system

Abstract

The system according to this embodiment aims to collect and analyze data from the air and underwater in real time and report it promptly to the relevant organizations. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a reporting unit. The collection unit collects data from the air and underwater. The analysis unit analyzes the data collected by the collection unit in real time. The reporting unit reports the information detected by the analysis unit to the relevant organizations.

Description

Technical Field

[0004] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that data collection and analysis from the air and water are not performed in real time, and it is difficult to detect environmental changes and illegal acts at an early stage.

[0005] The system according to the embodiment aims to collect and analyze data from the air and water in real time and promptly report it to relevant agencies.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a reporting unit. The collection unit collects data from the air and water. The analysis unit analyzes the data collected by the collection unit in real time. The reporting unit reports the information detected by the analysis unit to relevant agencies. [Effects of the Invention]

[0007] The system according to this embodiment can collect and analyze data from the air and underwater in real time and report it promptly to the relevant organizations. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) An environmental monitoring system according to an embodiment of the present invention is a system that combines an autonomous navigation drone and an autonomous underwater drone to collect and analyze data from the air and underwater in real time. This environmental monitoring system aims to detect marine pollution, illegal fishing, changes in the natural environment, etc., at an early stage and report them promptly to the relevant authorities. For example, in the environmental monitoring system, the autonomous navigation drone and the autonomous underwater drone first collect data from the air and underwater. Next, the collected data is analyzed in real time by AI. The AI ​​uses image recognition technology and data analysis technology to detect marine pollution, illegal fishing, changes in the natural environment, etc. For example, the AI ​​analyzes water quality data to detect signs of marine pollution and tracks the movement of vessels to monitor illegal fishing activities. The detected information is promptly reported to the relevant authorities. For example, the environmental monitoring system notifies the relevant authorities of the detected information in real time to encourage a rapid response. This system improves the range and speed of environmental monitoring and ensures the accuracy and immediacy of data. Furthermore, it is designed with environmental considerations in mind to minimize the impact on the ecosystem. For example, the environmental monitoring system uses low-energy-consumption sensors to minimize the environmental impact of drone operation. Furthermore, the introduction of AI agents will improve the detection rate of marine pollution by 50%, the efficiency of cracking down on illegal fishing by 30%, and the accuracy of tracking environmental changes by 40%. This will enable wide-ranging and detailed monitoring, leading to rapid response and reporting. It is also expected to contribute to sustainable environmental protection activities. Potential target groups include environmental protection agencies, government agencies, and university research institutes. This system will address the challenges faced by these organizations, such as limitations in the scope and speed of environmental monitoring, and the lack of accuracy and immediacy of data. The market size is estimated at 500 billion yen for the environmental monitoring market, with an initial target market of 100 billion yen for environmental protection agencies. The rapid increase in environmental problems and advancements in technology present an excellent time to enter the market, aiming to realize a sustainable society through increased environmental awareness and the promotion of concrete actions. As a result, the environmental monitoring system will be able to collect and analyze data from the air and underwater in real time and report it promptly to relevant organizations.

[0029] The environmental monitoring system according to this embodiment comprises a collection unit, an analysis unit, and a reporting unit. The collection unit collects data from the air and underwater. The collection unit can collect data from the air using, for example, an autonomous drone. The collection unit can also collect data from underwater using an autonomous underwater drone. For example, the collection unit can collect temperature data, chemical substance data, biological data, etc., as aerial data. The collection unit can also collect water quality data, marine organism data, seabed topography data, etc., as underwater data. Some or all of the above-described processing in the collection unit may be performed using AI or not. For example, the collection unit can collect data using sensors mounted on a drone and input that data into AI for analysis. The analysis unit analyzes the data collected by the collection unit in real time. The analysis unit can detect signs of marine pollution from the collected data using, for example, image recognition technology. The analysis unit can also detect illegal fishing activities from the collected data using data analysis technology. For example, the analysis unit can detect signs of marine pollution from the collected data using image recognition technology. Furthermore, the analysis department can use data analysis techniques to detect illegal fishing activities from the collected data. Some or all of the above-described processes in the analysis department may be performed using AI or not. For example, the analysis department can input the collected data into an AI, which can analyze the data to detect signs of marine pollution or illegal fishing activities. The reporting department reports the information detected by the analysis department to the relevant agencies. The reporting department can, for example, notify the relevant agencies of the detected information in real time. The reporting department can also periodically submit the detected information to the relevant agencies as reports. For example, the reporting department can notify the relevant agencies of the detected information in real time. The reporting department can also periodically submit the detected information to the relevant agencies as reports. Some or all of the above-described processes in the reporting department may be performed using AI or not.For example, the reporting unit can automatically generate a report from the information detected using AI and send that report to the relevant organizations. As a result, the environmental monitoring system according to this embodiment can collect and analyze data from the air and underwater in real time and report it promptly to the relevant organizations.

[0030] The data collection unit collects data from the air and underwater. For example, the data collection unit can collect data from the air using an autonomous drone. Autonomous drones are equipped with high-performance cameras and various sensors, which enable the efficient collection of wide-area environmental data. Specifically, the drone measures the temperature of the atmosphere using a temperature sensor during flight and detects the concentration of harmful substances in the air using a chemical sensor. It is also possible to collect biological data such as airborne microorganisms and pollen using a biosensor. This data is collected in real time along the drone's flight path and transmitted to a ground-based database. On the other hand, the data collection unit can also collect data from underwater using autonomous underwater drones. These autonomous underwater drones are equipped with water quality sensors, marine organism sensors, and seabed topography sensors, enabling the collection of detailed data on the underwater environment. Specifically, the underwater drones use water quality sensors to measure water quality data such as pH, dissolved oxygen, and turbidity, and marine organism sensors to detect the types and numbers of organisms in the water. They can also use seabed topography sensors to map the seabed topography and detect changes or anomalies in the terrain. This data is collected in real time along the underwater drone's navigation path and transmitted to a database on land. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can collect data using sensors mounted on a drone and input that data into an AI for analysis. The AI ​​analyzes the collected data in real time and detects anomalies and unique patterns. This enables the data collection unit to collect environmental data efficiently and accurately, allowing for early detection of anomalies and rapid response.

