system

Abstract

The system according to this embodiment aims to autonomously control a drone and efficiently collect and analyze environmental data. [Solution] The system according to the embodiment comprises a planning unit, a data collection unit, and an analysis unit. The planning unit plans the flight path of the drone. The data collection unit autonomously flies the drone based on the flight path planned by the planning unit. The analysis unit analyzes the data collected by the data collection unit in real time.

Description

Technical Field

[0003]

[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 the chatbot's 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 prior art, in the collection of wide-area environmental data using drones, efficient flight path planning and real-time data analysis have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to autonomously control a drone and efficiently collect and analyze environmental data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a planning unit, a data collection unit, and an analysis unit. The planning unit plans the flight path of the drone. The data collection unit autonomously flies the drone based on the flight path planned by the planning unit. The analysis unit analyzes the data collected by the data collection unit in real time. [Effects of the Invention]

[0007] The system according to this embodiment can autonomously control a drone and efficiently collect and analyze environmental data. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 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 �2. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An environmental data collection system according to an embodiment of the present invention is a system that autonomously controls multiple drones using generative AI to collect and analyze wide-area environmental data in real time. The environmental data collection system optimizes the flight paths and schedules of the drones using generative AI to achieve efficient data collection. Based on the collected data, the environmental data collection system immediately detects environmental anomalies and proposes countermeasures. For example, in the environmental data collection system, the generative AI plans the flight paths of the drones, and the drones fly autonomously to collect environmental data. The collected data is analyzed in real time, and if an anomaly is detected, the generative AI immediately proposes countermeasures. This system reduces the time and cost required for collecting environmental data and enables efficient monitoring of wide areas. It also prevents delays in anomaly detection and enables rapid response. As a result, the environmental data collection system reduces the time and cost required for collecting environmental data and enables efficient monitoring of wide areas.

[0029] The environmental data collection system according to this embodiment comprises a planning unit, a collection unit, and an analysis unit. The planning unit plans the flight path of the drone. The planning unit optimizes the flight path of the drone using, for example, a generative AI. The planning unit can plan a path considering the length, safety, and efficiency of the flight path. The planning unit can, for example, have the generative AI plan the optimal path based on real-time data. The planning unit can also have the generative AI analyze past flight data and select the optimal path. The planning unit can also have the generative AI optimize the path considering weather and terrain information. The collection unit autonomously flies the drone based on the flight path planned by the planning unit. The collection unit autonomously controls the drone using, for example, a generative AI. The collection unit monitors the drone's flight path in real time and can adjust the path as needed. The collection unit can also have the generative AI monitor the drone's battery level and plan the optimal path. The collection unit can also have the generative AI plan a path to avoid collisions with other drones. The analysis unit analyzes the data collected by the collection unit in real time. The analysis unit analyzes the collected data, for example, using a generative AI. Based on the collected data, the analysis unit can detect environmental anomalies. The analysis unit can also propose countermeasures when the generative AI detects an anomaly. The analysis unit can also optimize the analysis algorithm by allowing the generative AI to refer to past analysis data. As a result, the environmental data collection system according to this embodiment can plan the drone's flight path, fly it autonomously, and analyze the collected data in real time.

[0030] The planning department plans the drone's flight path. For example, the planning department optimizes the drone's flight path using generative AI. Specifically, when planning the drone's flight path, the generative AI plans the optimal path based on real-time data. The generative AI can also analyze past flight data and select the optimal path. For example, past flight data includes the drone's flight path, flight time, battery consumption, and the location of obstacles. Based on this data, the generative AI plans the optimal flight path. Furthermore, the generative AI can also optimize the path by considering weather and terrain information. For example, weather information includes wind speed, wind direction, rainfall, and temperature, while terrain information includes the location of mountains, valleys, and buildings. Based on this information, the generative AI plans a path that allows the drone to fly safely and efficiently. The planning department transmits the flight path planned by the generative AI to the drone and instructs the drone to fly according to that path. This allows the planning department to plan the drone's flight path efficiently and safely.

[0031] The collection unit autonomously flies the drone based on the flight path planned by the planning unit. The collection unit autonomously controls the drone, for example, using generative AI. Specifically, the generative AI monitors the drone's flight path in real time and adjusts the path as needed. For example, if the drone encounters an unexpected obstacle during flight, the generative AI immediately changes the drone's flight path to avoid the obstacle. The generative AI can also monitor the drone's battery level and plan the optimal path before the battery runs out. For example, if the drone is flying for a long period, the generative AI considers battery consumption and plans a path that passes through points where the battery can be replaced or charged along the way. Furthermore, the generative AI can also plan a path to avoid collisions with other drones. For example, if multiple drones are flying in the same airspace, the generative AI adjusts the flight path of each drone to minimize the risk of collision. The collection unit autonomously flies the drone according to the flight path planned by the generative AI and collects environmental data. This allows the collection unit to fly the drone efficiently and safely and reliably collect the necessary data.

[0032] The analysis unit analyzes the data collected by the collection unit in real time. The analysis unit analyzes the collected data using, for example, a generative AI. Specifically, the generative AI detects environmental anomalies based on the collected data. For example, it analyzes atmospheric gas concentration data and temperature data collected by a drone, and if abnormal values ​​are detected, the generative AI identifies the anomaly. Furthermore, the generative AI can also propose countermeasures when an anomaly is detected. For example, if an abnormal gas concentration is detected, the generative AI identifies the cause and proposes appropriate countermeasures. The generative AI can also optimize the analysis algorithm by referring to past analysis data. For example, it adjusts parameters to improve the accuracy of anomaly detection based on past data. Based on the results of the generative AI's analysis, the analysis unit can quickly detect environmental anomalies and take appropriate countermeasures. As a result, the analysis unit can efficiently and accurately analyze the collected data and detect environmental anomalies at an early stage.

