System

Abstract

An object of a system according to an embodiment is to accurately estimate the recognition of fish species, the size of fish, and the scale of a school, and to optimize the effect of fish attraction or fish avoidance using sound.SOLUTION: A system includes a collection part, an analysis part, an estimation part, a cooperation part, a recording part, an analysis part, a generation part, a learning part, and a provision part. The collection part collects data for recognizing a fish species. The analysis unit analyzes the data collected by the collection unit. The estimation unit estimates the size of fish and the scale of a school based on the data analyzed by the analysis unit. The cooperation unit cooperates with the environmental data. The recording unit records sound. The analysis unit analyzes the sound recorded by the recording unit. The generation unit reproduces the sound analyzed by the analysis unit. The learning unit creates a sound source based on the sound generated by the generation unit. The providing unit provides the sound source created by the learning unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to accurately identify fish species, estimate fish size, and estimate the size of schools, as well as optimizing the effectiveness of sound to attract and repel fish.

[0005] The system according to the embodiment aims to accurately estimate the fish species, fish size, and school size, and to optimize the effectiveness of attracting and repelling fish using sound. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an estimation unit, a linking unit, a recording unit, an analysis unit, a generation unit, a learning unit, and a providing unit. The collection unit collects data for recognizing fish species. The analysis unit analyzes the data collected by the collection unit. The estimation unit estimates the size of fish and the scale of the school based on the data analyzed by the analysis unit. The linking unit links with environmental data. The recording unit records sound. The analysis unit analyzes the sound recorded by the recording unit. The generation unit reproduces the sound analyzed by the analysis unit. The learning unit creates a sound source based on the sound generated by the generation unit. The providing unit provides the sound source created by the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately estimate the fish species, fish size, and school size, and can optimize the effectiveness of attracting and repelling fish using sound. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) A system according to an embodiment of the present invention effectively utilizes repellent and fish-attracting sounds through fish species recognition and automatic learning. This system uses identification AI to analyze data from underwater cameras installed in fishing basins and other facilities to identify fish species. Next, it estimates the size of individual fish, the size of schools, and the average size of fish. Furthermore, it links this data with environmental data such as air pressure, water temperature, light intensity, transparency, turbidity, water color, water current, floating objects, and odors. Underwater sounds are recorded and analyzed, including the sounds of bait schools swimming, predation sounds, and predator sounds. These sounds are reproduced using a generation AI, and their fish-attracting and repellent effects for each fish species and size are verified using an underwater camera. Combinations of multiple sounds are learned, and optimal sound sources are created for each fish species and size. Finally, the sound source data is used to efficiently attract fish. For example, sounds to attract or repel specific fish species can be generated and applied to actual fishing activities. This enables the system to effectively utilize repellent and fish-attracting sounds through fish species recognition and automatic learning. For example, sounds that attract specific fish species or that repel specific fish species can be generated, and this can be applied to actual fishing activities.

[0029] The fish species recognition system according to the embodiment includes a collection unit, an analysis unit, an estimation unit, a linking unit, a recording unit, an analysis unit, a generation unit, a learning unit, and a provision unit. The collection unit collects data for recognizing fish species. For example, the collection unit collects data from underwater cameras installed in a fishing port or the like. The collection unit can also collect data using sensors or sonar. The analysis unit analyzes the collected data to recognize fish species. For example, the analysis unit recognizes fish species using image analysis technology. The analysis unit can also recognize fish species using audio analysis technology. The estimation unit estimates fish size and school size based on the analyzed data. For example, the estimation unit estimates fish size using a statistical model. The estimation unit can also estimate school size using a machine learning algorithm. The linking unit links with environmental data such as atmospheric pressure, water temperature, light intensity, transparency, turbidity, water color, water current, floating objects, and odors. The linking unit acquires environmental data using, for example, API linkage. The linking unit can also acquire environmental data using database linkage. The recording unit records underwater sounds. The recording unit records sounds using, for example, an underwater microphone. The recording unit can also record sounds using an acoustic sensor. The analysis unit analyzes the recorded sounds and identifies the swimming sounds of a school of bait, the sounds of predation, the sounds of predators, etc. The analysis unit analyzes the sounds using, for example, a sound analysis algorithm. The analysis unit can also analyze the sounds using frequency analysis. The generation unit reproduces the analyzed sounds. The generation unit reproduces sounds using, for example, voice synthesis technology. The generation unit can also reproduce sounds using simulation technology. The learning unit learns combinations of multiple sounds and creates an optimal sound source. The learning unit creates the sound source using, for example, a machine learning algorithm. The learning unit can also create the sound source using a neural network. The providing unit provides the created sound source to efficiently attract fish. The providing unit provides the sound source using, for example, a cloud service. The providing unit can also provide the sound source using a database. As a result, the fish species recognition system according to the embodiment is capable of effectively utilizing repelling sounds and fish-attracting sounds through fish species recognition and automatic learning.

[0030] The collection unit can collect data from underwater cameras installed in payas, etc. The underwater cameras installed in payas, etc. are installed, for example, according to the installation depth and environmental conditions of the installation location. The collection unit, for example, collects images of fish using the underwater cameras. The collection unit can also adjust the installation position of the cameras to collect optimal data. For example, the collection unit installs cameras in places where schools of fish gather in large numbers to efficiently collect data. In this way, collecting data from the underwater cameras improves the accuracy of fish species recognition.

[0031] The analysis unit can analyze the collected data and recognize the fish species. The collected data includes, for example, image data, audio data, and sensor data. The analysis unit can recognize the fish species using, for example, image analysis technology. The analysis unit can also recognize the fish species using audio analysis technology. For example, the analysis unit can identify the fish species based on the shape and color of the fish. The analysis unit can also identify the fish species based on the sound or sound pattern of the fish. In this way, the accuracy of fish species recognition can be improved by analyzing the collected data.

[0032] The estimation unit can estimate the size of the fish and the size of the school based on the analyzed data. The size of the fish and the size of the school include, for example, body length, weight, and school density. The estimation unit estimates the size of the fish using, for example, a statistical model. The estimation unit can also estimate the size of the school using a machine learning algorithm. For example, the estimation unit measures the body length of the fish and estimates its weight. The estimation unit can also analyze the density of the school and estimate the size of the school. In this way, detailed information about the school of fish can be obtained by estimating the size of the fish and the size of the school based on the analyzed data.

[0033] The linking unit can link with environmental data such as air pressure, water temperature, light intensity, transparency, turbidity, water color, water current, floating matter, and odor. The environmental data includes, for example, air pressure, water temperature, light intensity, transparency, turbidity, water color, water current, floating matter, and odor. The linking unit acquires environmental data using, for example, API linking. The linking unit can also acquire environmental data using database linking. For example, the linking unit acquires air pressure and water temperature data from a weather database. The linking unit can also acquire transparency and turbidity data from a water quality sensor. This linking with environmental data improves the accuracy of fish species recognition and estimation.

[0034] The recording unit can record underwater sounds. Underwater sounds include, for example, the swimming sounds of a school of bait, the sounds of predation, and the sounds of predators. The recording unit records sounds using, for example, an underwater microphone. The recording unit can also record sounds using an acoustic sensor. For example, the recording unit records sounds in a specific frequency band. The recording unit can also record sounds over a long period of time. In this way, by recording underwater sounds, it is possible to understand fish behavior and changes in the environment.

[0035] The analysis unit analyzes the recorded sounds and can identify the swimming sounds of schools of bait, the sounds of predation, and the sounds of predators. The swimming sounds of schools of bait, the sounds of predation, and the sounds of predators include, for example, the frequency characteristics and sound patterns of the sounds. The analysis unit analyzes the sounds using, for example, a sound analysis algorithm. The analysis unit can also analyze the sounds using frequency analysis. For example, the analysis unit analyzes the frequency characteristics of the sounds and identifies the sounds of specific fish species. The analysis unit can also analyze the sound patterns and identify the behavior of the fish. In this way, by analyzing the recorded sounds, it is possible to understand the behavior of the fish and changes in the environment in detail.

[0036] The generation unit can reproduce the analyzed sound. To reproduce the analyzed sound, for example, a voice synthesis technique or a simulation technique is used. The generation unit reproduces the sound using, for example, a voice synthesis technique. The generation unit can also reproduce the sound using a simulation technique. For example, the generation unit generates a sound having a specific frequency characteristic. The generation unit can also generate a sound having a specific pattern. In this way, by reproducing the analyzed sound, it is possible to simulate the behavior of fish and changes in the environment.

[0037] The learning unit can learn combinations of multiple sounds and create an optimal sound source. Combinations of multiple sounds include, for example, overlapping sounds and sound sequences. The learning unit creates a sound source using, for example, a machine learning algorithm. The learning unit can also create a sound source using a neural network. For example, the learning unit can create an optimal sound source by combining sounds of different frequencies. The learning unit can also create an optimal sound source by combining different sound intensities. In this way, an optimal sound source can be created by learning combinations of multiple sounds.

[0038] The providing unit provides the created sound source, enabling efficient fish catching. Efficient fish catching includes, for example, a method for evaluating the effectiveness of catching fish and success criteria for catching fish. The providing unit provides the sound source using, for example, a cloud service. The providing unit can also provide the sound source using a database. For example, the providing unit can provide the sound source that is optimal for a specific fish species. The providing unit can also provide the sound source that is optimal for fish of a specific size. In this way, efficient fish catching becomes possible by providing the created sound source.

[0039] The collection unit can dynamically change the installation position of the underwater camera to select the optimal data collection point. For example, if a school of fish moves, the collection unit automatically adjusts the position of the camera to collect data at the optimal point. The collection unit can also change the position of the camera to maximize the efficiency of data collection if environmental conditions change. Furthermore, if a specific fish species appears, the collection unit can move the camera to the optimal position for that fish species. This makes it possible to optimally collect data by dynamically changing the installation position of the underwater camera.

[0040] The collection unit can perform filtering to focus on specific fish species or sizes when collecting data from the underwater camera. For example, if a specific fish species appears, the collection unit can collect data focusing on that fish species. Also, if a fish of a specific size appears, the collection unit can collect data focusing on that size. Furthermore, the collection unit can collect data focusing on a specific combination of fish species and size. In this way, by performing filtering to focus on specific fish species and sizes, necessary data can be collected efficiently.

[0041] The collection unit can automatically adjust the collection method in response to environmental changes when collecting data from the underwater camera. For example, if the water temperature changes, the collection unit adjusts the collection method to collect data. The collection unit can also adjust the collection method to collect data in response to changes in water flow. Furthermore, the collection unit can also adjust the collection method to collect data in response to changes in the amount of light. This enables efficient data collection by automatically adjusting the collection method in response to environmental changes.