[0031] The analysis unit analyzes the data collected by the collection unit in real time. For example, the analysis unit can use image recognition technology to detect signs of marine pollution from the collected data. Specifically, it uses AI-based image recognition technology to analyze image data captured by aerial drones and detect pollutants such as oil slicks and debris floating on the sea surface. The AI ​​can also analyze the spread and concentration of these pollutants and assess the degree of pollution. Furthermore, the analysis department can use data analysis technology to detect illegal fishing activities from the collected data. For example, AI-based data analysis technology can analyze data collected by underwater drones to detect abnormal declines in fish populations or the misuse of fishing gear in specific sea areas. Based on this data, the AI ​​can identify activities that are highly likely to be illegal fishing and issue warnings to relevant agencies. Some or all of the above-described processes in the analysis department may be performed using AI or not. For example, the analysis department can input collected data into an AI, which can then analyze the data to detect signs of marine pollution or illegal fishing activities. The AI ​​has the ability to learn from past data and patterns and to quickly detect anomalous data and patterns. This allows the analysis department to efficiently and accurately analyze the collected data and detect environmental anomalies and risks at an early stage. Furthermore, the analysis unit can accumulate collected data over the long term and perform trend analysis and predictive analysis. For example, it can analyze pollution trends in specific seasons or regions based on historical data and predict future risks. It can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The reporting department reports the information detected by the analysis department to the relevant agencies. For example, the reporting department can notify the relevant agencies of the detected information in real time. Specifically, the reporting department uses AI to automatically generate reports of the detected information and sends these reports to the relevant agencies. Based on the data provided by the analysis department, the AI ​​creates detailed reports that describe the degree of pollution and the details of illegal fishing activities. This allows the relevant agencies to quickly and accurately understand the situation and take appropriate action. Furthermore, the reporting department can periodically submit detected information to relevant organizations as reports. For example, the reporting department can prepare monthly or weekly reports summarizing past data and trends. This allows relevant organizations to understand long-term environmental changes and risk trends and to formulate strategic countermeasures. Some or all of the above-described processes in the reporting department may be performed using AI or not. For example, the reporting department may use AI to automatically generate a report from the detected information and send that report to the relevant organizations. The AI ​​would automatically organize the report's contents and highlight important information to enable the relevant organizations to respond quickly. Furthermore, the reporting department can reliably transmit information using multiple communication methods. For example, real-time notifications are sent via email, SMS, and a dedicated notification application. Regular reports are also provided in PDF format and through a web portal, making them easily accessible to relevant organizations. This allows the reporting department to provide information to relevant organizations quickly and reliably, maximizing the effectiveness of the environmental monitoring system.

[0033] The data collection unit can collect data using autonomous drones and autonomous underwater drones. For example, the data collection unit can collect data from the air using autonomous drones. For example, the data collection unit can collect temperature data, chemical data, biological data, etc. from the air using sensors mounted on autonomous drones. The data collection unit can also collect data from underwater using autonomous underwater drones. For example, the data collection unit can collect water quality data, marine organism data, seabed topography data, etc. from underwater using sensors mounted on autonomous underwater drones. This makes it possible to collect data from both the air and underwater by using autonomous drones and autonomous underwater drones. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data acquired from sensors mounted on autonomous drones and autonomous underwater drones into a generating AI, which can then analyze and collect the data.

[0034] The analysis unit can detect marine pollution, illegal fishing, and changes in the natural environment using image recognition technology and data analysis technology. For example, the analysis unit can use image recognition technology to detect signs of marine pollution from collected data. The analysis unit can also use data analysis technology to detect illegal fishing activities from collected data. In this way, by using image recognition technology and data analysis technology, marine pollution, illegal fishing, and changes in the natural environment can be accurately detected. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without generative AI. For example, the analysis unit can input collected data into a generative AI, and the generative AI can analyze the data to detect signs of marine pollution or illegal fishing activities.

[0035] The reporting department can promptly report detected information to relevant organizations. For example, the reporting department can notify relevant organizations of detected information in real time. The reporting department can also periodically submit detected information to relevant organizations as reports. This enables a rapid response by promptly reporting detected information to relevant organizations. Some or all of the above processing in the reporting department may be performed using or without generation AI. For example, the reporting department can automatically generate a report of detected information using generation AI and send that report to relevant organizations.

[0036] The collection unit can be designed with environmental considerations in mind to minimize its impact on the ecosystem. For example, the collection unit may use low-energy-consumption sensors. The collection unit may also be designed to minimize the environmental impact of drone operations. This contributes to environmental protection by minimizing the impact on the ecosystem. Some or all of the processing described above in the collection unit may be performed using generative AI or not. For example, the collection unit can optimize energy consumption and minimize environmental impact using generative AI.

[0037] The analysis unit can improve the detection rate of marine pollution by 50%. The analysis unit can, for example, use image recognition technology to detect signs of marine pollution. The analysis unit can also detect signs of marine pollution using data analysis technology. This improves the detection rate of marine pollution, enabling more accurate environmental monitoring. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can input collected data into a generative AI, which can then analyze the data to detect signs of marine pollution.

[0038] The analysis unit can improve the efficiency of detecting illegal fishing by 30%. The analysis unit can, for example, use image recognition technology to detect illegal fishing activities. The analysis unit can also detect illegal fishing activities using data analysis technology. This can contribute to suppressing illegal activities by improving the efficiency of detecting illegal fishing. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without generative AI. For example, the analysis unit can input collected data into a generative AI, and the generative AI can analyze the data to detect illegal fishing activities.

[0039] The analysis unit can improve the accuracy of tracking environmental changes by 40%. The analysis unit can, for example, use image recognition technology to detect signs of environmental changes. The analysis unit can also detect signs of environmental changes using data analysis technology. This improves the accuracy of tracking environmental changes, enabling more detailed environmental monitoring. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can input collected data into a generative AI, which can then analyze the data to detect signs of environmental changes.

[0040] The data collection unit can dynamically change the type of data it collects in response to environmental changes. For example, if marine pollution is progressing, the data collection unit can change the target of data collection according to the type of pollutant. The data collection unit can also focus on monitoring the movements of fishing vessels if illegal fishing is increasing. The data collection unit can also concentrate on collecting data from specific areas if the natural environment is changing rapidly. By changing the type of data in response to environmental changes, more appropriate data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input environmental change data into a generation AI, which can dynamically change the type of data.