[0033] The analysis unit can detect environmental anomalies based on the collected data. For example, the analysis unit uses a generative AI to analyze the collected data and detect anomalies. The analysis unit can detect environmental anomalies such as temperature anomalies, humidity anomalies, and gas leaks. The analysis unit can also use a generative AI to detect anomalies based on real-time data. Furthermore, the analysis unit can use a generative AI to detect anomalies by referring to past anomaly data. This allows for the detection of environmental anomalies based on collected data.

[0034] The analysis unit includes a countermeasure proposal unit that suggests countermeasures when an anomaly is detected. For example, the analysis unit uses a generation AI to suggest countermeasures when an anomaly is detected. The analysis unit can suggest countermeasures based on the algorithm and priority of the suggestions. The analysis unit can also have the generation AI suggest the most appropriate countermeasure depending on the type of anomaly. Furthermore, the analysis unit can have the generation AI suggest the most appropriate countermeasure by referring to past countermeasure history. This allows the system to suggest countermeasures when an anomaly is detected.

[0035] The planning department can optimize the drone's flight paths and schedules. For example, the planning department can use generative AI to optimize the drone's flight paths and schedules. The planning department can optimize routes and schedules based on the optimization objectives and the algorithms used. The planning department can also have the generative AI optimize routes and schedules based on real-time data. Furthermore, the planning department can have the generative AI optimize routes and schedules by referencing past flight data. This allows for the optimization of the drone's flight paths and schedules.

[0036] The data collection unit can collect wide-area environmental data in real time. For example, the data collection unit uses generative AI to collect wide-area environmental data in real time. The data collection unit can collect data based on the collection range and the type of data to be collected. The data collection unit can also optimize the collection range based on real-time data generated by the generative AI. Furthermore, the data collection unit can adjust the collection range based on specific environmental conditions using the generative AI. This enables the collection of wide-area environmental data in real time.

[0037] The planning department can analyze past flight data and select the optimal flight path. For example, the planning department can use generative AI to analyze past flight data and select the optimal flight path. The planning department can use generative AI to analyze past flight data and select the most efficient path. The planning department can also use generative AI to select a path with fewer obstacles from past flight data. The planning department can also use generative AI to select a path suitable for weather conditions based on past flight data. In this way, the optimal flight path can be selected by analyzing past flight data.

[0038] The planning department can optimize flight paths based on weather and terrain information. For example, the planning department can use generative AI to optimize paths based on weather and terrain information. The planning department can use generative AI to acquire real-time weather information and plan the optimal path. The planning department can also use generative AI to analyze terrain data and plan paths that take altitude and obstacles into consideration. The planning department can also use generative AI to plan paths that avoid bad weather based on weather forecast data. In this way, flight paths can be optimized based on weather and terrain information.

[0039] The planning unit can adjust the flight path based on the drone's battery level when planning the flight path. For example, the planning unit can use a generation AI to monitor the drone's battery level in real time and plan the optimal path. The planning unit can also have the generation AI plan a path that includes charging points along the way, depending on the battery level. The planning unit can also have the generation AI plan the shortest path, taking the battery level into consideration. This allows the flight path to be adjusted based on the drone's battery level.

[0040] The planning unit can set routes to avoid collisions with other drones when planning flight paths. For example, the planning unit can use generative AI to set routes to avoid collisions with other drones. The planning unit can use generative AI to acquire the location information of other drones in real time and plan routes to avoid collisions. The planning unit can also use generative AI to predict the flight paths of other drones and plan the optimal route. The planning unit can also use generative AI to communicate with other drones and plan flight paths in cooperation. This makes it possible to set routes that avoid collisions with other drones.

[0041] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can use generative AI to analyze past data collection history and select the optimal collection method. The data collection unit uses generative AI to analyze past data collection history and select the most efficient collection method. The data collection unit can also use generative AI to select a collection method suitable for specific environmental conditions based on past data collection history. Furthermore, the data collection unit can use generative AI to optimize the collection range based on past data collection history. This allows for the selection of the optimal collection method by analyzing past data collection history.

[0042] The data collection unit can adjust the collection range based on specific environmental conditions during data collection. For example, the data collection unit can use generative AI to adjust the collection range based on specific environmental conditions. The data collection unit uses generative AI to acquire real-time environmental data and plan the optimal collection range. The data collection unit can also use generative AI to adjust the collection range based on specific environmental conditions (e.g., temperature, humidity). Furthermore, the data collection unit can use generative AI to optimize the collection range based on environmental prediction data. This allows the data collection range to be adjusted based on specific environmental conditions.

[0043] The data collection unit can optimize the collection range based on the drone's location information during data collection. For example, the collection unit can use generative AI to optimize the collection range based on the drone's location information. The collection unit uses generative AI to acquire the drone's location information in real time and plan the optimal collection range. The collection unit can also use generative AI to optimize the collection range based on the drone's flight path. Furthermore, the collection unit can use generative AI to dynamically adjust the collection range, taking the drone's location information into consideration. This allows for the optimization of the collection range based on the drone's location information.

[0044] The data collection unit can improve collection accuracy by referencing other sensor information during data collection. For example, the data collection unit can improve collection accuracy by referencing other sensor information using a generating AI. The data collection unit can improve collection accuracy by having the generating AI reference other sensor information (e.g., temperature sensor, humidity sensor). The data collection unit can also improve the accuracy of collected data by having the generating AI integrate information from multiple sensors. Furthermore, the data collection unit can optimize the collection range by having the generating AI acquire sensor information in real time. This allows for improved collection accuracy by referencing other sensor information.

[0045] The analysis unit can optimize the analysis algorithm by referring to past analysis data. For example, the analysis unit can optimize the analysis algorithm by referring to past analysis data using a generative AI. The generative AI analyzes past analysis data and selects the optimal algorithm. The generative AI can also select an algorithm suitable for specific environmental conditions from past analysis data. The generative AI can also select an algorithm that improves analysis accuracy based on past analysis data. This allows the analysis algorithm to be optimized by referring to past analysis data.