[0042] When collecting data from the underwater camera, the collection unit can also collect data in cooperation with other sensors. For example, the collection unit can collect data related to fish movements in cooperation with an acoustic sensor. The collection unit can also collect data related to water temperature in cooperation with a temperature sensor. Furthermore, the collection unit can also collect data related to light intensity in cooperation with a light sensor. This allows for more comprehensive data collection by collecting data in cooperation with other sensors.

[0043] The collection unit can upload data collected by the underwater camera to the cloud in real time, enabling remote monitoring. For example, the collection unit can upload data to the cloud in real time to enable remote monitoring. The collection unit can also upload data to the cloud to make it accessible from multiple devices. Furthermore, the collection unit can upload data to the cloud to enable remote data analysis. This allows remote monitoring by uploading data to the cloud in real time.

[0044] The collection unit can evaluate the quality of the collected data when collecting data from the underwater camera and automatically exclude low-quality data. For example, the collection unit evaluates the quality of the collected data in real time and excludes low-quality data. The collection unit can also evaluate the quality of the collected data and exclude noisy data. Furthermore, the collection unit can evaluate the quality of the collected data and exclude incomplete data. As a result, by evaluating the quality of the collected data and excluding low-quality data, the accuracy of the data is improved.

[0045] During analysis, the analysis unit can optimize the analysis algorithm by comparing it with past data to improve the accuracy of identifying fish species. The analysis unit, for example, compares past data with current data and optimizes the analysis algorithm. The analysis unit can also adjust the parameters of the analysis algorithm based on the past data. Furthermore, the analysis unit can integrate past data and current data to improve the accuracy of analysis. In this way, optimizing the analysis algorithm by comparing it with past data improves the accuracy of identifying fish species.

[0046] During the analysis, the analysis unit analyzes the movement and behavior patterns of the fish, which can be used to identify the fish species. For example, the analysis unit analyzes the movement patterns of the fish, which can be used to identify the fish species. The analysis unit can also analyze the behavior patterns of the fish, which can be used to identify the fish species. Furthermore, the analysis unit can analyze the swimming speed and direction of the fish, which can be used to identify the fish species. In this way, by analyzing the movement and behavior patterns of the fish, the accuracy of identifying the fish species is improved.

[0047] During analysis, the analysis unit can integrate environmental data and fish species data to perform more accurate analysis. The analysis unit, for example, integrates environmental data and fish species data to improve analysis accuracy. The analysis unit can also analyze fish species data based on environmental data. Furthermore, the analysis unit can combine fish species data and environmental data to perform more accurate analysis. In this way, by integrating environmental data and fish species data, analysis accuracy is improved.

[0048] During analysis, the analysis unit can improve the accuracy of the analysis by linking with other data sources. The analysis unit can improve the accuracy of the analysis by linking with satellite data, for example. The analysis unit can also improve the accuracy of the analysis by linking with meteorological data. Furthermore, the analysis unit can also improve the accuracy of the analysis by linking with other data sources. As a result, the analysis accuracy is improved by linking with other data sources.

[0049] During analysis, the analysis unit can combine different analysis algorithms, compare the analysis results, and select the optimal result. The analysis unit, for example, combines different analysis algorithms and compares the analysis results. The analysis unit can also compare the analysis results and select the optimal result. Furthermore, the analysis unit can combine different analysis algorithms to improve the analysis accuracy. As a result, the analysis accuracy is improved by combining different analysis algorithms.

[0050] During the analysis, the analysis unit can visualize the analysis results to enable the user to intuitively understand them. For example, the analysis unit can visualize the analysis results in graphs or charts to enable the user to intuitively understand them. The analysis unit can also display the analysis results on a map to enable the user to intuitively understand them. Furthermore, the analysis unit can visualize the analysis results in a 3D model to enable the user to intuitively understand them. In this way, by visualizing the analysis results, the user can intuitively understand them.

[0051] During estimation, the estimation unit can estimate the size and school size by taking into account the growth pattern of the fish. For example, the estimation unit estimates the size based on the growth pattern of the fish. The estimation unit can also estimate the school size based on the growth pattern of the fish. Furthermore, the estimation unit can estimate the size of individual fish by taking into account the growth pattern of the fish. In this way, by taking the growth pattern of the fish into account, the accuracy of estimating the size and school size is improved.

[0052] The estimation unit can analyze the behavioral patterns of the fish during estimation to improve the estimation accuracy. For example, the estimation unit analyzes the behavioral patterns of the fish to improve the estimation accuracy. The estimation unit can also analyze the swimming speed and direction of the fish to improve the estimation accuracy. Furthermore, the estimation unit can analyze the predatory behavior of the fish to improve the estimation accuracy. In this way, the estimation accuracy is improved by analyzing the behavioral patterns of the fish.

[0053] During estimation, the estimation unit can perform more accurate estimation in cooperation with environmental data. The estimation unit, for example, improves estimation accuracy in cooperation with environmental data. The estimation unit can also improve estimation accuracy based on environmental data. Furthermore, the estimation unit can improve estimation accuracy by combining environmental data and fish species data. In this way, estimation accuracy is improved by cooperation with environmental data.

[0054] During estimation, the estimation unit can improve the estimation accuracy in cooperation with other data sources. For example, the estimation unit can improve the estimation accuracy in cooperation with acoustic data. The estimation unit can also improve the estimation accuracy in cooperation with temperature data. Furthermore, the estimation unit can also improve the estimation accuracy in cooperation with other data sources. As a result, the estimation accuracy is improved by cooperation with other data sources.

[0055] During estimation, the estimation unit can combine different estimation algorithms, compare the estimation results, and select the optimal result. For example, the estimation unit combines different estimation algorithms and compares the estimation results. The estimation unit can also compare the estimation results and select the optimal result. Furthermore, the estimation unit can improve estimation accuracy by combining different estimation algorithms. As a result, the estimation accuracy is improved by combining different estimation algorithms.

[0056] The estimation unit can visualize the estimation result during estimation, allowing the user to intuitively understand it. For example, the estimation unit can visualize the estimation result in a graph or chart, allowing the user to intuitively understand it. The estimation unit can also display the estimation result on a map, allowing the user to intuitively understand it. Furthermore, the estimation unit can visualize the estimation result in a 3D model, allowing the user to intuitively understand it. In this way, by visualizing the estimation result, the user can intuitively understand it.

[0057] During collaboration, the collaboration unit can monitor fluctuations in the environmental data in real time and improve the accuracy of the data. For example, the collaboration unit can monitor fluctuations in the environmental data in real time and improve the accuracy of the data. The collaboration unit can also monitor fluctuations in the environmental data and detect abnormal values. Furthermore, the collaboration unit can improve the accuracy of the data based on the fluctuations in the environmental data. In this way, by monitoring fluctuations in the environmental data in real time, the accuracy of the data is improved.

[0058] During linking, the linking unit can integrate different environmental data sources to perform more comprehensive data linking. For example, the linking unit integrates different environmental data sources to perform comprehensive data linking. The linking unit can also integrate environmental data sources to improve data accuracy. Furthermore, the linking unit can combine different data sources to perform comprehensive data linking. As a result, by integrating different environmental data sources, data accuracy is improved.

[0059] During collaboration, the collaboration unit can automatically detect abnormal values ​​in the environmental data and ensure the reliability of the data. For example, the collaboration unit can automatically detect abnormal values ​​in the environmental data and ensure the reliability of the data. The collaboration unit can also detect abnormal values ​​and improve the accuracy of the data. Furthermore, the collaboration unit can also ensure the reliability of the data based on the abnormal values ​​in the environmental data. In this way, the reliability of the data is improved by automatically detecting abnormal values ​​in the environmental data.

[0060] During collaboration, the collaboration unit can improve the accuracy of the data by collaborating with other data sources. The collaboration unit can improve the accuracy of the data by collaborating with satellite data, for example. The collaboration unit can also improve the accuracy of the data by collaborating with meteorological data. Furthermore, the collaboration unit can also improve the accuracy of the data by collaborating with other data sources. As a result, the accuracy of the data is improved by collaborating with other data sources.

[0061] The linking unit can automatically convert different data formats during linking to ensure data compatibility. For example, the linking unit can automatically convert different data formats to ensure data compatibility. The linking unit can also convert data formats to improve data accuracy. Furthermore, the linking unit can combine different data formats to ensure data compatibility. In this way, data compatibility is ensured by automatically converting different data formats.

[0062] The linking unit can visualize the linked data to enable the user to intuitively understand it. For example, the linking unit can visualize the linked data in a graph or chart to enable the user to intuitively understand it. The linking unit can also display the linked data on a map to enable the user to intuitively understand it. Furthermore, the linking unit can visualize the linked data in a 3D model to enable the user to intuitively understand it. In this way, by visualizing the linked data, the user can intuitively understand it.

[0063] The recording unit can filter sounds that focus on a specific fish species or size during recording. For example, the recording unit can record sounds that focus on sounds emitted by a specific fish species. The recording unit can also record sounds that focus on sounds emitted by fish of a specific size. Furthermore, the recording unit can also record sounds that focus on a specific combination of fish species and size. In this way, necessary sounds can be efficiently recorded by filtering sounds that focus on a specific fish species or size.

[0064] The recording unit can automatically adjust the recording method according to environmental changes during recording. For example, if the water temperature changes, the recording unit adjusts the recording method to record sound. The recording unit can also adjust the recording method to record sound if the water flow changes. Furthermore, the recording unit can also adjust the recording method to record sound if the amount of light changes. This makes it possible to efficiently record sound by automatically adjusting the recording method according to environmental changes.

[0065] The recording unit can evaluate the quality of the sound during recording and automatically exclude low-quality sounds. For example, the recording unit evaluates the quality of the recorded sound in real time and excludes low-quality sounds. The recording unit can also evaluate the quality of the recorded sound and exclude noisy sounds. Furthermore, the recording unit can evaluate the quality of the recorded sound and exclude imperfect sounds. As a result, the accuracy of the recorded data is improved by evaluating the quality of the sound and excluding low-quality sounds.

[0066] During recording, the recording unit can record sounds in cooperation with other sensors. For example, the recording unit can record sounds related to fish movements in cooperation with an acoustic sensor. The recording unit can also record sounds related to water temperature in cooperation with a temperature sensor. Furthermore, the recording unit can also record sounds related to light intensity in cooperation with a light sensor. This makes it possible to record more comprehensive sounds by recording sounds in cooperation with other sensors.