[0041] The data collection unit can adjust the accuracy of the data it collects according to the importance of the data being collected. For example, when collecting important data, the data collection unit can use high-precision sensors to collect the data. For example, when collecting important data, the data collection unit can use high-precision sensors to collect the data. Alternatively, when collecting general data, the data collection unit can use standard sensors to collect the data. For example, in an emergency, the data collection unit can sacrifice accuracy to collect data quickly. In this way, efficient data collection becomes possible by adjusting the accuracy of the data according to the importance of the data being collected. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input importance data of the data to be collected into a generative AI, which can then adjust the accuracy of the data.

[0042] The data collection unit can dynamically change the range of data to be collected according to geographical conditions. For example, the data collection unit can focus on collecting data in areas where marine pollution is progressing. For example, the data collection unit can focus on collecting data in areas where illegal fishing is rampant. For example, the data collection unit can focus on collecting data in areas where illegal fishing is rampant. For example, the data collection unit can focus on collecting data in areas where the natural environment is changing rapidly. For example, the data collection unit can focus on collecting data in areas where the natural environment is changing rapidly. This makes efficient data collection possible by changing the range of data according to geographical conditions. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input geographical condition data into a generation AI, and the generation AI can dynamically change the range of data.

[0043] The data collection unit can adjust the frequency of data collection according to the rate of environmental change. For example, if the environmental change is progressing rapidly, the data collection unit can increase the frequency of data collection. The data collection unit can also decrease the frequency of data collection if the environmental change is slow. The data collection unit can also adjust the frequency of data collection in advance if an environmental change is predicted. In this way, by adjusting the data frequency according to the rate of environmental change, data can be collected at the appropriate time. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input environmental change rate data into a generative AI, and the generative AI can adjust the data frequency.

[0044] The analysis unit can dynamically change the algorithm it uses depending on the type of data collected. For example, when analyzing marine pollution data, the analysis unit can use an algorithm that corresponds to the type of pollutant. Similarly, when analyzing illegal fishing data, the analysis unit can use an algorithm that analyzes the movement of fishing vessels. Furthermore, when analyzing data on changes in the natural environment, the analysis unit can use an algorithm that analyzes patterns of environmental change. This allows for more appropriate data analysis by changing the algorithm according to the type of data. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the type of collected data into the generative AI, which can then dynamically change the algorithm.

[0045] The analysis unit can adjust the level of detail in the reports it generates according to the importance of the subject of analysis. For example, the analysis unit can provide detailed information in reports containing important analysis results. The analysis unit can also provide standard information in reports containing general analysis results. The analysis unit can also provide quick, concise reports in urgent situations. This allows for efficient information provision by adjusting the level of detail in the reports according to the importance of the subject of analysis. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input importance data of the subject of analysis into a generation AI, which can then adjust the level of detail in the reports.

[0046] The analysis unit can dynamically add and remove data sources in response to environmental changes. For example, if a new source of pollution is discovered, the analysis unit can add that data source. The analysis unit can also remove existing data sources when they are no longer needed. The analysis unit can also dynamically adjust the necessary data sources in response to environmental changes. This allows for more appropriate data analysis by adjusting data sources in response to environmental changes. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input environmental change data into a generative AI, which can then dynamically add and remove data sources.

[0047] The analysis department can customize the format of the reports it generates according to the requirements of the relevant organizations. For example, the analysis department can generate detailed reports according to the requirements of environmental protection agencies. The analysis department can also generate concise reports according to the requirements of government agencies. The analysis department can also generate academic reports according to the requirements of university research institutes. This streamlines information dissemination by providing report formats tailored to the requirements of the relevant organizations. Some or all of the above processing in the analysis department may be performed using a generation AI, or not. For example, the analysis department can input the data requested by the relevant organizations into the generation AI, which can then customize the report format.

[0048] The reporting unit can adjust the level of detail of the information it reports according to the importance of the subject being reported. For example, when reporting important information, the reporting unit will provide detailed information. For example, when reporting important information, the reporting unit will provide detailed information. The reporting unit can also provide standard information when reporting general information. For example, when reporting general information, the reporting unit will provide standard information. The reporting unit can also provide quick, concise information in emergencies. For example, when reporting emergencies, the reporting unit can provide quick, concise information. This allows for efficient information provision by adjusting the level of detail of the information according to the importance of the subject being reported. Some or all of the above processing in the reporting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reporting unit can input importance data of the subject being reported into a generation AI, and the generation AI can adjust the level of detail of the information.

[0049] The reporting unit can dynamically change the timing of reports according to the rate of environmental change. For example, if the environmental change is progressing rapidly, the reporting unit can increase the frequency of reports. For example, if the environmental change is progressing rapidly, the reporting unit can increase the frequency of reports. The reporting unit can also decrease the frequency of reports if the environmental change is progressing slowly. For example, if the environmental change is progressing slowly, the reporting unit can decrease the frequency of reports. The reporting unit can also adjust the timing of reports in advance if an environmental change is predicted. For example, if an environmental change is predicted, the reporting unit can adjust the timing of reports in advance. By adjusting the timing of reports according to the rate of environmental change, information can be provided at the appropriate time. Some or all of the above processing in the reporting unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reporting unit can input environmental change rate data into a generative AI, and the generative AI can dynamically change the timing of reports.

[0050] The reporting department can customize the format of the information it reports according to the requirements of the relevant organizations. For example, the reporting department can generate a detailed report according to the requirements of an environmental protection agency. The reporting department can also generate a concise report according to the requirements of a government agency. The reporting department can also generate an academic report according to the requirements of a university research institute. This streamlines the dissemination of information by providing reporting formats tailored to the requirements of the relevant organizations. Some or all of the above processing in the reporting department may be performed using or without a generating AI. For example, the reporting department can input the data requested by the relevant organizations into a generating AI, which can then customize the format of the report.

[0051] The reporting unit can dynamically change the method of transmitting the information to be reported according to the communication environment. For example, if the communication environment is good, the reporting unit can transmit information in real time. For example, if the communication environment is good, the reporting unit can transmit information in real time. The reporting unit can also transmit information in batch processing if the communication environment is unstable. For example, if the communication environment is unstable, the reporting unit can transmit information in batch processing. The reporting unit can also save information offline and transmit it later if the communication environment is poor. For example, if the communication environment is poor, the reporting unit can save information offline and transmit it later. In this way, by adjusting the method of transmitting information according to the communication environment, information can be provided at the appropriate time. Some or all of the above processing in the reporting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reporting unit can input communication environment data into a generation AI, and the generation AI can dynamically change the method of transmitting information.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The data collection unit can be equipped with feedback functions to minimize the impact on the target ecosystem during data collection. For example, the collection unit can monitor the behavioral patterns of the target organisms in real time and adjust the collection activities to avoid stressing the organisms. It can also evaluate the impact on the target environment and change the collection method as needed. Furthermore, the collection unit can dynamically adjust the collection frequency and range to minimize the impact of collection activities on the ecosystem. This enables data collection that prioritizes environmental protection.