[0046] The analysis unit can change the analysis method based on specific environmental parameters during data analysis. For example, the analysis unit can use a generative AI to change the analysis method based on specific environmental parameters. The generative AI acquires real-time environmental data and selects the optimal analysis method. The analysis unit can also have the generative AI change the analysis method based on specific environmental parameters (e.g., temperature, humidity). Furthermore, the generative AI can optimize the analysis method based on environmental prediction data. This allows the analysis method to be changed based on specific environmental parameters.

[0047] The analysis unit can improve the accuracy of its analysis by referencing other data sources during data analysis. For example, the analysis unit can improve accuracy by referencing other data sources using a generative AI. The analysis unit can improve accuracy by having the generative AI reference other data sources (e.g., weather data, topographic data). The analysis unit can also improve the accuracy of the analyzed data by having the generative AI integrate multiple data sources. Furthermore, the analysis unit can optimize the analysis method by having the generative AI acquire data sources in real time. This allows for improved analysis accuracy by referencing other data sources.

[0048] The analysis unit can dynamically adjust the threshold for anomaly detection during data analysis. For example, the analysis unit can dynamically adjust the anomaly detection threshold using a generative AI. The analysis unit uses the generative AI to acquire real-time data and dynamically adjust the anomaly detection threshold. The analysis unit can also use the generative AI to analyze past anomaly data and set the optimal threshold. Furthermore, the analysis unit can use the generative AI to dynamically change the anomaly detection threshold based on environmental conditions. This allows for dynamic adjustment of the anomaly detection threshold.

[0049] The countermeasure proposal department can analyze past countermeasure history and select the optimal countermeasure. For example, the countermeasure proposal department can use generative AI to analyze past countermeasure history and select the optimal countermeasure. The countermeasure proposal department's generative AI analyzes past countermeasure history and selects the most effective countermeasure. The countermeasure proposal department's generative AI can also select countermeasures suitable for specific environmental conditions from past countermeasure history. The countermeasure proposal department's generative AI can also select countermeasures that can be quickly implemented based on past countermeasure history. In this way, the optimal countermeasure can be selected by analyzing past countermeasure history.

[0050] The proposed countermeasures unit can modify its proposed solutions based on specific environmental conditions. For example, it can use a generative AI to modify the proposed solutions based on specific environmental conditions. The generative AI acquires real-time environmental data and proposes optimal countermeasures. The generative AI can also modify the proposed solutions based on specific environmental conditions (e.g., temperature, humidity). Furthermore, the generative AI can optimize the proposed solutions based on environmental prediction data. This allows the proposed solutions to be modified based on specific environmental conditions.

[0051] The countermeasure proposal unit can improve the accuracy of its proposals by referencing other data sources when proposing countermeasures. For example, the countermeasure proposal unit can improve the accuracy of its proposals by referencing other data sources using a generative AI. The countermeasure proposal unit can improve the accuracy of its proposals by having the generative AI reference other data sources (e.g., weather data, topographic data). The countermeasure proposal unit can also improve the accuracy of its proposals by having the generative AI integrate multiple data sources. The countermeasure proposal unit can also optimize its proposals by having the generative AI acquire data sources in real time. This allows it to improve the accuracy of its proposals by referencing other data sources.

[0052] The countermeasure proposal unit can apply different proposal algorithms depending on the type of anomaly when proposing countermeasures. For example, the countermeasure proposal unit can use a generation AI to apply different proposal algorithms depending on the type of anomaly. The countermeasure proposal unit has the generation AI select the optimal proposal algorithm according to the type of anomaly (e.g., temperature anomaly, humidity anomaly). The countermeasure proposal unit can also have the generation AI modify the proposed content based on the type of anomaly. The countermeasure proposal unit can also have the generation AI detect the type of anomaly in real time and apply the optimal proposal algorithm. This allows for the application of different proposal algorithms depending on the type of anomaly.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The planning department can adjust the drone's flight speed when planning its flight path. For example, the planning department can optimize the drone's flight speed using generative AI. The planning department can adjust the speed considering factors such as flight path length, safety, and efficiency. The planning department uses generative AI to plan the optimal speed based on real-time data. The planning department can also use generative AI to analyze past flight data and select the optimal speed. Furthermore, the planning department can use generative AI to optimize the speed considering weather and terrain information. This allows for the optimization of the drone's flight speed.

[0055] The analysis unit can consider the frequency of anomalies when detecting environmental anomalies based on collected data. For example, the analysis unit uses a generative AI to analyze the collected data and detect the frequency of anomalies. If the frequency of anomalies is high, the generative AI immediately proposes countermeasures. If the frequency of anomalies is low, the generative AI performs a detailed analysis to identify the cause of the anomaly. The analysis unit can also evaluate the importance of anomalies according to their frequency. This allows for anomaly detection while considering the frequency of occurrence.

[0056] The analysis unit can consider the scope of the anomaly's impact when proposing countermeasures upon detection. For example, the analysis unit uses a generation AI to evaluate the scope of the anomaly's impact and propose countermeasures. If the scope of the anomaly's impact is wide, the generation AI proposes rapid countermeasures. If the scope of the anomaly's impact is narrow, the generation AI proposes detailed countermeasures. The analysis unit can also determine the priority of countermeasures according to the scope of the anomaly's impact. This allows it to propose countermeasures while considering the scope of the anomaly's impact.

[0057] The planning department can adjust the drone's flight altitude when optimizing the drone's flight path and schedule. For example, the planning department can use generative AI to optimize the drone's flight altitude. The planning department can adjust the altitude considering the length, safety, and efficiency of the flight path. The planning department can use generative AI to plan the optimal altitude based on real-time data. The planning department can also use generative AI to analyze past flight data and select the optimal altitude. Furthermore, the planning department can use generative AI to optimize the altitude considering weather and terrain information. This allows for the optimization of the drone's flight altitude.