[0067] The recording unit can upload the sound to the cloud in real time during recording, enabling monitoring from a remote location. For example, the recording unit can upload the sound to the cloud in real time and monitor from a remote location. The recording unit can also upload the sound to the cloud to make it accessible from multiple devices. Furthermore, the recording unit can also upload the sound to the cloud to enable data analysis from a remote location. This makes it possible to monitor from a remote location by uploading the sound to the cloud in real time.

[0068] The recording unit can evaluate the quality of the recorded data and automatically exclude low-quality data. For example, the recording unit evaluates the quality of the recorded data in real time and excludes low-quality data. The recording unit can also evaluate the quality of the recorded data and exclude noisy data. Furthermore, the recording unit can evaluate the quality of the recorded data and exclude incomplete data. As a result, by evaluating the quality of the recorded data and excluding low-quality data, the accuracy of the data is improved.

[0069] During the analysis, the analysis unit can analyze the characteristics of the sound in detail and identify a specific fish species or behavior pattern. The analysis unit can, for example, analyze the frequency characteristics of the sound and identify a specific fish species. The analysis unit can also analyze changes in the sound over time and identify the behavior pattern of the fish. Furthermore, the analysis unit can analyze the intensity of the sound and identify the activity level of the fish. In this way, by analyzing the characteristics of the sound in detail, it is possible to identify a specific fish species or behavior pattern.

[0070] During analysis, the analysis unit can optimize the analysis algorithm by comparing it with past data. For example, the analysis unit compares past data with current data and optimizes the analysis algorithm. The analysis unit can also adjust the parameters of the analysis algorithm based on the past data. Furthermore, the analysis unit can integrate past data and current data to improve the accuracy of the analysis. In this way, the analysis accuracy is improved by optimizing the analysis algorithm by comparing it with past data.

[0071] The analysis unit can integrate environmental data and sound data during analysis to perform more accurate analysis. For example, the analysis unit integrates environmental data and sound data to improve the accuracy of analysis. The analysis unit can also analyze sound data based on environmental data. Furthermore, the analysis unit can combine sound data and environmental data to perform more accurate analysis. In this way, the integration of environmental data and sound data improves the accuracy of analysis.

[0072] During analysis, the analysis unit can improve the accuracy of the analysis by linking with other data sources. For example, the analysis unit can improve the accuracy of the analysis by linking with acoustic data. The analysis unit can also improve the accuracy of the analysis by linking with temperature data. Furthermore, the analysis unit can also improve the accuracy of the analysis by linking with other data sources. As a result, the analysis accuracy is improved by linking with other data sources.

[0073] During analysis, the analysis unit can combine different analysis algorithms, compare the analysis results, and select the optimal result. For example, the analysis unit combines different analysis algorithms and compares the analysis results. The analysis unit can also compare the analysis results and select the optimal result. Furthermore, the analysis unit can combine different analysis algorithms to improve the accuracy of the analysis. As a result, the accuracy of the analysis is improved by combining different analysis algorithms.

[0074] During the analysis, the analysis unit can visualize the analysis results to enable the user to intuitively understand them. For example, the analysis unit can visualize the analysis results in graphs or charts to enable the user to intuitively understand them. The analysis unit can also display the analysis results on a map to enable the user to intuitively understand them. Furthermore, the analysis unit can visualize the analysis results in a 3D model to enable the user to intuitively understand them. In this way, by visualizing the analysis results, the user can intuitively understand them.

[0075] The generation unit can refer to past data when generating a sound to generate an optimal sound for a specific fish species or size. For example, the generation unit generates an optimal sound for a specific fish species based on past data. The generation unit can also generate an optimal sound for a specific size of fish based on past data. Furthermore, the generation unit can also generate an optimal sound for a specific combination of fish species and size by referring to past data. In this way, by referring to past data, it is possible to generate an optimal sound for a specific fish species or size.

[0076] During generation, the generation unit can try different combinations of sounds to generate the optimal sound. For example, the generation unit can combine sounds of different frequencies to generate the optimal sound. The generation unit can also combine different sound intensities to generate the optimal sound. Furthermore, the generation unit can combine different sound patterns to generate the optimal sound. In this way, the optimal sound can be generated by trying different combinations of sounds.

[0077] The generation unit can generate more effective sounds in cooperation with environmental data during generation. The generation unit generates optimal sounds based on, for example, environmental data. The generation unit can also generate effective sounds by combining environmental data and sound data. Furthermore, the generation unit can also generate optimal sounds for specific situations by referring to the environmental data. In this way, more effective sounds can be generated by cooperation with environmental data.

[0078] During generation, the generation unit can improve generation accuracy in cooperation with other data sources. The generation unit can improve generation accuracy, for example, in cooperation with acoustic data. The generation unit can also improve generation accuracy in cooperation with temperature data. Furthermore, the generation unit can also improve generation accuracy in cooperation with other data sources. As a result, by cooperation with other data sources, generation accuracy is improved.

[0079] During generation, the generation unit can combine different generation algorithms, compare the generation results, and select the optimal result. For example, the generation unit combines different generation algorithms and compares the generation results. The generation unit can also compare the generation results and select the optimal result. Furthermore, the generation unit can improve generation accuracy by combining different generation algorithms. As a result, generation accuracy is improved by combining different generation algorithms.

[0080] The generation unit can visualize the generated results to enable the user to intuitively understand. For example, the generation unit can visualize the generated results in a graph or chart to enable the user to intuitively understand. The generation unit can also display the generated results on a map to enable the user to intuitively understand. Furthermore, the generation unit can visualize the generated results in a 3D model to enable the user to intuitively understand. In this way, by visualizing the generated results, the user can intuitively understand.

[0081] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit optimizes the learning algorithm based on, for example, past learning data. The learning unit can also compare past learning data with current data and adjust the learning algorithm. Furthermore, the learning unit can also improve learning accuracy by referring to past learning data. In this way, the accuracy of the learning algorithm is improved by referring to past learning data.

[0082] During learning, the learning unit can try different sound combinations and learn the optimal sound source. For example, the learning unit combines sounds of different frequencies to learn the optimal sound source. The learning unit can also combine different sound intensities to learn the optimal sound source. Furthermore, the learning unit can also combine different sound patterns to learn the optimal sound source. In this way, the optimal sound source can be learned by trying different sound combinations.

[0083] During learning, the learning unit can perform more effective learning in cooperation with environmental data. The learning unit, for example, learns the optimal sound source based on the environmental data. The learning unit can also perform effective learning by combining the environmental data and sound data. Furthermore, the learning unit can also learn the optimal sound source for a specific situation by referring to the environmental data. This improves learning accuracy by working with the environmental data.

[0084] During learning, the learning unit can improve the learning accuracy in cooperation with other data sources. For example, the learning unit improves the learning accuracy in cooperation with acoustic data. The learning unit can also improve the learning accuracy in cooperation with temperature data. Furthermore, the learning unit can also improve the learning accuracy in cooperation with other data sources. As a result, the learning accuracy is improved by cooperation with other data sources.

[0085] During learning, the learning unit can combine different learning algorithms, compare the learning results, and select the optimal result. For example, the learning unit combines different learning algorithms and compares the learning results. The learning unit can also compare the learning results and select the optimal result. Furthermore, the learning unit can combine different learning algorithms to improve learning accuracy. As a result, by combining different learning algorithms, learning accuracy is improved.

[0086] The learning unit can visualize the learning results to enable the user to intuitively understand. For example, the learning unit can visualize the learning results in graphs or charts to enable the user to intuitively understand. The learning unit can also display the learning results on a map to enable the user to intuitively understand. Furthermore, the learning unit can visualize the learning results in a 3D model to enable the user to intuitively understand. In this way, visualizing the learning results allows the user to intuitively understand.

[0087] The providing unit can refer to past data when providing the sound source that is optimal for a specific fish species or size. For example, the providing unit provides the sound source that is optimal for a specific fish species based on past data. The providing unit can also provide the sound source that is optimal for a fish of a specific size based on past data. Furthermore, the providing unit can also provide the sound source that is optimal for a specific combination of fish species and size by referring to past data. In this way, the sound source that is optimal for a specific fish species or size can be provided by referring to past data.

[0088] The providing unit can try different combinations of sound sources at the time of providing and provide the optimal sound source. For example, the providing unit can combine sounds of different frequencies to provide the optimal sound source. The providing unit can also combine different sound intensities to provide the optimal sound source. Furthermore, the providing unit can also combine different sound patterns to provide the optimal sound source. In this way, the optimal sound source can be provided by trying different combinations of sound sources.

[0089] The providing unit can provide a more effective sound source in cooperation with the environmental data when providing the sound source. For example, the providing unit provides the optimal sound source based on the environmental data. The providing unit can also provide an effective sound source by combining the environmental data and the sound data. Furthermore, the providing unit can also provide the optimal sound source for a specific situation by referring to the environmental data. In this way, a more effective sound source can be provided by coordinating with the environmental data.

[0090] The providing unit can improve the accuracy of providing data by cooperating with other data sources during the providing process. The providing unit can improve the accuracy of providing data by cooperating with acoustic data, for example. The providing unit can also improve the accuracy of providing data by cooperating with temperature data. Furthermore, the providing unit can also improve the accuracy of providing data by cooperating with other data sources. As a result, the accuracy of providing data is improved by cooperating with other data sources.

[0091] At the time of provision, the providing unit can combine different provision algorithms to compare the provision results and select the optimal result. The providing unit, for example, combines different provision algorithms to compare the provision results. The providing unit can also compare the provision results and select the optimal result. Furthermore, the providing unit can combine different provision algorithms to improve provision accuracy. As a result, provision accuracy is improved by combining different provision algorithms.

[0092] The providing unit can visualize the provided results to enable the user to intuitively understand them. For example, the providing unit can visualize the provided results in a graph or chart to enable the user to intuitively understand them. The providing unit can also display the provided results on a map to enable the user to intuitively understand them. Furthermore, the providing unit can visualize the provided results in a 3D model to enable the user to intuitively understand them. In this way, by visualizing the provided results, the user can intuitively understand them.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The analysis unit can also analyze the behavioral patterns of fish to help identify fish species. For example, the analysis unit can analyze the swimming speed and direction of fish to identify the behavioral patterns of specific fish species. The analysis unit can also analyze the feeding behavior and school formation patterns of fish to help identify fish species. Furthermore, the analysis unit can analyze the migration paths of fish to identify the habitats of specific fish species. In this way, analyzing fish behavioral patterns improves the accuracy of identifying fish species.

[0095] The collection unit can also collect data in cooperation with other sensors. For example, it can collect data on fish movements in cooperation with an acoustic sensor. The collection unit can also collect data on water temperature in cooperation with a temperature sensor. Furthermore, the collection unit can also collect data on light intensity in cooperation with a light sensor. This allows for more comprehensive data collection by collecting data in cooperation with other sensors.