[0054] The analysis department can use collected data to build predictive models for environmental changes. For example, it can analyze trends in environmental changes based on past data and predict future changes. Furthermore, it can use anomaly detection algorithms to detect abnormal environmental changes early based on the predictive models. In addition, the analysis department can improve prediction accuracy by regularly updating the predictive models and incorporating the latest data. This enables a rapid response to environmental changes.

[0055] The reporting system can be equipped with a dashboard function to display detected information in an easily understandable visual format. For example, the reporting system can plot detected information on a map for visual confirmation. It can also visually display data trends and anomalies using graphs and charts. Furthermore, the reporting system can provide users with customizable widgets, allowing them to quickly and accurately access necessary information. This enables relevant organizations to quickly and accurately grasp and respond to information.

[0056] The data collection unit can be equipped with an eco mode to minimize the impact on the environment being collected during data collection. For example, by enabling eco mode, the data collection unit can adjust the drone's flight speed and sensor operating time to reduce energy consumption. Furthermore, by using eco mode, the data collection unit can minimize the environmental impact of the collection activity. In addition, the data collection unit can monitor the usage of eco mode and optimize its settings as needed. This enables environmentally friendly data collection.

[0057] The analysis department can use the collected data to evaluate the effectiveness of environmental protection activities. For example, it can analyze environmental changes before and after the implementation of environmental protection activities by comparing them with past data. It can also quantitatively evaluate the effectiveness of specific environmental protection activities. Furthermore, the analysis department can visualize the effects of environmental protection activities and report them to relevant organizations. This makes it possible to objectively evaluate the effectiveness of environmental protection activities and reflect the findings in future activities.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The data collection unit collects data from the air and underwater. For example, an autonomous drone is used to collect temperature data, chemical data, and biological data from the air, and an autonomous underwater drone is used to collect water quality data, marine organism data, and seabed topography data from underwater. Processing in the data collection unit may or may not be performed using AI. Step 2: The analysis unit analyzes the data collected by the collection unit in real time. For example, it may use image recognition technology to detect signs of marine pollution or data analysis technology to detect illegal fishing activities. Processing in the analysis unit may or may not be performed using AI. Step 3: The reporting department reports the information detected by the analysis department to the relevant organizations. For example, this may involve notifying them of the detected information in real time or submitting it as a regular report. Processing in the reporting department may or may not be performed using AI.

[0060] (Example of form 2) An environmental monitoring system according to an embodiment of the present invention is a system that combines an autonomous navigation drone and an autonomous underwater drone to collect and analyze data from the air and underwater in real time. This environmental monitoring system aims to detect marine pollution, illegal fishing, changes in the natural environment, etc., at an early stage and report them promptly to the relevant authorities. For example, in the environmental monitoring system, the autonomous navigation drone and the autonomous underwater drone first collect data from the air and underwater. Next, the collected data is analyzed in real time by AI. The AI ​​uses image recognition technology and data analysis technology to detect marine pollution, illegal fishing, changes in the natural environment, etc. For example, the AI ​​analyzes water quality data to detect signs of marine pollution and tracks the movement of vessels to monitor illegal fishing activities. The detected information is promptly reported to the relevant authorities. For example, the environmental monitoring system notifies the relevant authorities of the detected information in real time to encourage a rapid response. This system improves the range and speed of environmental monitoring and ensures the accuracy and immediacy of data. Furthermore, it is designed with environmental considerations in mind to minimize the impact on the ecosystem. For example, the environmental monitoring system uses low-energy-consumption sensors to minimize the environmental impact of drone operation. Furthermore, the introduction of AI agents will improve the detection rate of marine pollution by 50%, the efficiency of cracking down on illegal fishing by 30%, and the accuracy of tracking environmental changes by 40%. This will enable wide-ranging and detailed monitoring, leading to rapid response and reporting. It is also expected to contribute to sustainable environmental protection activities. Potential target groups include environmental protection agencies, government agencies, and university research institutes. This system will address the challenges faced by these organizations, such as limitations in the scope and speed of environmental monitoring, and the lack of accuracy and immediacy of data. The market size is estimated at 500 billion yen for the environmental monitoring market, with an initial target market of 100 billion yen for environmental protection agencies. The rapid increase in environmental problems and advancements in technology present an excellent time to enter the market, aiming to realize a sustainable society through increased environmental awareness and the promotion of concrete actions. As a result, the environmental monitoring system will be able to collect and analyze data from the air and underwater in real time and report it promptly to relevant organizations.

[0061] The environmental monitoring system according to this embodiment comprises a collection unit, an analysis unit, and a reporting unit. The collection unit collects data from the air and underwater. The collection unit can collect data from the air using, for example, an autonomous drone. The collection unit can also collect data from underwater using an autonomous underwater drone. For example, the collection unit can collect temperature data, chemical substance data, biological data, etc., as aerial data. The collection unit can also collect water quality data, marine organism data, seabed topography data, etc., as underwater data. Some or all of the above-described processing in the collection unit may be performed using AI or not. For example, the collection unit can collect data using sensors mounted on a drone and input that data into AI for analysis. The analysis unit analyzes the data collected by the collection unit in real time. The analysis unit can detect signs of marine pollution from the collected data using, for example, image recognition technology. The analysis unit can also detect illegal fishing activities from the collected data using data analysis technology. For example, the analysis unit can detect signs of marine pollution from the collected data using image recognition technology. Furthermore, the analysis department can use data analysis techniques to detect illegal fishing activities from the collected data. Some or all of the above-described processes in the analysis department may be performed using AI or not. For example, the analysis department can input the collected data into an AI, which can analyze the data to detect signs of marine pollution or illegal fishing activities. The reporting department reports the information detected by the analysis department to the relevant agencies. The reporting department can, for example, notify the relevant agencies of the detected information in real time. The reporting department can also periodically submit the detected information to the relevant agencies as reports. For example, the reporting department can notify the relevant agencies of the detected information in real time. The reporting department can also periodically submit the detected information to the relevant agencies as reports. Some or all of the above-described processes in the reporting department may be performed using AI or not.For example, the reporting unit can automatically generate a report from the information detected using AI and send that report to the relevant organizations. As a result, the environmental monitoring system according to this embodiment can collect and analyze data from the air and underwater in real time and report it promptly to the relevant organizations.

