system

Abstract

The system according to this embodiment aims to automate the flight route planning and flight management of flying objects, thereby achieving safe and efficient flight. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a management unit, and a control unit. The collection unit collects geographic data and obstacle data. The analysis unit analyzes the data collected by the collection unit to plan the flight route of the aircraft. The management unit performs flight management based on the aircraft's battery life and weather conditions. The control unit performs automatic landing or emergency stop if an abnormality is detected.

Description

Technical Field

[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, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the flight route planning and flight management of flying objects are not fully automated, and there is room for improvement in safety and efficiency.

[0005] The system according to the embodiment aims to automate the flight route planning and flight management of flying objects and realize safe and efficient flight.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a management unit, and a control unit. The data collection unit collects geographic data and obstacle data. The analysis unit analyzes the data collected by the data collection unit to plan the flight route of the aircraft. The management unit performs flight management based on the aircraft's battery life and weather conditions. The control unit performs automatic landing or emergency stop if an abnormality is detected. [Effects of the Invention]

[0007] The system according to this embodiment can automate the flight route planning and flight management of flying objects, enabling safe and efficient flight. [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 multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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) The AI ​​assistant for the automated flight support system of a drone according to an embodiment of the present invention is a system that plans a safe flight route based on geographic data and obstacle data, performs flight management considering battery life and weather conditions, and has automatic landing and emergency stop functions. The AI ​​assistant for the automated flight support system of a drone plans a safe flight route based on geographic data and obstacle data, performs flight management considering battery life and weather conditions, and has automatic landing and emergency stop functions. For example, the AI ​​assistant for the automated flight support system of a drone includes a collection unit that collects geographic data and obstacle data. This collection unit collects geographic data and obstacle data using GPS data and sensors. For example, it collects information on the terrain of the area in which the drone will fly, the location of buildings, and obstacles such as trees. Next, it includes an analysis unit that analyzes the collected data. This analysis unit analyzes the collected data using AI and plans an efficient flight route. For example, it plans a route for the drone to reach its destination in the shortest distance, or a route to avoid obstacles. It is also possible to plan a flight route considering the flight routes of other flying objects. Furthermore, it includes a management unit that performs flight management. This control unit manages flight based on battery life and weather conditions. For example, it can monitor the battery level and change the flight route as needed. It also collects weather data using a weather database and real-time weather sensors, and analyzes it with AI to perform flight management that responds to weather changes. Finally, it is equipped with a control unit that performs automatic landing or emergency stop if an anomaly is detected. This control unit detects anomalies such as low battery level or communication loss and performs automatic landing or emergency stop. For example, it can safely land the drone if the battery level falls below a certain level or if communication is lost. In this way, the drone's automatic flight support system AI assistant supports the safe and efficient flight of drones by planning safe flight routes based on geographic and obstacle data, managing flight considering battery life and weather conditions, and having automatic landing and emergency stop functions.

[0029] The AI ​​assistant for the automated flight support system of a drone according to this embodiment comprises a data collection unit, an analysis unit, a management unit, and a control unit. The data collection unit collects geographic data and obstacle data. The data collection unit collects geographic data and obstacle data using, for example, GPS data and sensors. For example, the data collection unit can collect information on the terrain of the area in which the drone is flying, the location of buildings, and obstacles such as trees. The data collection unit can also analyze the drone's flight history and select the optimal data collection method. For example, the data collection unit can identify the optimal altitude and speed for data collection from past flight history. Furthermore, the data collection unit can estimate the user's emotions and adjust the timing of data collection for geographic data and obstacle data based on the estimated user emotions. For example, if the user is feeling stressed, the data collection unit can delay the timing of data collection and wait until the user is relaxed. The analysis unit analyzes the collected data using AI and plans an efficient flight route. For example, the analysis unit can analyze the collected data using AI and plan a route for the drone to reach its destination in the shortest distance or a route to avoid obstacles. The analysis unit can also plan flight routes by considering the flight paths of other flying objects. For example, the analysis unit can monitor the routes of other flying objects in real time and plan a route that avoids collisions. The management unit performs flight management based on battery life and weather. For example, the management unit can monitor the battery level and change the flight route if the battery level falls below a certain level. The management unit can also collect weather data using a weather database and real-time weather sensors and analyze it with AI. For example, the management unit can obtain historical weather data from a weather database and plan the flight route. The control unit performs automatic landing or emergency stop if an anomaly is detected. For example, the control unit can detect anomalies such as low battery level or communication loss and perform automatic landing or emergency stop. For example, the control unit can perform automatic landing if the battery level falls below a certain level.As a result, the AI ​​assistant for the drone's automated flight support system according to this embodiment can assist in the safe and efficient flight of the drone.

[0030] The data collection unit collects geographic and obstacle data. For example, it uses GPS data and sensors to collect geographic and obstacle data. Specifically, it uses a high-precision GPS module and multiple sensors (e.g., LiDAR, camera, ultrasonic sensor, etc.) mounted on the drone to acquire detailed topographic information of the flight area and location information of obstacles such as buildings and trees. The LiDAR sensor uses laser light to accurately measure the distance to surrounding objects and generate a 3D map. The camera provides visual information and uses image analysis techniques to identify the type and shape of obstacles. The ultrasonic sensor detects obstacles at close range, enabling immediate response to prevent drone collisions. The data collection unit processes the data obtained from these sensors in real time and transmits it to a central database. Furthermore, the data collection unit can analyze the drone's flight history and select the optimal data collection method. For example, it can analyze past flight data to identify that data collection is most efficient at specific altitudes and speeds. This allows the data collection unit to maximize the accuracy and efficiency of data collection. Furthermore, the data collection unit can estimate the user's emotions and adjust the timing of geographic and obstacle data collection based on the estimated emotions. For example, if the user is feeling stressed, the data collection timing can be delayed until the user is relaxed. This reduces the burden on the user and provides a more comfortable operating environment.