[0058] The data collection unit can adjust the data collection frequency when collecting wide-area environmental data in real time. For example, the data collection unit can optimize the data collection frequency using generative AI. The data collection unit can adjust the collection frequency based on the collection range and the type of data being collected. The data collection unit can optimize the collection frequency based on real-time data using generative AI. The data collection unit can also optimize the collection frequency based on specific environmental conditions using generative AI. This allows for the optimization of the data collection frequency.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The planning department plans the drone's flight path. The planning department uses generative AI to optimize the flight path, considering its length, safety, and efficiency. The planning department can select the optimal path based on real-time data, historical flight data, weather, and terrain information. Step 2: The collection unit autonomously flies the drone based on the flight path planned by the planning unit. The collection unit autonomously controls the drone using generative AI, monitors the flight path in real time, and adjusts the path as needed. The collection unit can also monitor the drone's battery level and plan the optimal path, as well as a path to avoid collisions with other drones. Step 3: The analysis unit analyzes the data collected by the collection unit in real time. The analysis unit uses generative AI to analyze the collected data and detect anomalies in the environment. The analysis unit can also propose countermeasures when an anomaly is detected and optimize the analysis algorithm by referring to past analysis data.

[0061] (Example of form 2) An environmental data collection system according to an embodiment of the present invention is a system that autonomously controls multiple drones using generative AI to collect and analyze wide-area environmental data in real time. The environmental data collection system optimizes the flight paths and schedules of the drones using generative AI to achieve efficient data collection. Based on the collected data, the environmental data collection system immediately detects environmental anomalies and proposes countermeasures. For example, in the environmental data collection system, the generative AI plans the flight paths of the drones, and the drones fly autonomously to collect environmental data. The collected data is analyzed in real time, and if an anomaly is detected, the generative AI immediately proposes countermeasures. This system reduces the time and cost required for collecting environmental data and enables efficient monitoring of wide areas. It also prevents delays in anomaly detection and enables rapid response. As a result, the environmental data collection system reduces the time and cost required for collecting environmental data and enables efficient monitoring of wide areas.

[0062] The environmental data collection system according to this embodiment comprises a planning unit, a collection unit, and an analysis unit. The planning unit plans the flight path of the drone. The planning unit optimizes the flight path of the drone using, for example, a generative AI. The planning unit can plan a path considering the length, safety, and efficiency of the flight path. The planning unit can, for example, have the generative AI plan the optimal path based on real-time data. The planning unit can also have the generative AI analyze past flight data and select the optimal path. The planning unit can also have the generative AI optimize the path considering weather and terrain information. The collection unit autonomously flies the drone based on the flight path planned by the planning unit. The collection unit autonomously controls the drone using, for example, a generative AI. The collection unit monitors the drone's flight path in real time and can adjust the path as needed. The collection unit can also have the generative AI monitor the drone's battery level and plan the optimal path. The collection unit can also have the generative AI plan a path to avoid collisions with other drones. The analysis unit analyzes the data collected by the collection unit in real time. The analysis unit analyzes the collected data, for example, using a generative AI. Based on the collected data, the analysis unit can detect environmental anomalies. The analysis unit can also propose countermeasures when the generative AI detects an anomaly. The analysis unit can also optimize the analysis algorithm by allowing the generative AI to refer to past analysis data. As a result, the environmental data collection system according to this embodiment can plan the drone's flight path, fly it autonomously, and analyze the collected data in real time.

[0063] The planning department plans the drone's flight path. For example, the planning department optimizes the drone's flight path using generative AI. Specifically, when planning the drone's flight path, the generative AI plans the optimal path based on real-time data. The generative AI can also analyze past flight data and select the optimal path. For example, past flight data includes the drone's flight path, flight time, battery consumption, and the location of obstacles. Based on this data, the generative AI plans the optimal flight path. Furthermore, the generative AI can also optimize the path by considering weather and terrain information. For example, weather information includes wind speed, wind direction, rainfall, and temperature, while terrain information includes the location of mountains, valleys, and buildings. Based on this information, the generative AI plans a path that allows the drone to fly safely and efficiently. The planning department transmits the flight path planned by the generative AI to the drone and instructs the drone to fly according to that path. This allows the planning department to plan the drone's flight path efficiently and safely.

[0064] The collection unit autonomously flies the drone based on the flight path planned by the planning unit. The collection unit autonomously controls the drone, for example, using generative AI. Specifically, the generative AI monitors the drone's flight path in real time and adjusts the path as needed. For example, if the drone encounters an unexpected obstacle during flight, the generative AI immediately changes the drone's flight path to avoid the obstacle. The generative AI can also monitor the drone's battery level and plan the optimal path before the battery runs out. For example, if the drone is flying for a long period, the generative AI considers battery consumption and plans a path that passes through points where the battery can be replaced or charged along the way. Furthermore, the generative AI can also plan a path to avoid collisions with other drones. For example, if multiple drones are flying in the same airspace, the generative AI adjusts the flight path of each drone to minimize the risk of collision. The collection unit autonomously flies the drone according to the flight path planned by the generative AI and collects environmental data. This allows the collection unit to fly the drone efficiently and safely and reliably collect the necessary data.

[0065] The analysis unit analyzes the data collected by the collection unit in real time. The analysis unit analyzes the collected data using, for example, a generative AI. Specifically, the generative AI detects environmental anomalies based on the collected data. For example, it analyzes atmospheric gas concentration data and temperature data collected by a drone, and if abnormal values ​​are detected, the generative AI identifies the anomaly. Furthermore, the generative AI can also propose countermeasures when an anomaly is detected. For example, if an abnormal gas concentration is detected, the generative AI identifies the cause and proposes appropriate countermeasures. The generative AI can also optimize the analysis algorithm by referring to past analysis data. For example, it adjusts parameters to improve the accuracy of anomaly detection based on past data. Based on the results of the generative AI's analysis, the analysis unit can quickly detect environmental anomalies and take appropriate countermeasures. As a result, the analysis unit can efficiently and accurately analyze the collected data and detect environmental anomalies at an early stage.