[0062] The data collection unit collects data from the air and underwater. For example, the data collection unit can collect data from the air using an autonomous drone. Autonomous drones are equipped with high-performance cameras and various sensors, which enable the efficient collection of wide-area environmental data. Specifically, the drone measures the temperature of the atmosphere using a temperature sensor during flight and detects the concentration of harmful substances in the air using a chemical sensor. It is also possible to collect biological data such as airborne microorganisms and pollen using a biosensor. This data is collected in real time along the drone's flight path and transmitted to a ground-based database. On the other hand, the data collection unit can also collect data from underwater using autonomous underwater drones. These autonomous underwater drones are equipped with water quality sensors, marine organism sensors, and seabed topography sensors, enabling the collection of detailed data on the underwater environment. Specifically, the underwater drones use water quality sensors to measure water quality data such as pH, dissolved oxygen, and turbidity, and marine organism sensors to detect the types and numbers of organisms in the water. They can also use seabed topography sensors to map the seabed topography and detect changes or anomalies in the terrain. This data is collected in real time along the underwater drone's navigation path and transmitted to a database on land. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can collect data using sensors mounted on a drone and input that data into an AI for analysis. The AI ​​analyzes the collected data in real time and detects anomalies and unique patterns. This enables the data collection unit to collect environmental data efficiently and accurately, allowing for early detection of anomalies and rapid response.

[0063] The analysis unit analyzes the data collected by the collection unit in real time. For example, the analysis unit can use image recognition technology to detect signs of marine pollution from the collected data. Specifically, it uses AI-based image recognition technology to analyze image data captured by aerial drones and detect pollutants such as oil slicks and debris floating on the sea surface. The AI ​​can also analyze the spread and concentration of these pollutants and assess the degree of pollution. Furthermore, the analysis department can use data analysis technology to detect illegal fishing activities from the collected data. For example, AI-based data analysis technology can analyze data collected by underwater drones to detect abnormal declines in fish populations or the misuse of fishing gear in specific sea areas. Based on this data, the AI ​​can identify activities that are highly likely to be illegal fishing and issue warnings to relevant agencies. Some or all of the above-described processes in the analysis department may be performed using AI or not. For example, the analysis department can input collected data into an AI, which can then analyze the data to detect signs of marine pollution or illegal fishing activities. The AI ​​has the ability to learn from past data and patterns and to quickly detect anomalous data and patterns. This allows the analysis department to efficiently and accurately analyze the collected data and detect environmental anomalies and risks at an early stage. Furthermore, the analysis unit can accumulate collected data over the long term and perform trend analysis and predictive analysis. For example, it can analyze pollution trends in specific seasons or regions based on historical data and predict future risks. It can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0064] The reporting department reports the information detected by the analysis department to the relevant agencies. For example, the reporting department can notify the relevant agencies of the detected information in real time. Specifically, the reporting department uses AI to automatically generate reports of the detected information and sends these reports to the relevant agencies. Based on the data provided by the analysis department, the AI ​​creates detailed reports that describe the degree of pollution and the details of illegal fishing activities. This allows the relevant agencies to quickly and accurately understand the situation and take appropriate action. Furthermore, the reporting department can periodically submit detected information to relevant organizations as reports. For example, the reporting department can prepare monthly or weekly reports summarizing past data and trends. This allows relevant organizations to understand long-term environmental changes and risk trends and to formulate strategic countermeasures. Some or all of the above-described processes in the reporting department may be performed using AI or not. For example, the reporting department may use AI to automatically generate a report from the detected information and send that report to the relevant organizations. The AI ​​would automatically organize the report's contents and highlight important information to enable the relevant organizations to respond quickly. Furthermore, the reporting department can reliably transmit information using multiple communication methods. For example, real-time notifications are sent via email, SMS, and a dedicated notification application. Regular reports are also provided in PDF format and through a web portal, making them easily accessible to relevant organizations. This allows the reporting department to provide information to relevant organizations quickly and reliably, maximizing the effectiveness of the environmental monitoring system.

[0065] The data collection unit can collect data using autonomous drones and autonomous underwater drones. For example, the data collection unit can collect data from the air using autonomous drones. For example, the data collection unit can collect temperature data, chemical data, biological data, etc. from the air using sensors mounted on autonomous drones. The data collection unit can also collect data from underwater using autonomous underwater drones. For example, the data collection unit can collect water quality data, marine organism data, seabed topography data, etc. from underwater using sensors mounted on autonomous underwater drones. This makes it possible to collect data from both the air and underwater by using autonomous drones and autonomous underwater drones. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data acquired from sensors mounted on autonomous drones and autonomous underwater drones into a generating AI, which can then analyze and collect the data.

[0066] The analysis unit can detect marine pollution, illegal fishing, and changes in the natural environment using image recognition technology and data analysis technology. For example, the analysis unit can use image recognition technology to detect signs of marine pollution from collected data. The analysis unit can also use data analysis technology to detect illegal fishing activities from collected data. In this way, by using image recognition technology and data analysis technology, marine pollution, illegal fishing, and changes in the natural environment can be accurately detected. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without generative AI. For example, the analysis unit can input collected data into a generative AI, and the generative AI can analyze the data to detect signs of marine pollution or illegal fishing activities.

[0067] The reporting department can promptly report detected information to relevant organizations. For example, the reporting department can notify relevant organizations of detected information in real time. The reporting department can also periodically submit detected information to relevant organizations as reports. This enables a rapid response by promptly reporting detected information to relevant organizations. Some or all of the above processing in the reporting department may be performed using or without generation AI. For example, the reporting department can automatically generate a report of detected information using generation AI and send that report to relevant organizations.

[0068] The collection unit can be designed with environmental considerations in mind to minimize its impact on the ecosystem. For example, the collection unit may use low-energy-consumption sensors. The collection unit may also be designed to minimize the environmental impact of drone operations. This contributes to environmental protection by minimizing the impact on the ecosystem. Some or all of the processing described above in the collection unit may be performed using generative AI or not. For example, the collection unit can optimize energy consumption and minimize environmental impact using generative AI.