[0096] The estimation unit can also estimate the size and school size of fish by taking into account their growth patterns. For example, the size is estimated based on the growth patterns of fish. The estimation unit can also estimate the school size based on the growth patterns of fish. Furthermore, the estimation unit can estimate the size of individual fish by taking into account their growth patterns. In this way, by taking into account the growth patterns of fish, the accuracy of estimating the size and school size is improved.

[0097] The linking unit can also monitor fluctuations in the environmental data in real time to improve the accuracy of the data. For example, the linking unit can monitor fluctuations in the environmental data in real time to detect abnormal values. The linking unit can also improve the accuracy of the data based on the fluctuations in the environmental data. Furthermore, the linking unit can monitor fluctuations in the environmental data to ensure the reliability of the data. In this way, by monitoring fluctuations in the environmental data in real time, the accuracy of the data is improved.

[0098] The recording unit can also evaluate the quality of the recorded data and automatically exclude low-quality data. For example, the quality of the recorded data can be evaluated in real time and low-quality data can be excluded. The recording unit can also evaluate the quality of the recorded data and exclude noisy data. Furthermore, the recording unit can evaluate the quality of the recorded data and exclude incomplete data. As a result, by evaluating the quality of the recorded data and excluding low-quality data, the accuracy of the data can be improved.

[0099] The processing flow of the first embodiment will be briefly explained below.

[0100] Step 1: The collection unit collects data for recognizing fish species. For example, the collection unit collects data from an underwater camera installed in a paya or the like. The collection unit can also collect data using sensors or sonar. Step 2: The analysis unit analyzes the collected data and recognizes the fish species. The analysis unit may recognize the fish species using, for example, image analysis technology. The analysis unit may also recognize the fish species using audio analysis technology. Step 3: The estimation unit estimates the size of the fish and the size of the school based on the analyzed data. For example, the estimation unit estimates the size of the fish using a statistical model. The estimation unit can also estimate the size of the school using a machine learning algorithm. Step 4: The linking unit links with environmental data such as air pressure, water temperature, light intensity, transparency, turbidity, water color, water current, floating matter, and odor. The linking unit acquires the environmental data using, for example, API linking. The linking unit can also acquire the environmental data using database linking. Step 5: The recording unit records underwater sounds. The recording unit may record sounds using, for example, an underwater microphone. Alternatively, the recording unit may record sounds using an acoustic sensor. Step 6: The analysis unit analyzes the recorded sounds and identifies the swimming sounds of a school of bait, the sounds of predation, the sounds of predators, etc. The analysis unit analyzes the sounds using, for example, a sound analysis algorithm. The analysis unit can also analyze the sounds using frequency analysis. Step 7: The generator reproduces the analyzed sound. For example, the generator reproduces the sound using a voice synthesis technique. Alternatively, the generator can reproduce the sound using a simulation technique. Step 8: The learning unit learns combinations of multiple sounds and creates an optimal sound source. The learning unit creates the sound source using, for example, a machine learning algorithm. The learning unit can also create the sound source using a neural network. Step 9: The providing unit provides the created sound source to efficiently attract fish. The providing unit provides the sound source using, for example, a cloud service. The providing unit can also provide the sound source using a database.

[0101] (Example 2) A system according to an embodiment of the present invention effectively utilizes repellent and fish-attracting sounds through fish species recognition and automatic learning. This system uses identification AI to analyze data from underwater cameras installed in fishing basins and other facilities to identify fish species. Next, it estimates the size of individual fish, the size of schools, and the average size of fish. Furthermore, it links this data with environmental data such as air pressure, water temperature, light intensity, transparency, turbidity, water color, water current, floating objects, and odors. Underwater sounds are recorded and analyzed, including the sounds of bait schools swimming, predation sounds, and predator sounds. These sounds are reproduced using a generation AI, and their fish-attracting and repellent effects for each fish species and size are verified using an underwater camera. Combinations of multiple sounds are learned, and optimal sound sources are created for each fish species and size. Finally, the sound source data is used to efficiently attract fish. For example, sounds to attract or repel specific fish species can be generated and applied to actual fishing activities. This enables the system to effectively utilize repellent and fish-attracting sounds through fish species recognition and automatic learning. For example, sounds that attract specific fish species or that repel specific fish species can be generated, and this can be applied to actual fishing activities.

[0102] The fish species recognition system according to the embodiment includes a collection unit, an analysis unit, an estimation unit, a linking unit, a recording unit, an analysis unit, a generation unit, a learning unit, and a provision unit. The collection unit collects data for recognizing fish species. For example, the collection unit collects data from underwater cameras installed in a fishing port or the like. The collection unit can also collect data using sensors or sonar. The analysis unit analyzes the collected data to recognize fish species. For example, the analysis unit recognizes fish species using image analysis technology. The analysis unit can also recognize fish species using audio analysis technology. The estimation unit estimates fish size and school size based on the analyzed data. For example, the estimation unit estimates fish size using a statistical model. The estimation unit can also estimate school size using a machine learning algorithm. The linking unit links with environmental data such as atmospheric pressure, water temperature, light intensity, transparency, turbidity, water color, water current, floating objects, and odors. The linking unit acquires environmental data using, for example, API linkage. The linking unit can also acquire environmental data using database linkage. The recording unit records underwater sounds. The recording unit records sounds using, for example, an underwater microphone. The recording unit can also record sounds using an acoustic sensor. The analysis unit analyzes the recorded sounds and identifies the swimming sounds of a school of bait, the sounds of predation, the sounds of predators, etc. The analysis unit analyzes the sounds using, for example, a sound analysis algorithm. The analysis unit can also analyze the sounds using frequency analysis. The generation unit reproduces the analyzed sounds. The generation unit reproduces sounds using, for example, voice synthesis technology. The generation unit can also reproduce sounds using simulation technology. The learning unit learns combinations of multiple sounds and creates an optimal sound source. The learning unit creates the sound source using, for example, a machine learning algorithm. The learning unit can also create the sound source using a neural network. The providing unit provides the created sound source to efficiently attract fish. The providing unit provides the sound source using, for example, a cloud service. The providing unit can also provide the sound source using a database. As a result, the fish species recognition system according to the embodiment is capable of effectively utilizing repelling sounds and fish-attracting sounds through fish species recognition and automatic learning.

[0103] The collection unit can collect data from underwater cameras installed in payas, etc. The underwater cameras installed in payas, etc. are installed, for example, according to the installation depth and environmental conditions of the installation location. The collection unit, for example, collects images of fish using the underwater cameras. The collection unit can also adjust the installation position of the cameras to collect optimal data. For example, the collection unit installs cameras in places where schools of fish gather in large numbers to efficiently collect data. In this way, collecting data from the underwater cameras improves the accuracy of fish species recognition.

[0104] The analysis unit can analyze the collected data and recognize the fish species. The collected data includes, for example, image data, audio data, and sensor data. The analysis unit can recognize the fish species using, for example, image analysis technology. The analysis unit can also recognize the fish species using audio analysis technology. For example, the analysis unit can identify the fish species based on the shape and color of the fish. The analysis unit can also identify the fish species based on the sound or sound pattern of the fish. In this way, the accuracy of fish species recognition can be improved by analyzing the collected data.

[0105] The estimation unit can estimate the size of the fish and the size of the school based on the analyzed data. The size of the fish and the size of the school include, for example, body length, weight, and school density. The estimation unit estimates the size of the fish using, for example, a statistical model. The estimation unit can also estimate the size of the school using a machine learning algorithm. For example, the estimation unit measures the body length of the fish and estimates its weight. The estimation unit can also analyze the density of the school and estimate the size of the school. In this way, detailed information about the school of fish can be obtained by estimating the size of the fish and the size of the school based on the analyzed data.

[0106] The linking unit can link with environmental data such as air pressure, water temperature, light intensity, transparency, turbidity, water color, water current, floating matter, and odor. The environmental data includes, for example, air pressure, water temperature, light intensity, transparency, turbidity, water color, water current, floating matter, and odor. The linking unit acquires environmental data using, for example, API linking. The linking unit can also acquire environmental data using database linking. For example, the linking unit acquires air pressure and water temperature data from a weather database. The linking unit can also acquire transparency and turbidity data from a water quality sensor. This linking with environmental data improves the accuracy of fish species recognition and estimation.

[0107] The recording unit can record underwater sounds. Underwater sounds include, for example, the swimming sounds of a school of bait, the sounds of predation, and the sounds of predators. The recording unit records sounds using, for example, an underwater microphone. The recording unit can also record sounds using an acoustic sensor. For example, the recording unit records sounds in a specific frequency band. The recording unit can also record sounds over a long period of time. In this way, by recording underwater sounds, it is possible to understand fish behavior and changes in the environment.

[0108] The analysis unit analyzes the recorded sounds and can identify the swimming sounds of schools of bait, the sounds of predation, and the sounds of predators. The swimming sounds of schools of bait, the sounds of predation, and the sounds of predators include, for example, the frequency characteristics and sound patterns of the sounds. The analysis unit analyzes the sounds using, for example, a sound analysis algorithm. The analysis unit can also analyze the sounds using frequency analysis. For example, the analysis unit analyzes the frequency characteristics of the sounds and identifies the sounds of specific fish species. The analysis unit can also analyze the sound patterns and identify the behavior of the fish. In this way, by analyzing the recorded sounds, it is possible to understand the behavior of the fish and changes in the environment in detail.

[0109] The generation unit can reproduce the analyzed sound. To reproduce the analyzed sound, for example, a voice synthesis technique or a simulation technique is used. The generation unit reproduces the sound using, for example, a voice synthesis technique. The generation unit can also reproduce the sound using a simulation technique. For example, the generation unit generates a sound having a specific frequency characteristic. The generation unit can also generate a sound having a specific pattern. In this way, by reproducing the analyzed sound, it is possible to simulate the behavior of fish and changes in the environment.

[0110] The learning unit can learn combinations of multiple sounds and create an optimal sound source. Combinations of multiple sounds include, for example, overlapping sounds and sound sequences. The learning unit creates a sound source using, for example, a machine learning algorithm. The learning unit can also create a sound source using a neural network. For example, the learning unit can create an optimal sound source by combining sounds of different frequencies. The learning unit can also create an optimal sound source by combining different sound intensities. In this way, an optimal sound source can be created by learning combinations of multiple sounds.