[0031] The analysis unit uses AI to analyze collected data and plan efficient flight routes. Specifically, the AI ​​analyzes collected geographic and obstacle data to calculate the optimal route for the drone to reach its destination in the shortest distance. The AI ​​uses machine learning algorithms to learn from past flight and environmental data to predict the optimal flight route. For example, the AI ​​considers terrain topography and obstacle placement to select an energy-efficient route. The analysis unit can also plan flight routes considering the flight paths of other aircraft. For example, the AI ​​monitors the flight paths of other drones and aircraft in real time and calculates routes to avoid collisions. This includes coordinating with air traffic control systems to obtain location information of other aircraft and take appropriate evasive action. Furthermore, the analysis unit can also plan flight routes considering weather data. For example, it analyzes weather conditions such as wind speed, wind direction, and rainfall to select a route that minimizes the impact of these conditions on flight. In this way, the analysis unit can support the safe and efficient flight of drones.

[0032] The control unit manages flights based on battery life and weather conditions. Specifically, it constantly monitors the drone's battery level and changes the flight route if the battery level falls below a certain point. For example, if the battery level is low, it plans a route to the nearest safe landing site to ensure the drone lands safely. The control unit can also collect weather data using a weather database and real-time weather sensors and analyze it with AI. For example, it can retrieve historical weather data from the weather database to plan flight routes. Real-time weather sensors monitor current weather conditions and provide information to respond to sudden weather changes. This allows the control unit to minimize the impact of weather conditions on flights. Furthermore, the control unit provides interfaces for quickly changing flight plans and responding to emergencies. For example, it provides an intuitive interface for users to manually change flight routes or instruct emergency landings. This allows the control unit to manage drone flights safely and efficiently.

[0033] The control unit performs automatic landing or emergency stop if an abnormality is detected. Specifically, the control unit monitors battery level, communication loss, sensor malfunctions, etc., in real time and takes appropriate action when an abnormality is detected. For example, if the battery level falls below a certain level, it can perform an automatic landing. When performing an automatic landing, it selects a safe landing site based on geographic and obstacle data collected in advance, ensuring that the drone lands safely. In addition, if a communication loss occurs, the control unit safely stops the drone according to a pre-configured emergency stop procedure. This includes the option for the drone to continue hovering at its current position or to automatically land at the nearest safe location. Furthermore, the control unit also takes appropriate action if a sensor malfunction is detected. For example, if the LiDAR sensor or camera is not functioning properly, it temporarily suspends the drone's flight and waits until the malfunction is resolved. In this way, the control unit can ensure the safety of the drone and minimize the risks when an abnormality occurs.

[0034] The data collection unit can collect geographic data and obstacle data using GPS data and specific sensors (e.g., LiDAR sensors). For example, the data collection unit can collect location information, speed information, and altitude information using GPS data. The data collection unit can also collect terrain data and obstacle data using LiDAR sensors. For example, the data collection unit can detect the location of trees and buildings with high accuracy using LiDAR sensors. Furthermore, the data collection unit can also collect obstacle data using camera sensors and ultrasonic sensors. For example, the data collection unit can collect visual information using camera sensors and distance information using ultrasonic sensors. This improves the accuracy of data collection for geographic data and obstacle data by using GPS data and sensors. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input GPS data and sensor data into a generating AI, which can then analyze the data.

[0035] The analysis unit can analyze the collected data using AI and plan a flight route that is fuel-efficient and time-efficient. For example, the analysis unit can analyze the collected geographic and obstacle data using AI to plan the optimal flight route. For example, the analysis unit can use AI to plan a route that allows the drone to reach its destination in the shortest distance. The analysis unit can also use AI to plan a route that avoids obstacles. Furthermore, the analysis unit can plan a flight route while considering the flight routes of other flying objects. For example, the analysis unit can monitor the routes of other flying objects in real time and plan a route that avoids collisions. This makes it possible to plan an efficient flight route by using AI. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the collected data into a generative AI, and the generative AI can plan the flight route.

[0036] The control unit monitors the battery level and can change the flight route when the battery level falls below a certain point. For example, the control unit can monitor the battery level in real time and change to the shortest route when the battery level falls below 20%. The control unit can also adjust the flight speed to reduce battery consumption when it is draining rapidly. Furthermore, the control unit can set charging points along the flight depending on the battery level. For example, the control unit can set the nearest charging point when the battery level is low and guide the drone to the charging point. This improves flight safety by changing the flight route according to the battery level. Some or all of the above processes in the control unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the control unit can input battery level data into a generative AI, and the generative AI can change the flight route.

[0037] The management unit can collect weather data using a weather database and real-time weather sensors (e.g., temperature sensors, humidity sensors) and analyze it using AI. For example, the management unit can obtain historical weather data from the weather database and plan flight routes. It can also obtain current weather data from real-time weather sensors and adjust flight routes. Furthermore, the management unit can have the AI ​​analyze the weather data and propose the optimal flight route. For example, the management unit can input data obtained from temperature and humidity sensors into the AI, which can then plan flight routes that respond to changes in weather. This enables flight management that responds to changes in weather by collecting and analyzing weather data. Some or all of the above processes in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input weather data into a generative AI, which can then analyze the weather data.

[0038] The control unit can detect abnormalities such as low battery level or communication loss and perform an automatic landing or emergency stop. For example, the control unit can perform an automatic landing when the battery level falls below 20%. The control unit can also perform an emergency stop if communication is lost for 5 seconds or more. Furthermore, if the battery level drops, the control unit can select the nearest safe landing site and perform an automatic landing. For example, if the battery level drops, the control unit can select the optimal landing site based on the drone's current position and flight altitude and perform an automatic landing. This improves the safety of the drone by performing an automatic landing or emergency stop when an abnormality is detected. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can input battery level data and communication status data into a generative AI, which can then perform abnormality detection and control automatic landing or emergency stop.