[0069] The analysis unit can improve the detection rate of marine pollution by 50%. The analysis unit can, for example, use image recognition technology to detect signs of marine pollution. The analysis unit can also detect signs of marine pollution using data analysis technology. This improves the detection rate of marine pollution, enabling more accurate environmental monitoring. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can input collected data into a generative AI, which can then analyze the data to detect signs of marine pollution.

[0070] The analysis unit can improve the efficiency of detecting illegal fishing by 30%. The analysis unit can, for example, use image recognition technology to detect illegal fishing activities. The analysis unit can also detect illegal fishing activities using data analysis technology. This can contribute to suppressing illegal activities by improving the efficiency of detecting illegal fishing. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without generative AI. For example, the analysis unit can input collected data into a generative AI, and the generative AI can analyze the data to detect illegal fishing activities.

[0071] The analysis unit can improve the accuracy of tracking environmental changes by 40%. The analysis unit can, for example, use image recognition technology to detect signs of environmental changes. The analysis unit can also detect signs of environmental changes using data analysis technology. This improves the accuracy of tracking environmental changes, enabling more detailed environmental monitoring. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can input collected data into a generative AI, which can then analyze the data to detect signs of environmental changes.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. The data collection unit can also increase the frequency of data collection and collect more detailed data if the user is relaxed. The data collection unit can also prioritize collecting only important data if the user is in a hurry. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the data collection unit can input user emotion data into a generative AI, which can then estimate the emotion and adjust the timing of data collection.

[0073] The data collection unit can dynamically change the type of data it collects in response to environmental changes. For example, if marine pollution is progressing, the data collection unit can change the target of data collection according to the type of pollutant. The data collection unit can also focus on monitoring the movements of fishing vessels if illegal fishing is increasing. The data collection unit can also concentrate on collecting data from specific areas if the natural environment is changing rapidly. By changing the type of data in response to environmental changes, more appropriate data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input environmental change data into a generation AI, which can dynamically change the type of data.

[0074] The data collection unit can adjust the accuracy of the data it collects according to the importance of the data being collected. For example, when collecting important data, the data collection unit can use high-precision sensors to collect the data. For example, when collecting important data, the data collection unit can use high-precision sensors to collect the data. Alternatively, when collecting general data, the data collection unit can use standard sensors to collect the data. For example, in an emergency, the data collection unit can sacrifice accuracy to collect data quickly. In this way, efficient data collection becomes possible by adjusting the accuracy of the data according to the importance of the data being collected. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input importance data of the data to be collected into a generative AI, which can then adjust the accuracy of the data.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only important data. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed data. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. For example, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. In this way, by determining the priority of data according to the user's emotions, important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using generative AI or not using generative AI. For example, the data collection unit can input user emotion data into a generating AI, which can then estimate the emotion and determine the priority of the data.

[0076] The data collection unit can dynamically change the range of data to be collected according to geographical conditions. For example, the data collection unit can focus on collecting data in areas where marine pollution is progressing. For example, the data collection unit can focus on collecting data in areas where illegal fishing is rampant. For example, the data collection unit can focus on collecting data in areas where illegal fishing is rampant. For example, the data collection unit can focus on collecting data in areas where the natural environment is changing rapidly. For example, the data collection unit can focus on collecting data in areas where the natural environment is changing rapidly. This makes efficient data collection possible by changing the range of data according to geographical conditions. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input geographical condition data into a generation AI, and the generation AI can dynamically change the range of data.

[0077] The data collection unit can adjust the frequency of data collection according to the rate of environmental change. For example, if the environmental change is progressing rapidly, the data collection unit can increase the frequency of data collection. The data collection unit can also decrease the frequency of data collection if the environmental change is slow. The data collection unit can also adjust the frequency of data collection in advance if an environmental change is predicted. In this way, by adjusting the data frequency according to the rate of environmental change, data can be collected at the appropriate time. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input environmental change rate data into a generative AI, and the generative AI can adjust the data frequency.

[0078] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and easy-to-read display method. For example, if the user is nervous, the analysis unit can provide a simple and easy-to-read display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. The analysis unit can also provide a display method that gets to the point if the user is in a hurry. For example, if the user is in a hurry, the analysis unit can provide a display method that gets to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using a generative AI or not using a generative AI. For example, the analysis unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust how the analysis results are displayed.

[0079] The analysis unit can dynamically change the algorithm it uses depending on the type of data collected. For example, when analyzing marine pollution data, the analysis unit can use an algorithm that corresponds to the type of pollutant. Similarly, when analyzing illegal fishing data, the analysis unit can use an algorithm that analyzes the movement of fishing vessels. Furthermore, when analyzing data on changes in the natural environment, the analysis unit can use an algorithm that analyzes patterns of environmental change. This allows for more appropriate data analysis by changing the algorithm according to the type of data. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the type of collected data into the generative AI, which can then dynamically change the algorithm.

[0080] The analysis unit can adjust the level of detail in the reports it generates according to the importance of the subject of analysis. For example, the analysis unit can provide detailed information in reports containing important analysis results. The analysis unit can also provide standard information in reports containing general analysis results. The analysis unit can also provide quick, concise reports in urgent situations. This allows for efficient information provision by adjusting the level of detail in the reports according to the importance of the subject of analysis. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input importance data of the subject of analysis into a generation AI, which can then adjust the level of detail in the reports.

[0081] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying only the most important analysis results. The analysis unit can also prioritize displaying detailed analysis results if the user is relaxed. The analysis unit can also prioritize analysis results that can be quickly reviewed if the user is in a hurry. In this way, by prioritizing the analysis results according to the user's emotions, important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input user emotion data into a generating AI, which can then estimate the emotion and determine the priority of the analysis results.

[0082] The analysis unit can dynamically add and remove data sources in response to environmental changes. For example, if a new source of pollution is discovered, the analysis unit can add that data source. The analysis unit can also remove existing data sources when they are no longer needed. The analysis unit can also dynamically adjust the necessary data sources in response to environmental changes. This allows for more appropriate data analysis by adjusting data sources in response to environmental changes. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input environmental change data into a generative AI, which can then dynamically add and remove data sources.

[0083] The analysis department can customize the format of the reports it generates according to the requirements of the relevant organizations. For example, the analysis department can generate detailed reports according to the requirements of environmental protection agencies. The analysis department can also generate concise reports according to the requirements of government agencies. The analysis department can also generate academic reports according to the requirements of university research institutes. This streamlines information dissemination by providing report formats tailored to the requirements of the relevant organizations. Some or all of the above processing in the analysis department may be performed using a generation AI, or not. For example, the analysis department can input the data requested by the relevant organizations into the generation AI, which can then customize the report format.