[0111] The providing unit provides the created sound source, enabling efficient fish catching. Efficient fish catching includes, for example, a method for evaluating the effectiveness of catching fish and success criteria for catching fish. The providing unit provides the sound source using, for example, a cloud service. The providing unit can also provide the sound source using a database. For example, the providing unit can provide the sound source that is optimal for a specific fish species. The providing unit can also provide the sound source that is optimal for fish of a specific size. In this way, efficient fish catching becomes possible by providing the created sound source.

[0112] The collection unit can estimate the user's emotions and adjust the timing of data collection by the underwater camera based on the user's emotions. The collection unit, for example, recognizes the user's facial expressions and estimates the emotions. The collection unit can also estimate the user's emotions using voice analysis technology. For example, if the user is excited, the collection unit can increase the collection frequency and collect data in real time. If the user is relaxed, the collection unit can also collect data at a lower frequency. Furthermore, if the user is feeling stressed, the collection unit can adjust the collection timing to reduce the burden on the user. This enables efficient data collection by adjusting the data collection timing based on the user's emotions.

[0113] The collection unit can dynamically change the installation position of the underwater camera to select the optimal data collection point. For example, if a school of fish moves, the collection unit automatically adjusts the position of the camera to collect data at the optimal point. The collection unit can also change the position of the camera to maximize the efficiency of data collection if environmental conditions change. Furthermore, if a specific fish species appears, the collection unit can move the camera to the optimal position for that fish species. This makes it possible to optimally collect data by dynamically changing the installation position of the underwater camera.

[0114] The collection unit can perform filtering to focus on specific fish species or sizes when collecting data from the underwater camera. For example, if a specific fish species appears, the collection unit can collect data focusing on that fish species. Also, if a fish of a specific size appears, the collection unit can collect data focusing on that size. Furthermore, the collection unit can collect data focusing on a specific combination of fish species and size. In this way, by performing filtering to focus on specific fish species and sizes, necessary data can be collected efficiently.

[0115] The collection unit can automatically adjust the collection method in response to environmental changes when collecting data from the underwater camera. For example, if the water temperature changes, the collection unit adjusts the collection method to collect data. The collection unit can also adjust the collection method to collect data in response to changes in water flow. Furthermore, the collection unit can also adjust the collection method to collect data in response to changes in the amount of light. This enables efficient data collection by automatically adjusting the collection method in response to environmental changes.

[0116] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit, for example, recognizes the user's facial expressions and estimates the emotions. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit prioritizes collecting important data when the user is excited. The collection unit can also collect detailed data when the user is relaxed. Furthermore, the collection unit can collect simplified data when the user is feeling stressed. This enables efficient data collection by determining the priority of data based on the user's emotions.

[0117] When collecting data from the underwater camera, the collection unit can also collect data in cooperation with other sensors. For example, the collection unit can collect data related to fish movements in cooperation with an acoustic sensor. The collection unit can also collect data related to water temperature in cooperation with a temperature sensor. Furthermore, the collection unit can also collect data related to light intensity in cooperation with a light sensor. This allows for more comprehensive data collection by collecting data in cooperation with other sensors.

[0118] The collection unit can upload data collected by the underwater camera to the cloud in real time, enabling remote monitoring. For example, the collection unit can upload data to the cloud in real time to enable remote monitoring. The collection unit can also upload data to the cloud to make it accessible from multiple devices. Furthermore, the collection unit can upload data to the cloud to enable remote data analysis. This allows remote monitoring by uploading data to the cloud in real time.

[0119] The collection unit can evaluate the quality of the collected data when collecting data from the underwater camera and automatically exclude low-quality data. For example, the collection unit evaluates the quality of the collected data in real time and excludes low-quality data. The collection unit can also evaluate the quality of the collected data and exclude noisy data. Furthermore, the collection unit can evaluate the quality of the collected data and exclude incomplete data. As a result, by evaluating the quality of the collected data and excluding low-quality data, the accuracy of the data is improved.

[0120] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, recognizes the user's facial expressions and estimates the emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the analysis results based on the user's emotions, it is possible to provide a display that is easy for the user to understand.

[0121] During analysis, the analysis unit can optimize the analysis algorithm by comparing it with past data to improve the accuracy of identifying fish species. The analysis unit, for example, compares past data with current data and optimizes the analysis algorithm. The analysis unit can also adjust the parameters of the analysis algorithm based on the past data. Furthermore, the analysis unit can integrate past data and current data to improve the accuracy of analysis. In this way, optimizing the analysis algorithm by comparing it with past data improves the accuracy of identifying fish species.

[0122] During the analysis, the analysis unit analyzes the movement and behavior patterns of the fish, which can be used to identify the fish species. For example, the analysis unit analyzes the movement patterns of the fish, which can be used to identify the fish species. The analysis unit can also analyze the behavior patterns of the fish, which can be used to identify the fish species. Furthermore, the analysis unit can analyze the swimming speed and direction of the fish, which can be used to identify the fish species. In this way, by analyzing the movement and behavior patterns of the fish, the accuracy of identifying the fish species is improved.

[0123] During analysis, the analysis unit can integrate environmental data and fish species data to perform more accurate analysis. The analysis unit, for example, integrates environmental data and fish species data to improve analysis accuracy. The analysis unit can also analyze fish species data based on environmental data. Furthermore, the analysis unit can combine fish species data and environmental data to perform more accurate analysis. In this way, by integrating environmental data and fish species data, analysis accuracy is improved.

[0124] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user emotions. The analysis unit, for example, recognizes the user's facial expressions and estimates the emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can prioritize displaying important analysis results when the user is excited. The analysis unit can also display detailed analysis results when the user is relaxed. Furthermore, the analysis unit can display simplified analysis results when the user is stressed. In this way, by determining the priority of analysis results based on the user's emotions, important analysis results can be displayed preferentially.

[0125] During analysis, the analysis unit can improve the accuracy of the analysis by linking with other data sources. The analysis unit can improve the accuracy of the analysis by linking with satellite data, for example. The analysis unit can also improve the accuracy of the analysis by linking with meteorological data. Furthermore, the analysis unit can also improve the accuracy of the analysis by linking with other data sources. As a result, the analysis accuracy is improved by linking with other data sources.

[0126] During analysis, the analysis unit can combine different analysis algorithms, compare the analysis results, and select the optimal result. The analysis unit, for example, combines different analysis algorithms and compares the analysis results. The analysis unit can also compare the analysis results and select the optimal result. Furthermore, the analysis unit can combine different analysis algorithms to improve the analysis accuracy. As a result, the analysis accuracy is improved by combining different analysis algorithms.

[0127] During the analysis, the analysis unit can visualize the analysis results to enable the user to intuitively understand them. For example, the analysis unit can visualize the analysis results in graphs or charts to enable the user to intuitively understand them. The analysis unit can also display the analysis results on a map to enable the user to intuitively understand them. Furthermore, the analysis unit can visualize the analysis results in a 3D model to enable the user to intuitively understand them. In this way, by visualizing the analysis results, the user can intuitively understand them.

[0128] The estimation unit can estimate the user's emotions and adjust the display method of the estimation results based on the estimated user emotions. The estimation unit, for example, recognizes the user's facial expressions and estimates the emotions. The estimation unit can also estimate the user's emotions using voice analysis technology. For example, if the user is nervous, the estimation unit can provide a simple, highly visible display method. If the user is relaxed, the estimation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the estimation unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the estimation results based on the user's emotions, it is possible to provide a display that is easy for the user to understand.

[0129] During estimation, the estimation unit can estimate the size and school size by taking into account the growth pattern of the fish. For example, the estimation unit estimates the size based on the growth pattern of the fish. The estimation unit can also estimate the school size based on the growth pattern of the fish. Furthermore, the estimation unit can estimate the size of individual fish by taking into account the growth pattern of the fish. In this way, by taking the growth pattern of the fish into account, the accuracy of estimating the size and school size is improved.

[0130] The estimation unit can analyze the behavioral patterns of the fish during estimation to improve the estimation accuracy. For example, the estimation unit analyzes the behavioral patterns of the fish to improve the estimation accuracy. The estimation unit can also analyze the swimming speed and direction of the fish to improve the estimation accuracy. Furthermore, the estimation unit can analyze the predatory behavior of the fish to improve the estimation accuracy. In this way, the estimation accuracy is improved by analyzing the behavioral patterns of the fish.

[0131] During estimation, the estimation unit can perform more accurate estimation in cooperation with environmental data. The estimation unit, for example, improves estimation accuracy in cooperation with environmental data. The estimation unit can also improve estimation accuracy based on environmental data. Furthermore, the estimation unit can improve estimation accuracy by combining environmental data and fish species data. In this way, estimation accuracy is improved by cooperation with environmental data.

[0132] The estimation unit can estimate the user's emotion and determine the priority of estimation results based on the estimated user's emotion. The estimation unit, for example, recognizes the user's facial expression and estimates the emotion. The estimation unit can also estimate the user's emotion using voice analysis technology. For example, the estimation unit can prioritize displaying important estimation results when the user is excited. The estimation unit can also display detailed estimation results when the user is relaxed. Furthermore, the estimation unit can display simplified estimation results when the user is stressed. In this way, by determining the priority of estimation results based on the user's emotion, important estimation results can be prioritized and displayed.

[0133] During estimation, the estimation unit can improve the estimation accuracy in cooperation with other data sources. For example, the estimation unit can improve the estimation accuracy in cooperation with acoustic data. The estimation unit can also improve the estimation accuracy in cooperation with temperature data. Furthermore, the estimation unit can also improve the estimation accuracy in cooperation with other data sources. As a result, the estimation accuracy is improved by cooperation with other data sources.

[0134] During estimation, the estimation unit can combine different estimation algorithms, compare the estimation results, and select the optimal result. For example, the estimation unit combines different estimation algorithms and compares the estimation results. The estimation unit can also compare the estimation results and select the optimal result. Furthermore, the estimation unit can improve estimation accuracy by combining different estimation algorithms. As a result, the estimation accuracy is improved by combining different estimation algorithms.

[0135] The estimation unit can visualize the estimation result during estimation, allowing the user to intuitively understand it. For example, the estimation unit can visualize the estimation result in a graph or chart, allowing the user to intuitively understand it. The estimation unit can also display the estimation result on a map, allowing the user to intuitively understand it. Furthermore, the estimation unit can visualize the estimation result in a 3D model, allowing the user to intuitively understand it. In this way, by visualizing the estimation result, the user can intuitively understand it.