[0066] The analysis unit can detect environmental anomalies based on the collected data. For example, the analysis unit uses a generative AI to analyze the collected data and detect anomalies. The analysis unit can detect environmental anomalies such as temperature anomalies, humidity anomalies, and gas leaks. The analysis unit can also use a generative AI to detect anomalies based on real-time data. Furthermore, the analysis unit can use a generative AI to detect anomalies by referring to past anomaly data. This allows for the detection of environmental anomalies based on collected data.

[0067] The analysis unit includes a countermeasure proposal unit that suggests countermeasures when an anomaly is detected. For example, the analysis unit uses a generation AI to suggest countermeasures when an anomaly is detected. The analysis unit can suggest countermeasures based on the algorithm and priority of the suggestions. The analysis unit can also have the generation AI suggest the most appropriate countermeasure depending on the type of anomaly. Furthermore, the analysis unit can have the generation AI suggest the most appropriate countermeasure by referring to past countermeasure history. This allows the system to suggest countermeasures when an anomaly is detected.

[0068] The planning department can optimize the drone's flight paths and schedules. For example, the planning department can use generative AI to optimize the drone's flight paths and schedules. The planning department can optimize routes and schedules based on the optimization objectives and the algorithms used. The planning department can also have the generative AI optimize routes and schedules based on real-time data. Furthermore, the planning department can have the generative AI optimize routes and schedules by referencing past flight data. This allows for the optimization of the drone's flight paths and schedules.

[0069] The data collection unit can collect wide-area environmental data in real time. For example, the data collection unit uses generative AI to collect wide-area environmental data in real time. The data collection unit can collect data based on the collection range and the type of data to be collected. The data collection unit can also optimize the collection range based on real-time data generated by the generative AI. Furthermore, the data collection unit can adjust the collection range based on specific environmental conditions using the generative AI. This enables the collection of wide-area environmental data in real time.

[0070] The planning unit can estimate the user's emotions and adjust the flight path plan based on the estimated emotions. For example, the planning unit can use generative AI to estimate the user's emotions and adjust the flight path plan. If the user is stressed, the planning unit can use generative AI to plan a simple route to reduce the burden of operation. If the user is relaxed, the planning unit can use generative AI to plan a detailed route to achieve a flight tailored to the user's preferences. If the user is in a hurry, the planning unit can use generative AI to plan the shortest route and collect data quickly. This allows the flight path plan to be adjusted based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0071] The planning department can analyze past flight data and select the optimal flight path. For example, the planning department can use generative AI to analyze past flight data and select the optimal flight path. The planning department can use generative AI to analyze past flight data and select the most efficient path. The planning department can also use generative AI to select a path with fewer obstacles from past flight data. The planning department can also use generative AI to select a path suitable for weather conditions based on past flight data. In this way, the optimal flight path can be selected by analyzing past flight data.

[0072] The planning department can optimize flight paths based on weather and terrain information. For example, the planning department can use generative AI to optimize paths based on weather and terrain information. The planning department can use generative AI to acquire real-time weather information and plan the optimal path. The planning department can also use generative AI to analyze terrain data and plan paths that take altitude and obstacles into consideration. The planning department can also use generative AI to plan paths that avoid bad weather based on weather forecast data. In this way, flight paths can be optimized based on weather and terrain information.

[0073] The planning unit can estimate the user's emotions and determine the priority of the flight path based on the estimated emotions. For example, the planning unit can use generative AI to estimate the user's emotions and determine the priority of the flight path. If the user is stressed, the planning unit will have the generative AI prioritize important routes. If the user is relaxed, the planning unit will have the generative AI prioritize detailed routes. If the user is in a hurry, the planning unit will have the generative AI prioritize the shortest route. This allows the planning unit to determine the priority of the flight path based on 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.

[0074] The planning unit can adjust the flight path based on the drone's battery level when planning the flight path. For example, the planning unit can use a generation AI to monitor the drone's battery level in real time and plan the optimal path. The planning unit can also have the generation AI plan a path that includes charging points along the way, depending on the battery level. The planning unit can also have the generation AI plan the shortest path, taking the battery level into consideration. This allows the flight path to be adjusted based on the drone's battery level.

[0075] The planning unit can set routes to avoid collisions with other drones when planning flight paths. For example, the planning unit can use generative AI to set routes to avoid collisions with other drones. The planning unit can use generative AI to acquire the location information of other drones in real time and plan routes to avoid collisions. The planning unit can also use generative AI to predict the flight paths of other drones and plan the optimal route. The planning unit can also use generative AI to communicate with other drones and plan flight paths in cooperation. This makes it possible to set routes that avoid collisions with other drones.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit uses generative AI to estimate the user's emotions and adjust the timing of data collection. If the user is stressed, the generative AI reduces the frequency of data collection to alleviate the burden. If the user is relaxed, the generative AI increases the frequency of data collection to collect more detailed data. If the user is in a hurry, the generative AI optimizes the timing of data collection to collect data quickly. This allows the timing of data collection to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can use generative AI to analyze past data collection history and select the optimal collection method. The data collection unit uses generative AI to analyze past data collection history and select the most efficient collection method. The data collection unit can also use generative AI to select a collection method suitable for specific environmental conditions based on past data collection history. Furthermore, the data collection unit can use generative AI to optimize the collection range based on past data collection history. This allows for the selection of the optimal collection method by analyzing past data collection history.

[0078] The data collection unit can adjust the collection range based on specific environmental conditions during data collection. For example, the data collection unit can use generative AI to adjust the collection range based on specific environmental conditions. The data collection unit uses generative AI to acquire real-time environmental data and plan the optimal collection range. The data collection unit can also use generative AI to adjust the collection range based on specific environmental conditions (e.g., temperature, humidity). Furthermore, the data collection unit can use generative AI to optimize the collection range based on environmental prediction data. This allows the data collection range to be adjusted based on specific environmental conditions.