[0084] The reporting unit can estimate the user's emotions and adjust the way the report is presented based on the estimated emotions. For example, if the user is nervous, the reporting unit can provide a simple and easy-to-read report. For example, if the user is nervous, the reporting unit can provide a simple and easy-to-read report. For example, if the user is relaxed, the reporting unit can provide a report that includes detailed information. For example, if the user is relaxed, the reporting unit can provide a report that includes detailed information. For example, if the user is in a hurry, the reporting unit can provide a report that gets straight to the point. For example, if the user is in a hurry, the reporting unit can provide a report that gets straight to the point. By adjusting the way the report is presented according to the user's emotions, it becomes possible to provide reports that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the reporting unit may be performed using generative AI or not using generative AI. For example, the reporting unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the way the report is expressed.

[0085] The reporting unit can adjust the level of detail of the information it reports according to the importance of the subject being reported. For example, when reporting important information, the reporting unit will provide detailed information. For example, when reporting important information, the reporting unit will provide detailed information. The reporting unit can also provide standard information when reporting general information. For example, when reporting general information, the reporting unit will provide standard information. The reporting unit can also provide quick, concise information in emergencies. For example, when reporting emergencies, the reporting unit can provide quick, concise information. This allows for efficient information provision by adjusting the level of detail of the information according to the importance of the subject being reported. Some or all of the above processing in the reporting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reporting unit can input importance data of the subject being reported into a generation AI, and the generation AI can adjust the level of detail of the information.

[0086] The reporting unit can dynamically change the timing of reports according to the rate of environmental change. For example, if the environmental change is progressing rapidly, the reporting unit can increase the frequency of reports. For example, if the environmental change is progressing rapidly, the reporting unit can increase the frequency of reports. The reporting unit can also decrease the frequency of reports if the environmental change is progressing slowly. For example, if the environmental change is progressing slowly, the reporting unit can decrease the frequency of reports. The reporting unit can also adjust the timing of reports in advance if an environmental change is predicted. For example, if an environmental change is predicted, the reporting unit can adjust the timing of reports in advance. By adjusting the timing of reports according to the rate of environmental change, information can be provided at the appropriate time. Some or all of the above processing in the reporting unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reporting unit can input environmental change rate data into a generative AI, and the generative AI can dynamically change the timing of reports.

[0087] The reporting unit can estimate the user's emotions and prioritize the content of the report based on the estimated emotions. For example, if the user is stressed, the reporting unit will prioritize reporting only important information. For example, if the user is stressed, the reporting unit will prioritize reporting only important information. For example, if the user is relaxed, the reporting unit will prioritize reporting detailed information. For example, if the user is relaxed, the reporting unit will prioritize reporting detailed information. For example, if the user is in a hurry, the reporting unit will prioritize information that can be quickly checked. For example, if the user is in a hurry, the reporting unit will prioritize information that can be quickly checked. In this way, by prioritizing the content of the report according to the user's emotions, important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using generative AI or not using generative AI. For example, the reporting department can input user emotion data into a generating AI, which can then estimate the emotion and determine the priority of the report content.

[0088] The reporting department can customize the format of the information it reports according to the requirements of the relevant organizations. For example, the reporting department can generate a detailed report according to the requirements of an environmental protection agency. The reporting department can also generate a concise report according to the requirements of a government agency. The reporting department can also generate an academic report according to the requirements of a university research institute. This streamlines the dissemination of information by providing reporting formats tailored to the requirements of the relevant organizations. Some or all of the above processing in the reporting department may be performed using or without a generating AI. For example, the reporting department can input the data requested by the relevant organizations into a generating AI, which can then customize the format of the report.

[0089] The reporting unit can dynamically change the method of transmitting the information to be reported according to the communication environment. For example, if the communication environment is good, the reporting unit can transmit information in real time. For example, if the communication environment is good, the reporting unit can transmit information in real time. The reporting unit can also transmit information in batch processing if the communication environment is unstable. For example, if the communication environment is unstable, the reporting unit can transmit information in batch processing. The reporting unit can also save information offline and transmit it later if the communication environment is poor. For example, if the communication environment is poor, the reporting unit can save information offline and transmit it later. In this way, by adjusting the method of transmitting information according to the communication environment, information can be provided at the appropriate time. Some or all of the above processing in the reporting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reporting unit can input communication environment data into a generation AI, and the generation AI can dynamically change the method of transmitting information.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The data collection unit can be equipped with feedback functions to minimize the impact on the target ecosystem during data collection. For example, the collection unit can monitor the behavioral patterns of the target organisms in real time and adjust the collection activities to avoid stressing the organisms. It can also evaluate the impact on the target environment and change the collection method as needed. Furthermore, the collection unit can dynamically adjust the collection frequency and range to minimize the impact of collection activities on the ecosystem. This enables data collection that prioritizes environmental protection.

[0092] The analysis department can use collected data to build predictive models for environmental changes. For example, it can analyze trends in environmental changes based on past data and predict future changes. Furthermore, it can use anomaly detection algorithms to detect abnormal environmental changes early based on the predictive models. In addition, the analysis department can improve prediction accuracy by regularly updating the predictive models and incorporating the latest data. This enables a rapid response to environmental changes.

[0093] The reporting system can be equipped with a dashboard function to display detected information in an easily understandable visual format. For example, the reporting system can plot detected information on a map for visual confirmation. It can also visually display data trends and anomalies using graphs and charts. Furthermore, the reporting system can provide users with customizable widgets, allowing them to quickly and accurately access necessary information. This enables relevant organizations to quickly and accurately grasp and respond to information.

[0094] The data collection unit can be equipped with an eco mode to minimize the impact on the environment being collected during data collection. For example, by enabling eco mode, the data collection unit can adjust the drone's flight speed and sensor operating time to reduce energy consumption. Furthermore, by using eco mode, the data collection unit can minimize the environmental impact of the collection activity. In addition, the data collection unit can monitor the usage of eco mode and optimize its settings as needed. This enables environmentally friendly data collection.