[0039] The data collection unit can analyze the drone's flight history and select the optimal data collection method. For example, the data collection unit can identify the optimal altitude and speed for data collection from past flight history. The data collection unit can also select methods to improve the efficiency of data collection in a specific area based on the flight history. Furthermore, the data collection unit can analyze the flight history and determine the optimal flight pattern for data collection. For example, the data collection unit can input past flight data into an AI, which can then select the optimal data collection method. This allows for the selection of the optimal data collection method by analyzing the flight history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input flight history data into a generative AI, which can then select the data collection method.

[0040] The data collection unit can adjust the collection accuracy based on the drone's flight altitude and speed when collecting geographic and obstacle data. For example, at high flight altitudes, the unit can collect data over a wide area and set the accuracy to a lower level. Conversely, at low flight altitudes, the unit can collect detailed data and set the accuracy to a higher level. Furthermore, at high flight speeds, the unit can increase the frequency of data collection to maintain accuracy. For example, the data collection accuracy can be improved by adjusting the collection accuracy based on flight altitude and speed. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input flight altitude and speed data into a generative AI, which can then adjust the collection accuracy.

[0041] The data collection unit can adjust its collection range according to the drone's flight purpose when collecting geographic and obstacle data. For example, in the case of a survey, the data collection unit can collect data over a wide area. In the case of a delivery, the data collection unit can focus on collecting data around the destination. Furthermore, in the case of a photography, the data collection unit can collect data on specific landscapes or buildings. For example, by adjusting the collection range according to the flight purpose, the data collection unit can efficiently collect data. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input flight purpose data into a generative AI, which can then adjust the collection range.

[0042] The data collection unit can optimize data collection points based on the drone's flight path when collecting geographic and obstacle data. For example, the data collection unit can prioritize data collection at important points along the flight path. It can also avoid areas along the flight path where data collection is difficult. Furthermore, the data collection unit can set points to efficiently collect data along the flight path. For example, the data collection unit can input flight path data into a generating AI, which can then optimize the data collection points. This enables efficient data collection by optimizing data collection points based on the flight path. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit can input flight path data into a generating AI, which can then optimize the data collection points.

[0043] The analysis unit can plan the flight path of an object by considering the flight paths of other objects. For example, the analysis unit can monitor the routes of other objects in real time and plan a route that avoids collisions. The analysis unit can also analyze the flight patterns of other objects and plan the optimal flight path. Furthermore, the analysis unit can adjust the flight path by considering the flight schedules of other objects. For example, the analysis unit can input the flight path data of other objects into a generating AI, which can then plan the optimal route to avoid collisions. This allows for the planning of a safe flight path that avoids collisions by considering the flight paths of other objects. Some or all of the above processing in the analysis unit may be performed using a generating AI, or not. For example, the analysis unit can input the flight path data of other objects into a generating AI, which can then plan the flight path.

[0044] The analysis unit can plan the flight path of an autonomously flying object. For example, the analysis unit can use AI to calculate the optimal route for autonomous flight. The analysis unit can also plan a route to avoid obstacles during autonomous flight. Furthermore, the analysis unit can plan a route that minimizes battery consumption during autonomous flight. For example, the analysis unit can input flight path data into a generating AI, which can then plan the optimal route for autonomous flight. This enables efficient autonomous flight by planning the flight path of an autonomously flying object. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input flight path data into a generating AI, which can then plan a route for autonomous flight.

[0045] The analysis unit can apply different analysis algorithms to flight route planning depending on geographical characteristics and types of obstacles. For example, in mountainous areas, the analysis unit can apply an analysis algorithm that emphasizes terrain data. In urban areas, the analysis unit can also apply an analysis algorithm that takes into account the location of buildings. Furthermore, in forested areas, the analysis unit can apply an analysis algorithm that takes into account the location of trees. For example, by applying different analysis algorithms depending on geographical characteristics and types of obstacles, the analysis unit can plan more accurate flight routes. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input geographic data and obstacle data into a generative AI, and the generative AI can select an appropriate analysis algorithm to plan the flight route.

[0046] The analysis unit can optimize the flight route according to the drone's flight purpose when planning the flight path. For example, in the case of delivery purposes, the analysis unit can plan the shortest route. In the case of survey purposes, the analysis unit can also plan a route that covers a wide area. Furthermore, in the case of photography purposes, the analysis unit can also plan a route with good scenery. For example, the analysis unit can input flight purpose data into a generating AI, and the generating AI can optimize the route according to the flight purpose. This makes efficient flight possible by optimizing the route according to the flight purpose. Some or all of the above processing in the analysis unit may be performed using a generating AI, for example, or without using a generating AI. For example, the analysis unit can input flight purpose data into a generating AI, and the generating AI can optimize the route.

[0047] The control unit monitors the battery level and can change the flight route as needed. For example, if the battery level is low, the control unit can change to the shortest route. The control unit can also adjust the flight speed to conserve battery power if it is draining rapidly. Furthermore, the control unit can set charging points along the flight path depending on the battery level. For example, if the battery level is low, the control unit can set the nearest charging point and guide the drone to it. This improves flight safety by changing the flight route according to the battery level. Some or all of the above processes in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can input battery level data into a generative AI, which can then change the flight route.

[0048] The management unit can collect weather data using a weather database and real-time weather sensors, and analyze it using AI. For example, the management unit can obtain historical weather data from the weather database and plan flight routes. It can also obtain current weather data from real-time weather sensors and adjust flight routes. Furthermore, the management unit can have the AI ​​analyze the weather data and propose the optimal flight route. For example, the management unit can input data obtained from temperature and humidity sensors into the AI, which can then plan flight routes that respond to changes in weather. This enables flight management that responds to changes in weather by collecting and analyzing weather data. Some or all of the above processes in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input weather data into a generative AI, which can then analyze the weather data.