[0136] The linking unit can estimate the user's emotions and select environmental data to be linked based on the estimated user emotions. The linking unit, for example, recognizes the user's facial expressions and estimates the emotions. The linking unit can also estimate the user's emotions using voice analysis technology. For example, the linking unit prioritizes linking important environmental data when the user is excited. The linking unit can also link detailed environmental data when the user is relaxed. Furthermore, the linking unit can also link simplified environmental data when the user is feeling stressed. In this way, by selecting environmental data based on the user's emotions, important data can be prioritized for linking.

[0137] During collaboration, the collaboration unit can monitor fluctuations in the environmental data in real time and improve the accuracy of the data. For example, the collaboration unit can monitor fluctuations in the environmental data in real time and improve the accuracy of the data. The collaboration unit can also monitor fluctuations in the environmental data and detect abnormal values. Furthermore, the collaboration unit can improve the accuracy of the data based on the fluctuations in the environmental data. In this way, by monitoring fluctuations in the environmental data in real time, the accuracy of the data is improved.

[0138] During linking, the linking unit can integrate different environmental data sources to perform more comprehensive data linking. For example, the linking unit integrates different environmental data sources to perform comprehensive data linking. The linking unit can also integrate environmental data sources to improve data accuracy. Furthermore, the linking unit can combine different data sources to perform comprehensive data linking. As a result, by integrating different environmental data sources, data accuracy is improved.

[0139] During collaboration, the collaboration unit can automatically detect abnormal values ​​in the environmental data and ensure the reliability of the data. For example, the collaboration unit can automatically detect abnormal values ​​in the environmental data and ensure the reliability of the data. The collaboration unit can also detect abnormal values ​​and improve the accuracy of the data. Furthermore, the collaboration unit can also ensure the reliability of the data based on the abnormal values ​​in the environmental data. In this way, the reliability of the data is improved by automatically detecting abnormal values ​​in the environmental data.

[0140] The linking unit can estimate the user's emotions and determine the priority of the environmental data to be linked based on the estimated user's emotions. The linking unit, for example, recognizes the user's facial expressions and estimates the emotions. The linking unit can also estimate the user's emotions using voice analysis technology. For example, the linking unit prioritizes linking important environmental data when the user is excited. The linking unit can also link detailed environmental data when the user is relaxed. Furthermore, the linking unit can also link simplified environmental data when the user is stressed. In this way, by determining the priority of environmental data based on the user's emotions, important data can be linked preferentially.

[0141] During collaboration, the collaboration unit can improve the accuracy of the data by collaborating with other data sources. The collaboration unit can improve the accuracy of the data by collaborating with satellite data, for example. The collaboration unit can also improve the accuracy of the data by collaborating with meteorological data. Furthermore, the collaboration unit can also improve the accuracy of the data by collaborating with other data sources. As a result, the accuracy of the data is improved by collaborating with other data sources.

[0142] The linking unit can automatically convert different data formats during linking to ensure data compatibility. For example, the linking unit can automatically convert different data formats to ensure data compatibility. The linking unit can also convert data formats to improve data accuracy. Furthermore, the linking unit can combine different data formats to ensure data compatibility. In this way, data compatibility is ensured by automatically converting different data formats.

[0143] The linking unit can visualize the linked data to enable the user to intuitively understand it. For example, the linking unit can visualize the linked data in a graph or chart to enable the user to intuitively understand it. The linking unit can also display the linked data on a map to enable the user to intuitively understand it. Furthermore, the linking unit can visualize the linked data in a 3D model to enable the user to intuitively understand it. In this way, by visualizing the linked data, the user can intuitively understand it.

[0144] The recording unit can estimate the user's emotions and adjust the timing of sound recording based on the estimated user emotions. The recording unit, for example, recognizes the user's facial expressions and estimates the emotions. The recording unit can also estimate the user's emotions using voice analysis technology. For example, when the user is excited, the recording unit can increase the recording frequency and record sounds in real time. When the user is relaxed, the recording unit can also record sounds at a lower recording frequency. Furthermore, when the user is feeling stressed, the recording unit can adjust the recording timing to reduce the burden on the user. This allows for efficient sound recording by adjusting the timing of sound recording based on the user's emotions.

[0145] The recording unit can filter sounds that focus on a specific fish species or size during recording. For example, the recording unit can record sounds that focus on sounds emitted by a specific fish species. The recording unit can also record sounds that focus on sounds emitted by fish of a specific size. Furthermore, the recording unit can also record sounds that focus on a specific combination of fish species and size. In this way, necessary sounds can be efficiently recorded by filtering sounds that focus on a specific fish species or size.

[0146] The recording unit can automatically adjust the recording method according to environmental changes during recording. For example, if the water temperature changes, the recording unit adjusts the recording method to record sound. The recording unit can also adjust the recording method to record sound if the water flow changes. Furthermore, the recording unit can also adjust the recording method to record sound if the amount of light changes. This makes it possible to efficiently record sound by automatically adjusting the recording method according to environmental changes.

[0147] The recording unit can evaluate the quality of the sound during recording and automatically exclude low-quality sounds. For example, the recording unit evaluates the quality of the recorded sound in real time and excludes low-quality sounds. The recording unit can also evaluate the quality of the recorded sound and exclude noisy sounds. Furthermore, the recording unit can evaluate the quality of the recorded sound and exclude imperfect sounds. As a result, the accuracy of the recorded data is improved by evaluating the quality of the sound and excluding low-quality sounds.

[0148] The recording unit can estimate the user's emotions and determine the priority of sounds to be recorded based on the estimated user's emotions. The recording unit, for example, recognizes the user's facial expressions and estimates the emotions. The recording unit can also estimate the user's emotions using voice analysis technology. For example, the recording unit can prioritize recording important sounds when the user is excited. The recording unit can also record detailed sounds when the user is relaxed. Furthermore, the recording unit can record simplified sounds when the user is feeling stressed. In this way, by determining the priority of sounds based on the user's emotions, important sounds can be prioritized and recorded.

[0149] During recording, the recording unit can record sounds in cooperation with other sensors. For example, the recording unit can record sounds related to fish movements in cooperation with an acoustic sensor. The recording unit can also record sounds related to water temperature in cooperation with a temperature sensor. Furthermore, the recording unit can also record sounds related to light intensity in cooperation with a light sensor. This makes it possible to record more comprehensive sounds by recording sounds in cooperation with other sensors.

[0150] The recording unit can upload the sound to the cloud in real time during recording, enabling monitoring from a remote location. For example, the recording unit can upload the sound to the cloud in real time and monitor from a remote location. The recording unit can also upload the sound to the cloud to make it accessible from multiple devices. Furthermore, the recording unit can also upload the sound to the cloud to enable data analysis from a remote location. This makes it possible to monitor from a remote location by uploading the sound to the cloud in real time.

[0151] The recording unit can evaluate the quality of the recorded data and automatically exclude low-quality data. For example, the recording unit evaluates the quality of the recorded data in real time and excludes low-quality data. The recording unit can also evaluate the quality of the recorded data and exclude noisy data. Furthermore, the recording unit can evaluate the quality of the recorded data and exclude incomplete data. As a result, by evaluating the quality of the recorded data and excluding low-quality data, the accuracy of the data is improved.

[0152] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, recognizes the user's facial expressions and estimates the emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the analysis results based on the user's emotions, it is possible to provide a display that is easy for the user to understand.

[0153] During the analysis, the analysis unit can analyze the characteristics of the sound in detail and identify a specific fish species or behavior pattern. The analysis unit can, for example, analyze the frequency characteristics of the sound and identify a specific fish species. The analysis unit can also analyze changes in the sound over time and identify the behavior pattern of the fish. Furthermore, the analysis unit can analyze the intensity of the sound and identify the activity level of the fish. In this way, by analyzing the characteristics of the sound in detail, it is possible to identify a specific fish species or behavior pattern.

[0154] During analysis, the analysis unit can optimize the analysis algorithm by comparing it with past data. For example, the analysis unit compares past data with current data and optimizes the analysis algorithm. The analysis unit can also adjust the parameters of the analysis algorithm based on the past data. Furthermore, the analysis unit can integrate past data and current data to improve the accuracy of the analysis. In this way, the analysis accuracy is improved by optimizing the analysis algorithm by comparing it with past data.

[0155] The analysis unit can integrate environmental data and sound data during analysis to perform more accurate analysis. For example, the analysis unit integrates environmental data and sound data to improve the accuracy of analysis. The analysis unit can also analyze sound data based on environmental data. Furthermore, the analysis unit can combine sound data and environmental data to perform more accurate analysis. In this way, the integration of environmental data and sound data improves the accuracy of analysis.

[0156] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. The analysis unit, for example, recognizes the user's facial expressions and estimates the emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can prioritize displaying important analysis results when the user is excited. The analysis unit can also display detailed analysis results when the user is relaxed. Furthermore, the analysis unit can display simplified analysis results when the user is stressed. In this way, by prioritizing the analysis results based on the user's emotions, important analysis results can be displayed preferentially.

[0157] During analysis, the analysis unit can improve the accuracy of the analysis by linking with other data sources. For example, the analysis unit can improve the accuracy of the analysis by linking with acoustic data. The analysis unit can also improve the accuracy of the analysis by linking with temperature data. Furthermore, the analysis unit can also improve the accuracy of the analysis by linking with other data sources. As a result, the analysis accuracy is improved by linking with other data sources.

[0158] During analysis, the analysis unit can combine different analysis algorithms, compare the analysis results, and select the optimal result. For example, the analysis unit combines different analysis algorithms and compares the analysis results. The analysis unit can also compare the analysis results and select the optimal result. Furthermore, the analysis unit can combine different analysis algorithms to improve the accuracy of the analysis. As a result, the accuracy of the analysis is improved by combining different analysis algorithms.

[0159] During the analysis, the analysis unit can visualize the analysis results to enable the user to intuitively understand them. For example, the analysis unit can visualize the analysis results in graphs or charts to enable the user to intuitively understand them. The analysis unit can also display the analysis results on a map to enable the user to intuitively understand them. Furthermore, the analysis unit can visualize the analysis results in a 3D model to enable the user to intuitively understand them. In this way, by visualizing the analysis results, the user can intuitively understand them.

[0160] The generation unit can estimate the user's emotion and adjust the expression method of the generated sound based on the estimated user's emotion. The generation unit, for example, recognizes the user's facial expression and estimates the emotion. The generation unit can also estimate the user's emotion using voice analysis technology. For example, the generation unit can generate a calm sound when the user is relaxed. The generation unit can also generate a stimulating sound when the user is excited. Furthermore, the generation unit can generate a relaxing sound when the user is stressed. In this way, by adjusting the expression method of the sound based on the user's emotion, more effective sounds can be generated.