[0095] The analysis department can use the collected data to evaluate the effectiveness of environmental protection activities. For example, it can analyze environmental changes before and after the implementation of environmental protection activities by comparing them with past data. It can also quantitatively evaluate the effectiveness of specific environmental protection activities. Furthermore, the analysis department can visualize the effects of environmental protection activities and report them to relevant organizations. This makes it possible to objectively evaluate the effectiveness of environmental protection activities and reflect the findings in future activities.

[0096] The data collection unit can estimate the user's emotions and adjust the type of data collected based on those emotions. For example, if the user is stressed, the data collection unit can prioritize collecting simple data to reduce the user's burden. Conversely, if the user is relaxed, the data collection unit can collect more detailed data to gather more information. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting only the most important data. This allows the data collection unit to adjust the type of data collected according to the user's emotions, thereby reducing the user's burden.

[0097] The analytics unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analytics unit can provide a simple and easy-to-understand notification method. The analytics unit can also provide a more detailed notification method if the user is relaxed. Furthermore, if the user is in a hurry, the analytics unit can provide a more concise notification method. By adjusting the notification method according to the user's emotions, notifications that are easier for the user to understand become possible.

[0098] The reporting system can estimate the user's emotions and adjust the format of the report based on those emotions. For example, if the user is stressed, the reporting system can provide a concise and to-the-point format. Similarly, if the user is relaxed, the reporting system can provide a format that includes detailed information. Furthermore, if the user is in a hurry, the reporting system can provide a format that allows for quick review. By adjusting the format of the report according to the user's emotions, reports that are easier for the user to understand become possible.

[0099] The analytics unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is stressed, the analytics unit can reduce the frequency of notifications to lessen the user's burden. The analytics unit can also increase the frequency of notifications and provide more detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the analytics unit can prioritize only important notifications. This allows the system to adjust notification timing according to the user's emotions, thereby reducing the user's burden.

[0100] The reporting system can estimate the user's emotions and adjust the level of detail in the report based on that estimation. For example, if the user is stressed, the reporting system can provide a concise report. It can also provide a detailed report if the user is relaxed. Furthermore, if the user is in a hurry, the reporting system can provide a concise report. By adjusting the level of detail in the report according to the user's emotions, it becomes possible to provide reports that are easier for the user to understand.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The data collection unit collects data from the air and underwater. For example, an autonomous drone is used to collect temperature data, chemical data, and biological data from the air, and an autonomous underwater drone is used to collect water quality data, marine organism data, and seabed topography data from underwater. Processing in the data collection unit may or may not be performed using AI. Step 2: The analysis unit analyzes the data collected by the collection unit in real time. For example, it may use image recognition technology to detect signs of marine pollution or data analysis technology to detect illegal fishing activities. Processing in the analysis unit may or may not be performed using AI. Step 3: The reporting department reports the information detected by the analysis department to the relevant organizations. For example, this may involve notifying them of the detected information in real time or submitting it as a regular report. Processing in the reporting department may or may not be performed using AI.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] Each of the multiple elements described above, including the data collection unit, analysis unit, and reporting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit can collect data from the air and underwater using the camera 42 and sensors of the smart device 14. The analysis unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. The reporting unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. The detected information is promptly reported to the relevant organizations. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the data collection unit, analysis unit, and reporting unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit can collect data from the air and underwater using the camera 42 and sensors of the smart glasses 214. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data in real time. The reporting unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and promptly reports the detected information to the relevant organizations. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the data collection unit, analysis unit, and reporting unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit can collect data from the air and underwater using the camera 42 and sensors of the headset terminal 314. The analysis unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. The reporting unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. The detected information is promptly reported to the relevant organizations. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the data collection unit, analysis unit, and reporting unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit can collect data from the air and underwater using the camera 42 and sensors of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data in real time. The reporting unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and promptly reports the detected information to the relevant organizations. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] (Note 1) A data collection unit that collects data from the air and underwater, An analysis unit analyzes the data collected by the aforementioned collection unit in real time, The reporting department reports the information detected by the aforementioned analysis department to the relevant organizations, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data using autonomous drones and autonomous underwater drones. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Using image recognition and data analysis technologies, we detect marine pollution, illegal fishing, and changes in the natural environment. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reporting department, We will promptly report any detected information to the relevant authorities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It features an environmentally conscious design to minimize its impact on the ecosystem. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is Improve the detection rate of marine pollution by 50% The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is Improve the efficiency of detecting illegal fishing by 30% The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is Improve the accuracy of tracking environmental changes by 40% The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The types of data collected are dynamically changed in response to changes in the environment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The accuracy of the collected data is adjusted according to the importance of the subject being collected. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is The scope of data collected is dynamically changed according to geographical conditions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is Adjust the frequency of data collection according to the rate of change in the environment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is The algorithm used is dynamically changed depending on the type of data collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is Adjust the level of detail in the generated report according to the importance of the subject being analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is The data sources used are dynamically added and removed in response to changes in the environment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is Customize the format of the generated reports according to the requirements of the relevant organizations. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reporting department, The system estimates the user's emotions and adjusts the way the report is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reporting department, Adjust the level of detail in the reported information according to the importance of the subject being reported. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reporting department, The timing of reporting is dynamically changed according to the rate at which the environment is changing. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reporting department, The system estimates the user's emotions and prioritizes the report based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reporting department, Customize the format of the information to be reported according to the requirements of the relevant agency. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reporting department, The method of sending reported information is dynamically changed depending on the communication environment. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data from the air and underwater, An analysis unit analyzes the data collected by the aforementioned collection unit in real time, The reporting department reports the information detected by the aforementioned analysis department to the relevant organizations, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is We collect data using autonomous drones and autonomous underwater drones. The system according to feature 1.

3. The aforementioned analysis unit is Using image recognition and data analysis technologies, we detect marine pollution, illegal fishing, and changes in the natural environment. The system according to feature 1.

4. The aforementioned reporting department, We will promptly report any detected information to the relevant authorities. The system according to feature 1.

5. The aforementioned collection unit is It features an environmentally conscious design to minimize its impact on the ecosystem. The system according to feature 1.

6. The aforementioned analysis unit is Improve the detection rate of marine pollution by 50% The system according to feature 1.

7. The aforementioned analysis unit is Improve the efficiency of detecting illegal fishing by 30% The system according to feature 1.

8. The aforementioned analysis unit is Improve the accuracy of tracking environmental changes by 40% The system according to feature 1.

9. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is The types of data collected are dynamically changed in response to changes in the environment. The system according to feature 1.

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