[0079] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, the data collection unit might use generative AI to estimate the user's emotions and prioritize the data to collect. If the user is stressed, the generative AI will prioritize collecting important data. If the user is relaxed, the generative AI will prioritize collecting detailed data. If the user is in a hurry, the generative AI will prioritize data that can be collected quickly. This allows the data collection unit to prioritize data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The data collection unit can optimize the collection range based on the drone's location information during data collection. For example, the collection unit can use generative AI to optimize the collection range based on the drone's location information. The collection unit uses generative AI to acquire the drone's location information in real time and plan the optimal collection range. The collection unit can also use generative AI to optimize the collection range based on the drone's flight path. Furthermore, the collection unit can use generative AI to dynamically adjust the collection range, taking the drone's location information into consideration. This allows for the optimization of the collection range based on the drone's location information.

[0081] The data collection unit can improve collection accuracy by referencing other sensor information during data collection. For example, the data collection unit can improve collection accuracy by referencing other sensor information using a generating AI. The data collection unit can improve collection accuracy by having the generating AI reference other sensor information (e.g., temperature sensor, humidity sensor). The data collection unit can also improve the accuracy of collected data by having the generating AI integrate information from multiple sensors. Furthermore, the data collection unit can optimize the collection range by having the generating AI acquire sensor information in real time. This allows for improved collection accuracy by referencing other sensor information.

[0082] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, the analysis unit can use generative AI to estimate the user's emotions and adjust the data analysis method. If the user is stressed, the analysis unit will have the generative AI select a simple analysis method to reduce the burden. If the user is relaxed, the analysis unit will have the generative AI select a detailed analysis method to perform analysis tailored to the user's preferences. If the user is in a hurry, the analysis unit will have the generative AI select a rapid analysis method to provide results quickly. This allows the data analysis method to be adjusted based on 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.

[0083] The analysis unit can optimize the analysis algorithm by referring to past analysis data. For example, the analysis unit can optimize the analysis algorithm by referring to past analysis data using a generative AI. The generative AI analyzes past analysis data and selects the optimal algorithm. The generative AI can also select an algorithm suitable for specific environmental conditions from past analysis data. The generative AI can also select an algorithm that improves analysis accuracy based on past analysis data. This allows the analysis algorithm to be optimized by referring to past analysis data.

[0084] The analysis unit can change the analysis method based on specific environmental parameters during data analysis. For example, the analysis unit can use a generative AI to change the analysis method based on specific environmental parameters. The generative AI acquires real-time environmental data and selects the optimal analysis method. The analysis unit can also have the generative AI change the analysis method based on specific environmental parameters (e.g., temperature, humidity). Furthermore, the generative AI can optimize the analysis method based on environmental prediction data. This allows the analysis method to be changed based on specific environmental parameters.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit can use a generative AI to estimate the user's emotions and adjust the display method of the analysis results. If the user is stressed, the analysis unit will have the generative AI select a simple display method to reduce the burden. If the user is relaxed, the analysis unit will have the generative AI select a detailed display method to match the user's preferences. If the user is in a hurry, the analysis unit will have the generative AI select a fast display method to provide results quickly. This allows the display method of the analysis results to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The analysis unit can improve the accuracy of its analysis by referencing other data sources during data analysis. For example, the analysis unit can improve accuracy by referencing other data sources using a generative AI. The analysis unit can improve accuracy by having the generative AI reference other data sources (e.g., weather data, topographic data). The analysis unit can also improve the accuracy of the analyzed data by having the generative AI integrate multiple data sources. Furthermore, the analysis unit can optimize the analysis method by having the generative AI acquire data sources in real time. This allows for improved analysis accuracy by referencing other data sources.

[0087] The analysis unit can dynamically adjust the threshold for anomaly detection during data analysis. For example, the analysis unit can dynamically adjust the anomaly detection threshold using a generative AI. The analysis unit uses the generative AI to acquire real-time data and dynamically adjust the anomaly detection threshold. The analysis unit can also use the generative AI to analyze past anomaly data and set the optimal threshold. Furthermore, the analysis unit can use the generative AI to dynamically change the anomaly detection threshold based on environmental conditions. This allows for dynamic adjustment of the anomaly detection threshold.

[0088] The solution suggestion unit can estimate the user's emotions and adjust its solution suggestion method based on the estimated emotions. For example, the solution suggestion unit can use generative AI to estimate the user's emotions and adjust its solution suggestion method. If the user is stressed, the generative AI will suggest a simple and quick solution. If the user is relaxed, the generative AI will suggest a detailed solution tailored to the user's preferences. If the user is in a hurry, the generative AI will suggest a solution that can be quickly implemented. This allows the solution suggestion method to be adjusted based on 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.

[0089] The countermeasure proposal department can analyze past countermeasure history and select the optimal countermeasure. For example, the countermeasure proposal department can use generative AI to analyze past countermeasure history and select the optimal countermeasure. The countermeasure proposal department's generative AI analyzes past countermeasure history and selects the most effective countermeasure. The countermeasure proposal department's generative AI can also select countermeasures suitable for specific environmental conditions from past countermeasure history. The countermeasure proposal department's generative AI can also select countermeasures that can be quickly implemented based on past countermeasure history. In this way, the optimal countermeasure can be selected by analyzing past countermeasure history.

[0090] The proposed countermeasures unit can modify its proposed solutions based on specific environmental conditions. For example, it can use a generative AI to modify the proposed solutions based on specific environmental conditions. The generative AI acquires real-time environmental data and proposes optimal countermeasures. The generative AI can also modify the proposed solutions based on specific environmental conditions (e.g., temperature, humidity). Furthermore, the generative AI can optimize the proposed solutions based on environmental prediction data. This allows the proposed solutions to be modified based on specific environmental conditions.