[0161] The generation unit can refer to past data when generating a sound to generate an optimal sound for a specific fish species or size. For example, the generation unit generates an optimal sound for a specific fish species based on past data. The generation unit can also generate an optimal sound for a specific size of fish based on past data. Furthermore, the generation unit can also generate an optimal sound for a specific combination of fish species and size by referring to past data. In this way, by referring to past data, it is possible to generate an optimal sound for a specific fish species or size.

[0162] During generation, the generation unit can try different combinations of sounds to generate the optimal sound. For example, the generation unit can combine sounds of different frequencies to generate the optimal sound. The generation unit can also combine different sound intensities to generate the optimal sound. Furthermore, the generation unit can combine different sound patterns to generate the optimal sound. In this way, the optimal sound can be generated by trying different combinations of sounds.

[0163] The generation unit can generate more effective sounds in cooperation with environmental data during generation. The generation unit generates optimal sounds based on, for example, environmental data. The generation unit can also generate effective sounds by combining environmental data and sound data. Furthermore, the generation unit can also generate optimal sounds for specific situations by referring to the environmental data. In this way, more effective sounds can be generated by cooperation with environmental data.

[0164] The generation unit can estimate the user's emotion and determine the priority of sounds to be generated based on the estimated user's emotion. The generation unit, for example, recognizes the user's facial expression and estimates the emotion. The generation unit can also estimate the user's emotion using voice analysis technology. For example, the generation unit can generate important sounds with priority when the user is excited. The generation unit can also generate detailed sounds when the user is relaxed. Furthermore, the generation unit can generate simplified sounds when the user is stressed. In this way, by determining the priority of sounds based on the user's emotion, important sounds can be generated with priority.

[0165] During generation, the generation unit can improve generation accuracy in cooperation with other data sources. The generation unit can improve generation accuracy, for example, in cooperation with acoustic data. The generation unit can also improve generation accuracy in cooperation with temperature data. Furthermore, the generation unit can also improve generation accuracy in cooperation with other data sources. As a result, by cooperation with other data sources, generation accuracy is improved.

[0166] During generation, the generation unit can combine different generation algorithms, compare the generation results, and select the optimal result. For example, the generation unit combines different generation algorithms and compares the generation results. The generation unit can also compare the generation results and select the optimal result. Furthermore, the generation unit can improve generation accuracy by combining different generation algorithms. As a result, generation accuracy is improved by combining different generation algorithms.

[0167] The generation unit can visualize the generated results to enable the user to intuitively understand. For example, the generation unit can visualize the generated results in a graph or chart to enable the user to intuitively understand. The generation unit can also display the generated results on a map to enable the user to intuitively understand. Furthermore, the generation unit can visualize the generated results in a 3D model to enable the user to intuitively understand. In this way, by visualizing the generated results, the user can intuitively understand.

[0168] The learning unit can estimate the user's emotions and select learning data based on the estimated user emotions. The learning unit, for example, recognizes the user's facial expressions and estimates the emotions. The learning unit can also estimate the user's emotions using voice analysis technology. For example, the learning unit can prioritize selecting important learning data when the user is excited. The learning unit can also select detailed learning data when the user is relaxed. Furthermore, the learning unit can select simplified learning data when the user is stressed. In this way, by selecting learning data based on the user's emotions, important data can be prioritized for learning.

[0169] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit optimizes the learning algorithm based on, for example, past learning data. The learning unit can also compare past learning data with current data and adjust the learning algorithm. Furthermore, the learning unit can also improve learning accuracy by referring to past learning data. In this way, the accuracy of the learning algorithm is improved by referring to past learning data.

[0170] During learning, the learning unit can try different sound combinations and learn the optimal sound source. For example, the learning unit combines sounds of different frequencies to learn the optimal sound source. The learning unit can also combine different sound intensities to learn the optimal sound source. Furthermore, the learning unit can also combine different sound patterns to learn the optimal sound source. In this way, the optimal sound source can be learned by trying different sound combinations.

[0171] During learning, the learning unit can perform more effective learning in cooperation with environmental data. The learning unit, for example, learns the optimal sound source based on the environmental data. The learning unit can also perform effective learning by combining the environmental data and sound data. Furthermore, the learning unit can also learn the optimal sound source for a specific situation by referring to the environmental data. This improves learning accuracy by working with the environmental data.

[0172] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. The learning unit, for example, recognizes the user's facial expressions and estimates the emotions. The learning unit can also estimate the user's emotions using voice analysis technology. For example, if the user is excited, the learning unit can increase the learning frequency and perform learning in real time. Also, if the user is relaxed, the learning unit can decrease the learning frequency and perform learning. Furthermore, if the user is feeling stressed, the learning unit can adjust the learning frequency to reduce the burden on the user. In this way, efficient learning is possible by adjusting the learning frequency based on the user's emotions.

[0173] During learning, the learning unit can improve the learning accuracy in cooperation with other data sources. For example, the learning unit improves the learning accuracy in cooperation with acoustic data. The learning unit can also improve the learning accuracy in cooperation with temperature data. Furthermore, the learning unit can also improve the learning accuracy in cooperation with other data sources. As a result, the learning accuracy is improved by cooperation with other data sources.

[0174] During learning, the learning unit can combine different learning algorithms, compare the learning results, and select the optimal result. For example, the learning unit combines different learning algorithms and compares the learning results. The learning unit can also compare the learning results and select the optimal result. Furthermore, the learning unit can combine different learning algorithms to improve learning accuracy. As a result, by combining different learning algorithms, learning accuracy is improved.

[0175] The learning unit can visualize the learning results to enable the user to intuitively understand. For example, the learning unit can visualize the learning results in graphs or charts to enable the user to intuitively understand. The learning unit can also display the learning results on a map to enable the user to intuitively understand. Furthermore, the learning unit can visualize the learning results in a 3D model to enable the user to intuitively understand. In this way, visualizing the learning results allows the user to intuitively understand.

[0176] The providing unit can estimate the user's emotions and adjust the display method of the provided sound source based on the estimated user's emotions. The providing unit, for example, recognizes the user's facial expressions and estimates the emotions. The providing unit can also estimate the user's emotions using voice analysis technology. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the sound source based on the user's emotions, it is possible to provide a display that is easy for the user to understand.

[0177] The providing unit can refer to past data when providing the sound source that is optimal for a specific fish species or size. For example, the providing unit provides the sound source that is optimal for a specific fish species based on past data. The providing unit can also provide the sound source that is optimal for a fish of a specific size based on past data. Furthermore, the providing unit can also provide the sound source that is optimal for a specific combination of fish species and size by referring to past data. In this way, the sound source that is optimal for a specific fish species or size can be provided by referring to past data.

[0178] The providing unit can try different combinations of sound sources at the time of providing and provide the optimal sound source. For example, the providing unit can combine sounds of different frequencies to provide the optimal sound source. The providing unit can also combine different sound intensities to provide the optimal sound source. Furthermore, the providing unit can also combine different sound patterns to provide the optimal sound source. In this way, the optimal sound source can be provided by trying different combinations of sound sources.

[0179] The providing unit can provide a more effective sound source in cooperation with the environmental data when providing the sound source. For example, the providing unit provides the optimal sound source based on the environmental data. The providing unit can also provide an effective sound source by combining the environmental data and the sound data. Furthermore, the providing unit can also provide the optimal sound source for a specific situation by referring to the environmental data. In this way, a more effective sound source can be provided by coordinating with the environmental data.

[0180] The providing unit can estimate the user's emotion and determine the priority of the sound sources to be provided based on the estimated user's emotion. The providing unit, for example, recognizes the user's facial expression and estimates the emotion. The providing unit can also estimate the user's emotion using voice analysis technology. For example, the providing unit can provide important sound sources with priority when the user is excited. The providing unit can also provide detailed sound sources when the user is relaxed. Furthermore, the providing unit can provide simplified sound sources when the user is feeling stressed. In this way, by determining the priority of sound sources based on the user's emotion, important sound sources can be provided with priority.

[0181] The providing unit can improve the accuracy of providing data by cooperating with other data sources during the providing process. The providing unit can improve the accuracy of providing data by cooperating with acoustic data, for example. The providing unit can also improve the accuracy of providing data by cooperating with temperature data. Furthermore, the providing unit can also improve the accuracy of providing data by cooperating with other data sources. As a result, the accuracy of providing data is improved by cooperating with other data sources.

[0182] At the time of provision, the providing unit can combine different provision algorithms to compare the provision results and select the optimal result. The providing unit, for example, combines different provision algorithms to compare the provision results. The providing unit can also compare the provision results and select the optimal result. Furthermore, the providing unit can combine different provision algorithms to improve provision accuracy. As a result, provision accuracy is improved by combining different provision algorithms.

