also use fishermen's opinions and feedback to make more detailed proposals. For example, the fishing activity proposal unit uses generative AI to analyze fishermen's opinions in addition to fishing activity data to detect abnormal fluctuations. For example, it evaluates fluctuations in catch volume based on fishermen's opinions. In this way, by using fishermen's opinions and feedback in combination, it becomes possible to make more detailed fishing activity proposals.

[0086] The fishing activity proposal unit can compare fishing activities in different regions and under different environmental conditions to identify fishing issues specific to each region. For example, the fishing activity proposal unit can use generative AI to compare fishing activity data from different regions to identify fishing issues specific to each region. For example, it can analyze differences in catch volume in specific regions. This can identify fishing issues specific to each region, making it possible to propose appropriate fishing activities for each region.

[0087] The fishing activity proposal unit can use the emotion estimation function to analyze human emotional responses to changes in fishing activities and evaluate the effectiveness of sustainable fishing activities. The fishing activity proposal unit, for example, uses generative AI to analyze human emotional responses to changes in fishing activities. For example, it can analyze human emotional responses when there is a sudden increase in catch volume and evaluate the effectiveness of sustainable fishing activities. In this way, by analyzing human emotional responses, it is possible to evaluate the effectiveness of sustainable fishing activities and take measures to improve them.

[0088] The maintenance measure proposal unit can monitor the effectiveness of maintenance measures in real time and immediately detect any abnormalities. The maintenance measure proposal unit can, for example, use generative AI to monitor the effectiveness of maintenance measures in real time and immediately detect any abnormalities. For example, if a maintenance measure is not being effective, the unit will detect the abnormality. In this way, by monitoring the effectiveness of maintenance measures in real time, any abnormalities can be immediately detected and a prompt response can be made.

[0089] The maintenance measure proposal unit can analyze maintenance measure data, compare it with past data to detect abnormalities, and identify their causes. For example, the maintenance measure proposal unit uses generation AI to compare past maintenance measure data with current data and detect abnormal fluctuations. For example, if a maintenance measure is not effective, it identifies the cause. This makes it easier to identify the cause of the abnormality by comparing it with past data.

[0090] The conservation measure proposal unit can use the emotion estimation function to estimate the stress level of aquatic organisms in response to changes in conservation measures and identify the stress factors. The conservation measure proposal unit, for example, uses a generation AI to estimate the stress level of aquatic organisms in response to changes in conservation measures. For example, if a conservation measure is not effective, the stress factors are identified. By identifying the stress factors of aquatic organisms, appropriate conservation measures can be taken.

[0091] When analyzing conservation measure data, the conservation measure proposal unit can also use the opinions and feedback of local residents to make more detailed proposals. For example, the conservation measure proposal unit can use generative AI to analyze the opinions of local residents in addition to the conservation measure data to detect abnormal fluctuations. For example, it can evaluate the effectiveness of conservation measures based on the opinions of residents. In this way, by taking into account the opinions and feedback of local residents, it becomes possible to propose more detailed conservation measures.

[0092] The conservation measure proposal unit can compare conservation measures in different regions and under different environmental conditions to identify region-specific conservation issues. For example, the conservation measure proposal unit can use generative AI to compare conservation measure data from different regions to identify region-specific conservation issues. For example, it can analyze the differences in the effectiveness of conservation measures in specific regions. This allows region-specific conservation issues to be identified, making it possible to implement appropriate conservation measures for each region.

[0093] The maintenance measure proposal unit can use the emotion estimation function to analyze human emotional responses to changes in maintenance measures and evaluate the effectiveness of maintenance activities. The maintenance measure proposal unit, for example, uses generative AI to analyze human emotional responses to changes in maintenance measures. For example, it analyzes human emotional responses when maintenance measures are not effective and evaluates the effectiveness of maintenance activities. In this way, by analyzing human emotional responses, the effectiveness of maintenance activities can be evaluated and improvement measures can be taken.

[0094] The maintenance planning support unit can monitor the progress of the maintenance plan in real time and immediately detect any abnormalities. The maintenance planning support unit can, for example, use generative AI to monitor the progress of the maintenance plan in real time and immediately detect any abnormalities. For example, if the maintenance plan is not progressing as planned, the abnormality will be detected. By monitoring the progress of the maintenance plan in real time, abnormalities can be detected immediately and a prompt response can be made.

[0095] The maintenance planning support unit can analyze maintenance plan data, compare it with past data to detect abnormalities, and identify their causes. For example, the maintenance planning support unit uses generative AI to compare past maintenance plan data with current data to detect abnormal fluctuations. For example, if a maintenance plan is not progressing as planned, the cause is identified. This makes it easier to identify the cause of the abnormality by comparing it with past data.