[0091] The solution suggestion unit can estimate the user's emotions and determine the priority of solution suggestions based on the estimated emotions. For example, the solution suggestion unit uses generative AI to estimate the user's emotions and determine the priority of solution suggestions. If the user is stressed, the generative AI will prioritize suggesting important solutions. If the user is relaxed, the generative AI will prioritize suggesting detailed solutions. If the user is in a hurry, the generative AI will prioritize suggesting solutions that can be implemented quickly. This allows the solution suggestion unit to determine the priority of solution suggestions based on 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.

[0092] The countermeasure proposal unit can improve the accuracy of its proposals by referencing other data sources when proposing countermeasures. For example, the countermeasure proposal unit can improve the accuracy of its proposals by referencing other data sources using a generative AI. The countermeasure proposal unit can improve the accuracy of its proposals by having the generative AI reference other data sources (e.g., weather data, topographic data). The countermeasure proposal unit can also improve the accuracy of its proposals by having the generative AI integrate multiple data sources. The countermeasure proposal unit can also optimize its proposals by having the generative AI acquire data sources in real time. This allows it to improve the accuracy of its proposals by referencing other data sources.

[0093] The countermeasure proposal unit can apply different proposal algorithms depending on the type of anomaly when proposing countermeasures. For example, the countermeasure proposal unit can use a generation AI to apply different proposal algorithms depending on the type of anomaly. The countermeasure proposal unit has the generation AI select the optimal proposal algorithm according to the type of anomaly (e.g., temperature anomaly, humidity anomaly). The countermeasure proposal unit can also have the generation AI modify the proposed content based on the type of anomaly. The countermeasure proposal unit can also have the generation AI detect the type of anomaly in real time and apply the optimal proposal algorithm. This allows for the application of different proposal algorithms depending on the type of anomaly.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The planning department can adjust the drone's flight speed when planning its flight path. For example, the planning department can optimize the drone's flight speed using generative AI. The planning department can adjust the speed considering factors such as flight path length, safety, and efficiency. The planning department uses generative AI to plan the optimal speed based on real-time data. The planning department can also use generative AI to analyze past flight data and select the optimal speed. Furthermore, the planning department can use generative AI to optimize the speed considering weather and terrain information. This allows for the optimization of the drone's flight speed.

[0096] The analysis unit can consider the frequency of anomalies when detecting environmental anomalies based on collected data. For example, the analysis unit uses a generative AI to analyze the collected data and detect the frequency of anomalies. If the frequency of anomalies is high, the generative AI immediately proposes countermeasures. If the frequency of anomalies is low, the generative AI performs a detailed analysis to identify the cause of the anomaly. The analysis unit can also evaluate the importance of anomalies according to their frequency. This allows for anomaly detection while considering the frequency of occurrence.

[0097] The analysis unit can consider the scope of the anomaly's impact when proposing countermeasures upon detection. For example, the analysis unit uses a generation AI to evaluate the scope of the anomaly's impact and propose countermeasures. If the scope of the anomaly's impact is wide, the generation AI proposes rapid countermeasures. If the scope of the anomaly's impact is narrow, the generation AI proposes detailed countermeasures. The analysis unit can also determine the priority of countermeasures according to the scope of the anomaly's impact. This allows it to propose countermeasures while considering the scope of the anomaly's impact.

[0098] The planning department can adjust the drone's flight altitude when optimizing the drone's flight path and schedule. For example, the planning department can use generative AI to optimize the drone's flight altitude. The planning department can adjust the altitude considering the length, safety, and efficiency of the flight path. The planning department can use generative AI to plan the optimal altitude based on real-time data. The planning department can also use generative AI to analyze past flight data and select the optimal altitude. Furthermore, the planning department can use generative AI to optimize the altitude considering weather and terrain information. This allows for the optimization of the drone's flight altitude.

[0099] The data collection unit can adjust the data collection frequency when collecting wide-area environmental data in real time. For example, the data collection unit can optimize the data collection frequency using generative AI. The data collection unit can adjust the collection frequency based on the collection range and the type of data being collected. The data collection unit can optimize the collection frequency based on real-time data using generative AI. The data collection unit can also optimize the collection frequency based on specific environmental conditions using generative AI. This allows for the optimization of the data collection frequency.

[0100] The planning unit can estimate the user's emotions and adjust the flight path plan based on those emotions. For example, the planning unit uses generative AI to estimate the user's emotions and adjust the flight path plan. If the user is stressed, the planning unit uses generative AI to plan a simple route to reduce the burden of operation. If the user is relaxed, the planning unit uses generative AI to plan a detailed route to achieve a flight tailored to the user's preferences. If the user is in a hurry, the planning unit uses generative AI to plan the shortest route and collect data quickly. This allows the flight path plan to be adjusted based on the user's emotions.

[0101] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, the data collection unit uses generative AI to estimate the user's emotions and adjust the timing of data collection. If the user is stressed, the generative AI reduces the frequency of data collection to alleviate the burden. If the user is relaxed, the generative AI increases the frequency of data collection to collect more detailed data. If the user is in a hurry, the generative AI optimizes the timing of data collection to collect data quickly. This allows the timing of data collection to be adjusted based on the user's emotions.

[0102] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, the analysis unit can use generative AI to estimate the user's emotions and adjust the data analysis method. If the user is stressed, the analysis unit will use generative AI to select a simple analysis method to reduce the burden. If the user is relaxed, the analysis unit will use generative AI to select a detailed analysis method and perform analysis tailored to the user's preferences. If the user is in a hurry, the analysis unit will use generative AI to select a rapid analysis method and provide results quickly. This allows the data analysis method to be adjusted based on the user's emotions.