[0049] The management unit can adjust its management methods based on the drone's flight altitude and speed during flight management. For example, the management unit can provide comprehensive management information when the altitude is high. It can also provide detailed management information when the altitude is low. Furthermore, it can provide concise management information when the flight speed is high. For example, the management unit can input flight altitude and speed data into a generating AI, which can then adjust the management methods. This allows for appropriate flight management by adjusting the management methods based on flight altitude and speed. Some or all of the above processing in the management unit may be performed using a generating AI, or without one. For example, the management unit can input flight altitude and speed data into a generating AI, which can then adjust the management methods.

[0050] The management unit can optimize its management methods during flight management according to the drone's flight purpose. For example, if the purpose is delivery, the management unit can prioritize managing the delivery route. If the purpose is research, the management unit can prioritize managing data collection. Furthermore, if the purpose is photography, the management unit can prioritize managing the photography points. For example, the management unit can input flight purpose data into a generating AI, which can then optimize the management methods. This enables efficient flight management by optimizing the management methods according to the flight purpose. Some or all of the above processing in the management unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the management unit can input flight purpose data into a generating AI, which can then optimize the management methods.

[0051] The control unit can detect abnormalities such as low battery level or communication loss and perform an automatic landing or emergency stop. For example, the control unit can perform an automatic landing when the battery level falls below 20%. The control unit can also perform an emergency stop if communication is lost for 5 seconds or more. Furthermore, if the battery level drops, the control unit can select the nearest safe landing site and perform an automatic landing. For example, if the battery level drops, the control unit can select the optimal landing site based on the drone's current position and flight altitude and perform an automatic landing. This improves the safety of the drone by performing an automatic landing or emergency stop when an abnormality is detected. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can input battery level data and communication status data into a generative AI, which can then perform abnormality detection and control automatic landing or emergency stop.

[0052] The control unit can select the optimal landing site based on the drone's current position and flight altitude when an anomaly is detected. For example, if the current position is in an urban area, the control unit can select a building rooftop or a public square as the landing site. If the current position is in a forested area, the control unit can also select an open area or a road as the landing site. Furthermore, if the current position is at sea, the control unit can select the nearest ship or floating object as the landing site. For example, when an anomaly is detected, the control unit can input the drone's current position and flight altitude data into a generating AI, which can then select the optimal landing site. This enables a safe landing by selecting the optimal landing site when an anomaly is detected. Some or all of the above processing in the control unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the control unit can input the current position and flight altitude data into a generating AI, which can then select the optimal landing site.

[0053] The control unit can apply different control methods depending on the drone's flight purpose when an anomaly is detected. For example, in the case of delivery, the control unit can apply a control method that prioritizes the safety of the cargo. In the case of survey purposes, the control unit can also apply a control method that prioritizes data preservation. Furthermore, in the case of photography purposes, the control unit can apply a control method that prioritizes the protection of camera equipment. For example, when an anomaly is detected, the control unit can input the drone's flight purpose data into a generating AI, which can then select an appropriate control method. This allows for appropriate responses by applying different control methods depending on the flight purpose. Some or all of the above-described processing in the control unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the control unit can input flight purpose data into a generating AI, which can then select an appropriate control method.

[0054] The control unit can select the optimal emergency stop point based on the drone's flight path when an anomaly is detected. For example, the control unit can select a safe location along the flight path as the emergency stop point. The control unit can also avoid locations along the flight path where emergency stopping would be difficult. Furthermore, the control unit can set points along the flight path that allow for efficient emergency stopping. For example, when an anomaly is detected, the control unit can input the drone's flight path data into a generating AI, which can then select the optimal emergency stop point. This enables safe emergency stopping by selecting the optimal emergency stop point based on the flight path. Some or all of the above processing in the control unit may be performed using a generating AI, or without using a generating AI. For example, the control unit can input flight path data into a generating AI, which can then select the optimal emergency stop point.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The drone's automated flight support system can also include a health monitoring unit that monitors the user's health. This unit can monitor the user's health in real time, for example, by using sensors to measure the user's heart rate and blood pressure. For instance, if the user's heart rate becomes abnormally high, the drone's flight path can be changed to perform an emergency landing. Similarly, if the user's blood pressure fluctuates rapidly, the drone's flight speed can be adjusted to ensure safe flight. Furthermore, the health monitoring unit can optimize the flight path plan based on the user's health. For example, if the user is in good health, a longer flight path can be planned. This enables safe and efficient flight that takes the user's health into consideration.

[0057] The drone's automated flight support system can also be equipped with an environmental data collection unit. This unit can collect environmental data during flight, for example, using air quality sensors and temperature sensors. For instance, it can measure the concentration of airborne pollutants using air quality sensors and adjust the flight route to select a less polluted path. It can also monitor temperature changes using temperature sensors and plan flight routes to minimize battery consumption. Furthermore, the environmental data collection unit can analyze the collected environmental data to help plan future flight routes. For example, it can plan optimal seasonal flight routes based on past environmental data. This enables safe and efficient flight that takes environmental data into consideration.

[0058] The drone's automated flight support system can also be equipped with a cloud connectivity unit that uploads flight data to the cloud in real time. For example, the cloud connectivity unit can upload geographical data and obstacle data collected during flight to the cloud and share it with other drones. This allows other drones flying in the same area to plan safe flight routes using the latest data. Furthermore, the cloud connectivity unit can upload battery data and weather data during flight to the cloud for real-time analysis. This enables optimal flight management based on flight conditions. Additionally, the cloud connectivity unit can upload abnormal flight data to the cloud for rapid response. For example, if the battery level drops, it can search for the nearest charging point on the cloud and notify the drone. This enables safe and efficient flight utilizing cloud connectivity.

[0059] The drone's automated flight support system can also be equipped with a real-time analysis unit that analyzes flight data in real time. For example, the real-time analysis unit can analyze geographical data and obstacle data collected during flight in real time to optimize the flight route. This allows for the planning of the optimal flight route according to the conditions during flight. Furthermore, the real-time analysis unit can analyze battery data and weather data in real time to optimize flight management. In addition, the real-time analysis unit can analyze abnormal data during flight in real time and respond quickly. For example, if the battery level drops, it can search for the nearest charging point in real time and notify the drone. This enables safe and efficient flight utilizing real-time analysis.