[0096] The conservation plan support unit can use the emotion estimation function to estimate the stress level of aquatic organisms in relation to the progress of the conservation plan and identify the stress factors. The conservation plan support unit, for example, uses generative AI to estimate the stress level of aquatic organisms in relation to the progress of the conservation plan. For example, if the conservation plan is not progressing as planned, the stress factors are identified. This makes it possible to support appropriate conservation plans by identifying the stress factors of aquatic organisms.

[0097] When analyzing conservation plan data, the Conservation Planning Support Department can provide more detailed support by incorporating the opinions and feedback of local residents. For example, the Conservation Planning Support Department can use generative AI to analyze the opinions of local residents in addition to the conservation plan data to detect abnormal fluctuations. For example, it can evaluate the progress of the conservation plan based on the opinions of local residents. This allows for more detailed support for conservation plans by incorporating the opinions and feedback of local residents.

[0098] The Conservation Planning Support Department can compare conservation plans in different regions and under different environmental conditions to identify region-specific conservation issues. For example, the Conservation Planning Support Department can use generative AI to compare conservation plan data from different regions to identify region-specific conservation issues. For example, it can analyze differences in the progress of conservation plans in specific regions. This can help identify region-specific conservation issues and support the creation of appropriate conservation plans for each region.

[0099] The maintenance planning support unit can use the emotion estimation function to analyze human emotional responses to the progress of a maintenance plan and evaluate the effectiveness of maintenance activities. The maintenance planning support unit, for example, uses generative AI to analyze human emotional responses to the progress of a maintenance plan. For example, it can analyze human emotional responses when a maintenance plan is not progressing as planned and evaluate the effectiveness of maintenance activities. In this way, by analyzing human emotional responses, the effectiveness of maintenance activities can be evaluated and improvement measures can be taken.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The conservation assessment system can further include a water quality improvement proposal unit. The water quality improvement proposal unit can analyze water quality data from aquatic organism habitats and propose appropriate water quality improvement measures. For example, the system can use generative AI to analyze data such as water temperature, pH, and dissolved oxygen levels to detect water quality abnormalities. If an abnormality is detected, the system will identify the cause and propose appropriate improvement measures. This will optimize the habitat for aquatic organisms and improve conservation conditions.

[0102] The behavioral pattern analysis unit uses emotion estimation to estimate the happiness level of aquatic organisms from their behavior and can propose environmental improvement measures to improve their happiness level. For example, generative AI can be used to analyze fish behavior data and estimate their happiness level. If an abnormal behavioral pattern is detected, the cause can be identified and environmental improvement measures to improve happiness can be proposed. This can improve the happiness level of aquatic organisms and thereby improve conservation status.

[0103] The behavioral pattern analysis unit can compare behavioral patterns under different seasons and climatic conditions to identify seasonal behavioral characteristics. For example, generative AI can be used to compare migration route data of fish species in different seasons to identify seasonal behavioral characteristics. This allows appropriate conservation measures to be taken for each season.

[0104] The behavioral pattern analysis unit can use emotion estimation to estimate the fear level from the behavior of aquatic organisms and identify the factors that cause the fear. For example, generative AI can be used to analyze the behavioral data of fish to estimate the fear level. Abnormal movement patterns and ingestive behavior can be detected and the factors that cause the fear can be identified. By identifying the factors that cause the fear of aquatic organisms, appropriate conservation measures can be taken.

[0105] The behavioral pattern analysis unit can also use light and temperature data to analyze behavioral patterns, allowing for more detailed behavioral analysis. For example, generative AI can be used to analyze light data in addition to fish migration route data to detect abnormal behavioral patterns. This allows for more detailed behavioral analysis by using light and temperature data in combination.

[0106] The behavioral pattern analysis unit uses the emotion estimation function to estimate the level of excitement of aquatic organisms in response to changes in behavioral patterns and identify the factors that cause the excitement. For example, generative AI can be used to analyze fish behavior data and estimate the level of excitement. If an abnormal behavioral pattern is detected, the factors that cause the excitement can be identified. This allows appropriate conservation measures to be taken by identifying the factors that cause the excitement of aquatic organisms.

[0107] The behavioral pattern analysis unit compares behavioral patterns under different water depths and current conditions, and can identify behavioral characteristics specific to those depths and currents. For example, using generation AI, it can compare migration route data of fish at different depths and identify behavioral characteristics specific to those depths. This makes it possible to implement appropriate conservation measures for each water depth and current.

[0108] The habitat analysis unit uses emotion estimation to estimate the safety of aquatic organisms in response to habitat changes and can propose environmental improvement measures to increase safety. For example, generative AI can be used to estimate the safety of aquatic organisms in response to changes in water temperature. If an abnormality is detected, the cause can be identified and environmental improvement measures to increase safety can be proposed. This can improve the safety of aquatic organisms and thereby improve conservation status.