[0183] The providing unit can visualize the provided results to enable the user to intuitively understand them. For example, the providing unit can visualize the provided results in a graph or chart to enable the user to intuitively understand them. The providing unit can also display the provided results on a map to enable the user to intuitively understand them. Furthermore, the providing unit can visualize the provided results in a 3D model to enable the user to intuitively understand them. In this way, by visualizing the provided results, the user can intuitively understand them. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, estimation unit, linking unit, recording unit, analysis unit, generation unit, learning unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect data for recognizing fish species using the camera 42 or sensors of the smart device 14. The collection unit can also be realized by the identification processing unit 290 of the data processing device 12. For example, the analysis unit can analyze data collected by the control unit 46A of the smart device 14 to recognize fish species. For example, the estimation unit can estimate fish size and school size using the identification processing unit 290 of the data processing device 12. For example, the linking unit can link with environmental data using the identification processing unit 290 of the data processing device 12. For example, the recording unit can record underwater sounds using the camera 42 or microphone 38B of the smart device 14. For example, the analysis unit can analyze sounds recorded by the identification processing unit 290 of the data processing device 12. The generation unit can reproduce the sound, for example, by the specific processing unit 290 of the data processing device 12. The learning unit can learn combinations of multiple sounds, for example, by the specific processing unit 290 of the data processing device 12, and create an optimal sound source. The provision unit can provide the sound source created by the specific processing unit 290 of the data processing device 12, for example, to efficiently attract fish. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, estimation unit, linking unit, recording unit, analysis unit, generation unit, learning unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect data for recognizing fish species using the camera 42 or sensor of the smart glasses 214. The collection unit can also be realized by the identification processing unit 290 of the data processing device 12. For example, the analysis unit can analyze data collected by the control unit 46A of the smart glasses 214 to recognize fish species. For example, the estimation unit can estimate fish size and school size by the identification processing unit 290 of the data processing device 12. For example, the linking unit can link with environmental data by the identification processing unit 290 of the data processing device 12. For example, the recording unit can record underwater sounds using the camera 42 or microphone 238 of the smart glasses 214. For example, the analysis unit can analyze sounds recorded by the identification processing unit 290 of the data processing device 12. The generation unit can reproduce the sound, for example, by the specific processing unit 290 of the data processing device 12. The learning unit can learn combinations of multiple sounds, for example, by the specific processing unit 290 of the data processing device 12, and create an optimal sound source. The provision unit can provide the sound source created by the specific processing unit 290 of the data processing device 12, for example, to efficiently attract fish. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, estimation unit, linking unit, recording unit, analysis unit, generation unit, learning unit, and provision unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect data for recognizing fish species using the camera 42 or sensors of the headset-type terminal 314. The collection unit can also be realized by the identification processing unit 290 of the data processing device 12. For example, the analysis unit can analyze data collected by the control unit 46A of the headset-type terminal 314 to recognize fish species. For example, the estimation unit can estimate fish size and school size by the identification processing unit 290 of the data processing device 12. For example, the linking unit can link with environmental data by the identification processing unit 290 of the data processing device 12. For example, the recording unit can record underwater sounds using the camera 42 or microphone 238 of the headset-type terminal 314. For example, the analysis unit can analyze sounds recorded by the identification processing unit 290 of the data processing device 12. The generation unit can reproduce the sound, for example, by the specific processing unit 290 of the data processing device 12. The learning unit can learn combinations of multiple sounds, for example, by the specific processing unit 290 of the data processing device 12, and create an optimal sound source. The provision unit can provide the sound source created by the specific processing unit 290 of the data processing device 12, for example, to efficiently attract fish. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, estimation unit, linking unit, recording unit, analysis unit, generation unit, learning unit, and provision unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data for recognizing fish species using the camera 42 or sensors of the robot 414. The collection unit can also be realized by the identification processing unit 290 of the data processing device 12. For example, the analysis unit can analyze data collected by the control unit 46A of the robot 414 to recognize fish species. For example, the estimation unit can estimate the size of fish and the size of a school of fish using the identification processing unit 290 of the data processing device 12. For example, the linking unit can link with environmental data using the identification processing unit 290 of the data processing device 12. For example, the recording unit can record underwater sounds using the camera 42 or microphone 238 of the robot 414. For example, the analysis unit can analyze the sounds recorded by the identification processing unit 290 of the data processing device 12. The generation unit can reproduce the sound, for example, by the specific processing unit 290 of the data processing device 12. The learning unit can learn combinations of multiple sounds, for example, by the specific processing unit 290 of the data processing device 12, and create an optimal sound source. The provision unit can provide the sound source created by the specific processing unit 290 of the data processing device 12, for example, to efficiently attract fish.

[0184] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0185] The analysis unit can also analyze the behavioral patterns of fish to help identify fish species. For example, the analysis unit can analyze the swimming speed and direction of fish to identify the behavioral patterns of specific fish species. The analysis unit can also analyze the feeding behavior and school formation patterns of fish to help identify fish species. Furthermore, the analysis unit can analyze the migration paths of fish to identify the habitats of specific fish species. In this way, analyzing fish behavioral patterns improves the accuracy of identifying fish species.

[0186] The collection unit can also collect data in cooperation with other sensors. For example, it can collect data on fish movements in cooperation with an acoustic sensor. The collection unit can also collect data on water temperature in cooperation with a temperature sensor. Furthermore, the collection unit can also collect data on light intensity in cooperation with a light sensor. This allows for more comprehensive data collection by collecting data in cooperation with other sensors.

[0187] The estimation unit can also estimate the size and school size of fish by taking into account their growth patterns. For example, the size is estimated based on the growth patterns of fish. The estimation unit can also estimate the school size based on the growth patterns of fish. Furthermore, the estimation unit can estimate the size of individual fish by taking into account their growth patterns. In this way, by taking into account the growth patterns of fish, the accuracy of estimating the size and school size is improved.

[0188] The linking unit can also monitor fluctuations in the environmental data in real time to improve the accuracy of the data. For example, the linking unit can monitor fluctuations in the environmental data in real time to detect abnormal values. The linking unit can also improve the accuracy of the data based on the fluctuations in the environmental data. Furthermore, the linking unit can monitor fluctuations in the environmental data to ensure the reliability of the data. In this way, by monitoring fluctuations in the environmental data in real time, the accuracy of the data is improved.

[0189] The recording unit can also evaluate the quality of the recorded data and automatically exclude low-quality data. For example, the quality of the recorded data can be evaluated in real time and low-quality data can be excluded. The recording unit can also evaluate the quality of the recorded data and exclude noisy data. Furthermore, the recording unit can evaluate the quality of the recorded data and exclude incomplete data. As a result, by evaluating the quality of the recorded data and excluding low-quality data, the accuracy of the data can be improved.

[0190] The collection unit can also estimate the user's emotions and adjust the timing of data collection from the underwater camera based on the estimated user emotions. For example, the collection unit can recognize the user's facial expressions and estimate the user's emotions. The collection unit can also estimate the user's emotions using voice analysis technology. For example, if the user is excited, the collection frequency can be increased to collect data in real time. If the user is relaxed, the collection frequency can be reduced to collect data. Furthermore, if the user is feeling stressed, the collection timing can be adjusted to reduce the burden on the user. This allows for efficient data collection by adjusting the data collection timing based on the user's emotions.

[0191] The analysis unit can also estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, it can recognize the user's facial expressions and estimate emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, if the user is nervous, it can provide a simple, highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that focuses on the main points. In this way, by adjusting the display method of the analysis results based on the user's emotions, it is possible to display the results in a way that is easy for the user to understand.

[0192] The estimation unit can also estimate the user's emotions and adjust the display method of the estimation results based on the estimated user emotions. For example, the estimation unit can recognize the user's facial expressions and estimate the emotions. The estimation unit can also estimate the user's emotions using voice analysis technology. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the estimation results based on the user's emotions, it is possible to display an easy-to-understand display for the user.

[0193] The generation unit can also estimate the user's emotion and adjust the expression method of the generated sound based on the estimated user's emotion. For example, the generation unit can recognize the user's facial expression and estimate the emotion. The generation unit can also estimate the user's emotion using voice analysis technology. For example, if the user is relaxed, a calm sound can be generated. If the user is excited, a stimulating sound can be generated. Furthermore, if the user is stressed, a relaxing sound can be generated. In this way, by adjusting the expression method of the sound based on the user's emotion, more effective sounds can be generated.

[0194] The providing unit can also estimate the user's emotions and adjust the display method of the audio source to be provided based on the estimated user's emotions. For example, the providing unit can recognize the user's facial expressions and estimate the emotions. The providing unit can also estimate the user's emotions using voice analysis technology. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the audio source based on the user's emotions, it is possible to provide a display that is easy for the user to understand.

[0195] The processing flow of the second embodiment will be briefly explained below.

[0196] Step 1: The collection unit collects data for recognizing fish species. For example, the collection unit collects data from an underwater camera installed in a paya or the like. The collection unit can also collect data using sensors or sonar. Step 2: The analysis unit analyzes the collected data and recognizes the fish species. The analysis unit may recognize the fish species using, for example, image analysis technology. The analysis unit may also recognize the fish species using audio analysis technology. Step 3: The estimation unit estimates the size of the fish and the size of the school based on the analyzed data. For example, the estimation unit estimates the size of the fish using a statistical model. The estimation unit can also estimate the size of the school using a machine learning algorithm. Step 4: The linking unit links with environmental data such as air pressure, water temperature, light intensity, transparency, turbidity, water color, water current, floating matter, and odor. The linking unit acquires the environmental data using, for example, API linking. The linking unit can also acquire the environmental data using database linking. Step 5: The recording unit records underwater sounds. The recording unit may record sounds using, for example, an underwater microphone. Alternatively, the recording unit may record sounds using an acoustic sensor. Step 6: The analysis unit analyzes the recorded sounds and identifies the swimming sounds of a school of bait, the sounds of predation, the sounds of predators, etc. The analysis unit analyzes the sounds using, for example, a sound analysis algorithm. The analysis unit can also analyze the sounds using frequency analysis. Step 7: The generator reproduces the analyzed sound. For example, the generator reproduces the sound using a voice synthesis technique. Alternatively, the generator can reproduce the sound using a simulation technique. Step 8: The learning unit learns combinations of multiple sounds and creates an optimal sound source. The learning unit creates the sound source using, for example, a machine learning algorithm. The learning unit can also create the sound source using a neural network. Step 9: The providing unit provides the created sound source to efficiently attract fish. The providing unit provides the sound source using, for example, a cloud service. The providing unit can also provide the sound source using a database.

[0197] 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.

[0198] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0199] 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.

[0200] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0201] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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.

[0210] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0211] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0212] 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.

[0213] 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.

[0214] 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 AI 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.

[0215] 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.

[0216] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0217] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0218] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0227] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0228] 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.

[0229] 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.

[0230] 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 AI 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.

[0231] 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.

[0232] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0233] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0234] 7, a 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0244] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0245] 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.

[0246] 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.

[0247] 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 AI 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.

[0248] 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.

[0249] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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).

[0254] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0255] 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."

[0256] 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.

[0257] 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.

[0258] 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.

[0259] 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.

[0260] 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.

[0261] 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.

[0262] 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.

[0263] 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.

[0264] 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.

[0265] 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.

[0266] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0267] 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.

[0268] [Explanation of symbols]

[0269] 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 collection unit for recognizing fish species; an analysis unit that analyzes the data collected by the collection unit; an estimation unit that estimates the size of fish and the scale of their school based on the data analyzed by the analysis unit; A collaboration department that collaborates with environmental data; A recording unit that records sound, an analysis unit that analyzes the sound recorded by the recording unit; a generator that reproduces the sound analyzed by the analyzer; a learning unit that creates a sound source based on the sound generated by the generation unit; a providing unit that provides the sound source created by the learning unit; Equipped with A system characterized by:

2. The collecting unit Collect data from underwater cameras installed in payaos, etc. The system of claim 1 .

3. The analysis unit Analyzing collected data and recognizing fish species The system of claim 1 .

4. The estimation unit Estimate fish size and school size based on analyzed data The system of claim 1 .

5. The linking unit is Links with environmental data such as air pressure, water temperature, light intensity, transparency, turbidity, water color, water flow, suspended matter, and odor The system of claim 1 .

6. The recording unit Recording underwater sounds The system of claim 1 .

7. The analysis unit Analyze the recorded sounds to identify the swimming sounds of bait schools, sounds of predation, and sounds of predators. The system of claim 1 .

8. The generation unit Reproducing the analyzed sound The system of claim 1 .

Images