[0060] The drone's automated flight support system can also be equipped with a visualization unit that visualizes data during flight. For example, the visualization unit can visualize geographical data and obstacle data collected during flight and provide this information to the user. This allows the user to intuitively understand the flight situation. The visualization unit can also visualize and provide battery data and weather data during flight. Furthermore, the visualization unit can visualize abnormal data during flight and enable rapid response. For example, if the battery level drops, it can search for the nearest charging point based on the visualized data and notify the drone. This enables safe and efficient flight utilizing visualization.

[0061] The drone's automated flight support system can also be equipped with a voice notification unit that provides voice notifications of flight data. For example, the voice notification unit can notify the user of geographical data and obstacle data collected during flight. This allows the user to understand the flight situation without relying on visual cues. The voice notification unit can also provide the user with voice notifications of battery and weather data during flight. Furthermore, the voice notification unit can provide voice notifications of abnormal flight data, enabling quick responses. For example, if the battery level is low, it can voice-notify the user of the nearest charging point and guide the drone. This enables safe and efficient flight utilizing voice notifications.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects geographic and obstacle data. The data collection unit collects geographic and obstacle data using, for example, GPS data and sensors. For example, the data collection unit can collect information on the terrain, building locations, and obstacles such as trees in the area where the drone is flying. The data collection unit can also analyze the drone's flight history and select the optimal data collection method. For example, the data collection unit can identify the optimal altitude and speed for data collection from past flight history. Furthermore, the data collection unit can estimate the user's emotions and adjust the timing of geographic and obstacle data collection based on the estimated user emotions. For example, if the user is feeling stressed, the data collection unit can delay the collection timing and wait until the user is relaxed. Step 2: The analysis unit analyzes the collected data using AI and plans an efficient flight route. For example, the analysis unit can analyze the collected data using AI and plan a route that allows the drone to reach its destination in the shortest distance, or a route that avoids obstacles. The analysis unit can also plan a flight route while considering the flight routes of other flying objects. For example, the analysis unit can monitor the routes of other flying objects in real time and plan a route that avoids collisions. Step 3: The control unit manages the flight based on battery life and weather conditions. For example, the control unit can monitor the battery level and change the flight route if the battery level falls below a certain level. The control unit can also collect weather data using a weather database and real-time weather sensors and analyze it with AI. For example, the control unit can retrieve historical weather data from the weather database and plan the flight route. Step 4: The control unit performs an automatic landing or emergency stop if an abnormality is detected. The control unit can, for example, detect an abnormality such as low battery level or communication failure and perform an automatic landing or emergency stop. For example, the control unit can perform an automatic landing if the battery level falls below a certain level.

[0064] (Example of form 2) The AI ​​assistant for the automated flight support system of a drone according to an embodiment of the present invention is a system that plans a safe flight route based on geographic data and obstacle data, performs flight management considering battery life and weather conditions, and has automatic landing and emergency stop functions. The AI ​​assistant for the automated flight support system of a drone plans a safe flight route based on geographic data and obstacle data, performs flight management considering battery life and weather conditions, and has automatic landing and emergency stop functions. For example, the AI ​​assistant for the automated flight support system of a drone includes a collection unit that collects geographic data and obstacle data. This collection unit collects geographic data and obstacle data using GPS data and sensors. For example, it collects information on the terrain of the area in which the drone will fly, the location of buildings, and obstacles such as trees. Next, it includes an analysis unit that analyzes the collected data. This analysis unit analyzes the collected data using AI and plans an efficient flight route. For example, it plans a route for the drone to reach its destination in the shortest distance, or a route to avoid obstacles. It is also possible to plan a flight route considering the flight routes of other flying objects. Furthermore, it includes a management unit that performs flight management. This control unit manages flight based on battery life and weather conditions. For example, it can monitor the battery level and change the flight route as needed. It also collects weather data using a weather database and real-time weather sensors, and analyzes it with AI to perform flight management that responds to weather changes. Finally, it is equipped with a control unit that performs automatic landing or emergency stop if an anomaly is detected. This control unit detects anomalies such as low battery level or communication loss and performs automatic landing or emergency stop. For example, it can safely land the drone if the battery level falls below a certain level or if communication is lost. In this way, the drone's automatic flight support system AI assistant supports the safe and efficient flight of drones by planning safe flight routes based on geographic and obstacle data, managing flight considering battery life and weather conditions, and having automatic landing and emergency stop functions.

[0065] The AI ​​assistant for the automated flight support system of a drone according to this embodiment comprises a data collection unit, an analysis unit, a management unit, and a control unit. The data collection unit collects geographic data and obstacle data. The data collection unit collects geographic data and obstacle data using, for example, GPS data and sensors. For example, the data collection unit can collect information on the terrain of the area in which the drone is flying, the location of buildings, and obstacles such as trees. The data collection unit can also analyze the drone's flight history and select the optimal data collection method. For example, the data collection unit can identify the optimal altitude and speed for data collection from past flight history. Furthermore, the data collection unit can estimate the user's emotions and adjust the timing of data collection for geographic data and obstacle data based on the estimated user emotions. For example, if the user is feeling stressed, the data collection unit can delay the timing of data collection and wait until the user is relaxed. The analysis unit analyzes the collected data using AI and plans an efficient flight route. For example, the analysis unit can analyze the collected data using AI and plan a route for the drone to reach its destination in the shortest distance or a route to avoid obstacles. The analysis unit can also plan flight routes by considering the flight paths of other flying objects. For example, the analysis unit can monitor the routes of other flying objects in real time and plan a route that avoids collisions. The management unit performs flight management based on battery life and weather. For example, the management unit can monitor the battery level and change the flight route if the battery level falls below a certain level. The management unit can also collect weather data using a weather database and real-time weather sensors and analyze it with AI. For example, the management unit can obtain historical weather data from a weather database and plan the flight route. The control unit performs automatic landing or emergency stop if an anomaly is detected. For example, the control unit can detect anomalies such as low battery level or communication loss and perform automatic landing or emergency stop. For example, the control unit can perform automatic landing if the battery level falls below a certain level.As a result, the AI ​​assistant for the drone's automated flight support system according to this embodiment can assist in the safe and efficient flight of the drone.