[0109] When analyzing habitat environmental data, the habitat analysis unit can also use geological and vegetation data to perform more detailed environmental analysis. For example, using generative AI, it can analyze geological data in addition to water temperature and water quality data to detect abnormal environmental changes. This allows for more detailed environmental analysis by using geological and vegetation data in combination.

[0110] The conservation status assessment unit uses emotion estimation to estimate the level of satisfaction of aquatic organisms in response to changes in conservation status and can propose conservation measures to improve satisfaction. For example, it uses generative AI to estimate the level of satisfaction of aquatic organisms in response to fluctuations in population. If an abnormality is detected, it identifies the cause and proposes conservation measures to improve satisfaction. This can improve the conservation status by increasing the satisfaction of aquatic organisms.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The behavioral pattern analysis unit analyzes the behavioral patterns of aquatic organisms. For example, the generation AI analyzes the migration routes, reproductive behavior, and feeding patterns of fish. The generation AI can also use prompts to extract behavioral patterns based on behavioral data. Furthermore, the generation AI can monitor fluctuations in behavioral patterns in real time and immediately detect abnormal behavior. Step 2: The habitat analysis unit analyzes the habitat of aquatic organisms. For example, the AI ​​generator analyzes the environmental conditions, such as water temperature, water quality, and topography, that a particular fish species prefers. The AI ​​generator can also use prompts to extract habitat characteristics based on the habitat's environmental data. Furthermore, the AI ​​generator can monitor changes in the habitat's environmental conditions in real time and immediately detect any abnormalities. Step 3: The conservation status assessment unit assesses the conservation status of aquatic organisms. For example, the AI ​​generator can analyze population fluctuations and habitat changes for a specific fish species to assess the conservation status. The AI ​​generator can also use prompts to make assessments based on data on the conservation status. Furthermore, the AI ​​generator can monitor changes in the conservation status in real time and immediately detect abnormalities. Step 4: The fishing activity suggestion unit proposes sustainable fishing activities. For example, the generation AI can propose catch limits, fishing seasons, and improvements to fishing gear. The generation AI can also use prompts to suggest sustainable fishing activities based on data on fishing activities. Furthermore, the generation AI can monitor the impact of fishing activities in real time and immediately detect any anomalies. Step 5: The conservation proposal unit proposes conservation measures to conservation organizations. For example, the generation AI can propose the establishment of protected areas for specific fish species or environmental improvement measures. The generation AI can also use prompts to suggest conservation measures based on data related to conservation measures. Furthermore, the generation AI can monitor the effectiveness of conservation measures in real time and immediately detect any abnormalities. Step 6: The conservation planning support unit supports conservation organizations in their conservation plans. For example, the generation AI monitors the progress of conservation plans and evaluates their effectiveness. The generation AI can also use prompts to provide support based on data related to the conservation plan. Furthermore, the generation AI can monitor the progress of conservation plans in real time and immediately detect any anomalies.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0115] 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.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 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.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0157] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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, in order to avoid confusion and to 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.

[0179] 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]

[0180] 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. a behavior pattern analysis unit that analyzes the behavior patterns of aquatic organisms; a habitat analysis department that analyzes the habitats of aquatic organisms; a Conservation Status Assessment Division that assesses the conservation status of aquatic organisms; The Fisheries Activities Proposal Division, which proposes sustainable fishing activities, A conservation proposal department that proposes conservation measures to conservation organizations; A conservation planning support department that supports conservation plans of conservation organizations. A system characterized by:

2. The behavior pattern analysis unit When analyzing the behavioral patterns of the aquatic organisms, sound data or vibration data is also used to perform a more detailed behavioral analysis.

2. The system of claim 1.

3. The habitat analysis unit Monitoring changes in the environmental conditions of said habitat in real time and immediately detecting abnormalities 2. The system of claim 1.

4. The maintenance status evaluation unit Monitor fluctuations in the maintenance status in real time and immediately detect abnormalities 2. The system of claim 1.

5. The fishing activity proposal unit Monitor the impact of said fishing activities in real time and immediately detect any anomalies 2. The system of claim 1.

6. The maintenance measure proposal unit Estimating the stress level of the aquatic organisms in response to changes in the conservation measures and identifying the stress factors 2. The system of claim 1.

7. The maintenance planning support unit Estimating a stress level of the aquatic organisms in relation to progress of the conservation plan and identifying stress factors.

2. The system of claim 1.

8. The behavior pattern analysis unit Estimating a stress level from the behavior of the aquatic organism and identifying stress factors 2. The system of claim 1.

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