[0103] The solution suggestion unit can estimate the user's emotions and adjust its solution suggestion method based on those emotions. For example, the solution suggestion unit can use generative AI to estimate the user's emotions and adjust its solution suggestion method. If the user is stressed, the generative AI will suggest a simple and quick solution. If the user is relaxed, the generative AI will suggest a detailed solution tailored to the user's preferences. If the user is in a hurry, the generative AI will suggest a solution that can be implemented quickly. This allows the solution suggestion method to be adjusted based on the user's emotions.

[0104] The solution suggestion unit can estimate the user's emotions and determine the priority of solution suggestions based on those emotions. For example, the solution suggestion unit uses generative AI to estimate the user's emotions and determine the priority of solution suggestions. If the user is feeling stressed, the generative AI will prioritize suggesting important solutions. If the user is relaxed, the generative AI will prioritize suggesting detailed solutions. If the user is in a hurry, the generative AI will prioritize suggesting solutions that can be implemented quickly. This allows the solution suggestion unit to determine the priority of solution suggestions based on the user's emotions.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The planning department plans the drone's flight path. The planning department uses generative AI to optimize the flight path, considering its length, safety, and efficiency. The planning department can select the optimal path based on real-time data, historical flight data, weather, and terrain information. Step 2: The collection unit autonomously flies the drone based on the flight path planned by the planning unit. The collection unit autonomously controls the drone using generative AI, monitors the flight path in real time, and adjusts the path as needed. The collection unit can also monitor the drone's battery level and plan the optimal path, as well as a path to avoid collisions with other drones. Step 3: The analysis unit analyzes the data collected by the collection unit in real time. The analysis unit uses generative AI to analyze the collected data and detect anomalies in the environment. The analysis unit can also propose countermeasures when an anomaly is detected and optimize the analysis algorithm by referring to past analysis data.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the planning unit, data collection unit, and analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the planning unit is implemented by the specific processing unit 290 of the data processing unit 12, which optimizes the drone's flight path using generated AI. The data collection unit is implemented by the control unit 46A of the smart device 14, which autonomously flies the drone based on the planned flight path. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the planning unit, data collection unit, and analysis unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the planning unit is implemented by the specific processing unit 290 of the data processing unit 12, which optimizes the drone's flight path using generated AI. The data collection unit is implemented by the control unit 46A of the smart glasses 214, which autonomously flies the drone based on the planned flight path. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the planning unit, data collection unit, and analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the planning unit is implemented by the specific processing unit 290 of the data processing unit 12, which optimizes the drone's flight path using generated AI. The data collection unit is implemented by the control unit 46A of the headset terminal 314, which autonomously flies the drone based on the planned flight path. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the planning unit, data collection unit, and analysis unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the planning unit is implemented by the specific processing unit 290 of the data processing unit 12, which optimizes the drone's flight path using generated AI. The data collection unit is implemented, for example, by the control unit 46A of the robot 414, which autonomously flies the drone based on the planned flight path. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) The planning department, which plans the drone's flight path, A collection unit that autonomously flies the drone based on the flight path planned by the aforementioned planning unit, An analysis unit analyzes the data collected by the aforementioned collection unit in real time, Equipped with A system characterized by the following features. (Note 2) The aforementioned analysis unit, The collected data is used to detect anomalies in the environment. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, It is equipped with a countermeasure proposal unit that suggests countermeasures when an anomaly is detected. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned planning department, Optimize drone flight paths and schedules. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect wide-area environmental data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned planning department, It estimates the user's emotions and adjusts the flight path plan based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned planning department, Analyze past flight data to select the optimal flight path. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned planning department, When planning flight paths, optimize the route based on weather and terrain information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned planning department, It estimates the user's emotions and determines the priority of the flight path based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned planning department, When planning the flight path, adjust the path based on the drone's battery level. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned planning department, When planning flight paths, set routes that avoid collisions with other drones. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, the collection range is adjusted based on specific environmental conditions. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The aforementioned collection unit is During data collection, the collection range is optimized based on the drone's location information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is During data collection, refer to other sensor information to improve collection accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, Optimize the analysis algorithm by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During data analysis, the analysis method is changed based on specific environmental parameters. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, When analyzing data, referencing other data sources improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, Dynamically adjust the threshold for anomaly detection during data analysis. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned countermeasure proposal department, It estimates the user's emotions and adjusts the method of suggesting countermeasures based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 25) The aforementioned countermeasure proposal department, Analyze past countermeasures and select the optimal solution. The system described in Appendix 3, characterized by the features described herein. (Note 26) The aforementioned countermeasure proposal department, When proposing countermeasures, we will modify the proposed content based on specific environmental conditions. The system described in Appendix 3, characterized by the features described herein. (Note 27) The aforementioned countermeasure proposal department, The system estimates user sentiment and prioritizes suggested actions based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 28) The aforementioned countermeasure proposal department, When proposing countermeasures, refer to other data sources to improve the accuracy of the proposals. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned countermeasure proposal department, When proposing countermeasures, different proposal algorithms are applied depending on the type of anomaly. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The planning department, which plans the drone's flight path, A collection unit that autonomously flies the drone based on the flight path planned by the aforementioned planning unit, An analysis unit analyzes the data collected by the aforementioned collection unit in real time, Equipped with A system characterized by the following features.

2. The aforementioned analysis unit, The collected data is used to detect anomalies in the environment. The system according to feature 1.

3. The aforementioned analysis unit, It is equipped with a countermeasure proposal unit that suggests countermeasures when an anomaly is detected. The system according to feature 1.

4. The aforementioned planning department, Optimize drone flight paths and schedules. The system according to feature 1.

5. The aforementioned collection unit is Collect wide-area environmental data in real time. The system according to feature 1.

6. The aforementioned planning department, It estimates the user's emotions and adjusts the flight path plan based on the estimated emotions. The system according to feature 1.

7. The aforementioned planning department, Analyze past flight data to select the optimal flight path. The system according to feature 1.

8. The aforementioned planning department, When planning flight paths, optimize the route based on weather and terrain information. The system according to feature 1.

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