[0066] The data collection unit collects geographic and obstacle data. For example, it uses GPS data and sensors to collect geographic and obstacle data. Specifically, it uses a high-precision GPS module and multiple sensors (e.g., LiDAR, camera, ultrasonic sensor, etc.) mounted on the drone to acquire detailed topographic information of the flight area and location information of obstacles such as buildings and trees. The LiDAR sensor uses laser light to accurately measure the distance to surrounding objects and generate a 3D map. The camera provides visual information and uses image analysis techniques to identify the type and shape of obstacles. The ultrasonic sensor detects obstacles at close range, enabling immediate response to prevent drone collisions. The data collection unit processes the data obtained from these sensors in real time and transmits it to a central database. Furthermore, the data collection unit can analyze the drone's flight history and select the optimal data collection method. For example, it can analyze past flight data to identify that data collection is most efficient at specific altitudes and speeds. This allows the data collection unit to maximize the accuracy and efficiency of data collection. Furthermore, the data collection unit can estimate the user's emotions and adjust the timing of geographic and obstacle data collection based on the estimated emotions. For example, if the user is feeling stressed, the data collection timing can be delayed until the user is relaxed. This reduces the burden on the user and provides a more comfortable operating environment.

[0067] The analysis unit uses AI to analyze collected data and plan efficient flight routes. Specifically, the AI ​​analyzes collected geographic and obstacle data to calculate the optimal route for the drone to reach its destination in the shortest distance. The AI ​​uses machine learning algorithms to learn from past flight and environmental data to predict the optimal flight route. For example, the AI ​​considers terrain topography and obstacle placement to select an energy-efficient route. The analysis unit can also plan flight routes considering the flight paths of other aircraft. For example, the AI ​​monitors the flight paths of other drones and aircraft in real time and calculates routes to avoid collisions. This includes coordinating with air traffic control systems to obtain location information of other aircraft and take appropriate evasive action. Furthermore, the analysis unit can also plan flight routes considering weather data. For example, it analyzes weather conditions such as wind speed, wind direction, and rainfall to select a route that minimizes the impact of these conditions on flight. In this way, the analysis unit can support the safe and efficient flight of drones.

[0068] The control unit manages flights based on battery life and weather conditions. Specifically, it constantly monitors the drone's battery level and changes the flight route if the battery level falls below a certain point. For example, if the battery level is low, it plans a route to the nearest safe landing site to ensure the drone lands safely. The control unit can also collect weather data using a weather database and real-time weather sensors and analyze it with AI. For example, it can retrieve historical weather data from the weather database to plan flight routes. Real-time weather sensors monitor current weather conditions and provide information to respond to sudden weather changes. This allows the control unit to minimize the impact of weather conditions on flights. Furthermore, the control unit provides interfaces for quickly changing flight plans and responding to emergencies. For example, it provides an intuitive interface for users to manually change flight routes or instruct emergency landings. This allows the control unit to manage drone flights safely and efficiently.

[0069] The control unit performs automatic landing or emergency stop if an abnormality is detected. Specifically, the control unit monitors battery level, communication loss, sensor malfunctions, etc., in real time and takes appropriate action when an abnormality is detected. For example, if the battery level falls below a certain level, it can perform an automatic landing. When performing an automatic landing, it selects a safe landing site based on geographic and obstacle data collected in advance, ensuring that the drone lands safely. In addition, if a communication loss occurs, the control unit safely stops the drone according to a pre-configured emergency stop procedure. This includes the option for the drone to continue hovering at its current position or to automatically land at the nearest safe location. Furthermore, the control unit also takes appropriate action if a sensor malfunction is detected. For example, if the LiDAR sensor or camera is not functioning properly, it temporarily suspends the drone's flight and waits until the malfunction is resolved. In this way, the control unit can ensure the safety of the drone and minimize the risks when an abnormality occurs.

[0070] The data collection unit can collect geographic data and obstacle data using GPS data and specific sensors (e.g., LiDAR sensors). For example, the data collection unit can collect location information, speed information, and altitude information using GPS data. The data collection unit can also collect terrain data and obstacle data using LiDAR sensors. For example, the data collection unit can detect the location of trees and buildings with high accuracy using LiDAR sensors. Furthermore, the data collection unit can also collect obstacle data using camera sensors and ultrasonic sensors. For example, the data collection unit can collect visual information using camera sensors and distance information using ultrasonic sensors. This improves the accuracy of data collection for geographic data and obstacle data by using GPS data and sensors. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input GPS data and sensor data into a generating AI, which can then analyze the data.

[0071] The analysis unit can analyze the collected data using AI and plan a flight route that is fuel-efficient and time-efficient. For example, the analysis unit can analyze the collected geographic and obstacle data using AI to plan the optimal flight route. For example, the analysis unit can use AI to plan a route that allows the drone to reach its destination in the shortest distance. The analysis unit can also use AI to plan a route that avoids obstacles. Furthermore, the analysis unit can plan a flight route while considering the flight routes of other flying objects. For example, the analysis unit can monitor the routes of other flying objects in real time and plan a route that avoids collisions. This makes it possible to plan an efficient flight route by using AI. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the collected data into a generative AI, and the generative AI can plan the flight route.

[0072] The control unit monitors the battery level and can change the flight route when the battery level falls below a certain point. For example, the control unit can monitor the battery level in real time and change to the shortest route when the battery level falls below 20%. The control unit can also adjust the flight speed to reduce battery consumption when it is draining rapidly. Furthermore, the control unit can set charging points along the flight depending on the battery level. For example, the control unit can set the nearest charging point when the battery level is low and guide the drone to the charging point. This improves flight safety by changing the flight route according to the battery level. Some or all of the above processes in the control unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the control unit can input battery level data into a generative AI, and the generative AI can change the flight route.

[0073] The management unit can collect weather data using a weather database and real-time weather sensors (e.g., temperature sensors, humidity sensors) and analyze it using AI. For example, the management unit can obtain historical weather data from the weather database and plan flight routes. It can also obtain current weather data from real-time weather sensors and adjust flight routes. Furthermore, the management unit can have the AI ​​analyze the weather data and propose the optimal flight route. For example, the management unit can input data obtained from temperature and humidity sensors into the AI, which can then plan flight routes that respond to changes in weather. This enables flight management that responds to changes in weather by collecting and analyzing weather data. Some or all of the above processes in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input weather data into a generative AI, which can then analyze the weather data.

[0074] The control unit can detect abnormalities such as low battery level or communication loss and perform an automatic landing or emergency stop. For example, the control unit can perform an automatic landing when the battery level falls below 20%. The control unit can also perform an emergency stop if communication is lost for 5 seconds or more. Furthermore, if the battery level drops, the control unit can select the nearest safe landing site and perform an automatic landing. For example, if the battery level drops, the control unit can select the optimal landing site based on the drone's current position and flight altitude and perform an automatic landing. This improves the safety of the drone by performing an automatic landing or emergency stop when an abnormality is detected. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can input battery level data and communication status data into a generative AI, which can then perform abnormality detection and control automatic landing or emergency stop.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of geographic and obstacle data collection based on the estimated user emotions. The data collection unit can estimate the user's emotions using, for example, facial recognition technology. For example, if the user is stressed, the data collection unit can delay the collection timing and wait until the user is relaxed. Conversely, if the user is relaxed, the data collection unit can speed up the collection timing to collect data efficiently. Furthermore, if the user is in a hurry, the data collection unit can optimize the collection timing to collect data quickly. This improves the efficiency of data collection by adjusting the collection timing 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. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input user image data acquired by facial recognition technology into a generative AI, which can then estimate the user's emotions.

[0076] The data collection unit can analyze the drone's flight history and select the optimal data collection method. For example, the data collection unit can identify the optimal altitude and speed for data collection from past flight history. The data collection unit can also select methods to improve the efficiency of data collection in a specific area based on the flight history. Furthermore, the data collection unit can analyze the flight history and determine the optimal flight pattern for data collection. For example, the data collection unit can input past flight data into an AI, which can then select the optimal data collection method. This allows for the selection of the optimal data collection method by analyzing the flight history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input flight history data into a generative AI, which can then select the data collection method.

[0077] The data collection unit can adjust the collection accuracy based on the drone's flight altitude and speed when collecting geographic and obstacle data. For example, at high flight altitudes, the unit can collect data over a wide area and set the accuracy to a lower level. Conversely, at low flight altitudes, the unit can collect detailed data and set the accuracy to a higher level. Furthermore, at high flight speeds, the unit can increase the frequency of data collection to maintain accuracy. For example, the data collection accuracy can be improved by adjusting the collection accuracy based on flight altitude and speed. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input flight altitude and speed data into a generative AI, which can then adjust the collection accuracy.

[0078] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. The data collection unit can estimate the user's emotions using, for example, facial recognition technology. For example, if the user is stressed, the data collection unit can prioritize collecting important data. Also, if the user is relaxed, the data collection unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. In this way, important data can be collected preferentially by determining the priority of data 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. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input user image data acquired with facial recognition technology into a generative AI, which can then estimate the user's emotions.

[0079] The data collection unit can adjust its collection range according to the drone's flight purpose when collecting geographic and obstacle data. For example, in the case of a survey, the data collection unit can collect data over a wide area. In the case of a delivery, the data collection unit can focus on collecting data around the destination. Furthermore, in the case of a photography, the data collection unit can collect data on specific landscapes or buildings. For example, by adjusting the collection range according to the flight purpose, the data collection unit can efficiently collect data. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input flight purpose data into a generative AI, which can then adjust the collection range.

[0080] The data collection unit can optimize data collection points based on the drone's flight path when collecting geographic and obstacle data. For example, the data collection unit can prioritize data collection at important points along the flight path. It can also avoid areas along the flight path where data collection is difficult. Furthermore, the data collection unit can set points to efficiently collect data along the flight path. For example, the data collection unit can input flight path data into a generating AI, which can then optimize the data collection points. This enables efficient data collection by optimizing data collection points based on the flight path. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit can input flight path data into a generating AI, which can then optimize the data collection points.

[0081] The analysis unit can estimate the user's emotions and adjust the flight route planning method based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, facial recognition technology. For example, if the user is relaxed, the analysis unit can plan a route with good scenery. Also, if the user is in a hurry, the analysis unit can plan the shortest route. Furthermore, if the user is stressed, the analysis unit can plan a route with fewer obstacles. In this way, by adjusting the flight route planning method based on the user's emotions, a flight route that meets the user's needs can be planned. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input user image data acquired by facial recognition technology into the generative AI, and the generative AI can estimate the user's emotions.

[0082] The analysis unit can plan the flight path of an object by considering the flight paths of other objects. For example, the analysis unit can monitor the routes of other objects in real time and plan a route that avoids collisions. The analysis unit can also analyze the flight patterns of other objects and plan the optimal flight path. Furthermore, the analysis unit can adjust the flight path by considering the flight schedules of other objects. For example, the analysis unit can input the flight path data of other objects into a generating AI, which can then plan the optimal route to avoid collisions. This allows for the planning of a safe flight path that avoids collisions by considering the flight paths of other objects. Some or all of the above processing in the analysis unit may be performed using a generating AI, or not. For example, the analysis unit can input the flight path data of other objects into a generating AI, which can then plan the flight path.

[0083] The analysis unit can plan the flight path of an autonomously flying object. For example, the analysis unit can use AI to calculate the optimal route for autonomous flight. The analysis unit can also plan a route to avoid obstacles during autonomous flight. Furthermore, the analysis unit can plan a route that minimizes battery consumption during autonomous flight. For example, the analysis unit can input flight path data into a generating AI, which can then plan the optimal route for autonomous flight. This enables efficient autonomous flight by planning the flight path of an autonomously flying object. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input flight path data into a generating AI, which can then plan a route for autonomous flight.

[0084] The analysis unit can estimate the user's emotions and determine the priority of flight routes based on the estimated emotions. The analysis unit can estimate the user's emotions using, for example, facial recognition technology. For example, if the user is feeling stressed, the analysis unit can prioritize the safest route. If the user is relaxed, the analysis unit can also prioritize a route with good scenery. Furthermore, if the user is in a hurry, the analysis unit can also prioritize the shortest route. In this way, by determining the priority of flight routes based on the user's emotions, a flight route that meets the user's needs can be planned preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input user image data acquired by facial recognition technology into a generative AI, and the generative AI can estimate the user's emotions.

[0085] The analysis unit can apply different analysis algorithms to flight route planning depending on geographical characteristics and types of obstacles. For example, in mountainous areas, the analysis unit can apply an analysis algorithm that emphasizes terrain data. In urban areas, the analysis unit can also apply an analysis algorithm that takes into account the location of buildings. Furthermore, in forested areas, the analysis unit can apply an analysis algorithm that takes into account the location of trees. For example, by applying different analysis algorithms depending on geographical characteristics and types of obstacles, the analysis unit can plan more accurate flight routes. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input geographic data and obstacle data into a generative AI, and the generative AI can select an appropriate analysis algorithm to plan the flight route.

[0086] The analysis unit can optimize the flight route according to the drone's flight purpose when planning the flight path. For example, in the case of delivery purposes, the analysis unit can plan the shortest route. In the case of survey purposes, the analysis unit can also plan a route that covers a wide area. Furthermore, in the case of photography purposes, the analysis unit can also plan a route with good scenery. For example, the analysis unit can input flight purpose data into a generating AI, and the generating AI can optimize the route according to the flight purpose. This makes efficient flight possible by optimizing the route according to the flight purpose. Some or all of the above processing in the analysis unit may be performed using a generating AI, for example, or without using a generating AI. For example, the analysis unit can input flight purpose data into a generating AI, and the generating AI can optimize the route.

[0087] The control unit can estimate the user's emotions and adjust flight management methods based on the estimated emotions. For example, the control unit can estimate the user's emotions using facial recognition technology. For example, if the user is stressed, the control unit can simplify flight management to reduce the user's burden. Also, if the user is relaxed, the control unit can provide detailed flight management information. Furthermore, if the user is in a hurry, the control unit can perform rapid flight management. In this way, by adjusting flight management methods based on the user's emotions, flight management that meets the user's needs becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using generative AI, for example, or without generative AI. For example, the control unit can input user image data acquired by facial recognition technology into a generative AI, and the generative AI can estimate the user's emotions.

[0088] The control unit monitors the battery level and can change the flight route as needed. For example, if the battery level is low, the control unit can change to the shortest route. The control unit can also adjust the flight speed to conserve battery power if it is draining rapidly. Furthermore, the control unit can set charging points along the flight path depending on the battery level. For example, if the battery level is low, the control unit can set the nearest charging point and guide the drone to it. This improves flight safety by changing the flight route according to the battery level. Some or all of the above processes in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can input battery level data into a generative AI, which can then change the flight route.

[0089] The management unit can collect weather data using a weather database and real-time weather sensors, and analyze it using AI. For example, the management unit can obtain historical weather data from the weather database and plan flight routes. It can also obtain current weather data from real-time weather sensors and adjust flight routes. Furthermore, the management unit can have the AI ​​analyze the weather data and propose the optimal flight route. For example, the management unit can input data obtained from temperature and humidity sensors into the AI, which can then plan flight routes that respond to changes in weather. This enables flight management that responds to changes in weather by collecting and analyzing weather data. Some or all of the above processes in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input weather data into a generative AI, which can then analyze the weather data.

[0090] The management unit can estimate the user's emotions and determine flight management priorities based on the estimated emotions. For example, the management unit can estimate the user's emotions using facial recognition technology. For instance, if the user is stressed, the management unit can prioritize critical flight management tasks. Conversely, if the user is relaxed, the management unit can prioritize detailed flight management tasks. Furthermore, if the user is in a hurry, the management unit can prioritize flight management tasks that can be completed quickly. This allows for the priority processing of critical flight management tasks by determining flight management priorities based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the management unit may be performed using, for example, generative AI, or not. For example, the management unit can input user image data acquired by facial recognition technology into a generative AI, which can then estimate the user's emotions.

[0091] The management unit can adjust its management methods based on the drone's flight altitude and speed during flight management. For example, the management unit can provide comprehensive management information when the altitude is high. It can also provide detailed management information when the altitude is low. Furthermore, it can provide concise management information when the flight speed is high. For example, the management unit can input flight altitude and speed data into a generating AI, which can then adjust the management methods. This allows for appropriate flight management by adjusting the management methods based on flight altitude and speed. Some or all of the above processing in the management unit may be performed using a generating AI, or without one. For example, the management unit can input flight altitude and speed data into a generating AI, which can then adjust the management methods.

[0092] The management unit can optimize its management methods during flight management according to the drone's flight purpose. For example, if the purpose is delivery, the management unit can prioritize managing the delivery route. If the purpose is research, the management unit can prioritize managing data collection. Furthermore, if the purpose is photography, the management unit can prioritize managing the photography points. For example, the management unit can input flight purpose data into a generating AI, which can then optimize the management methods. This enables efficient flight management by optimizing the management methods according to the flight purpose. Some or all of the above processing in the management unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the management unit can input flight purpose data into a generating AI, which can then optimize the management methods.

[0093] The control unit can estimate the user's emotions and adjust the automatic landing or emergency stop method based on the estimated user emotions. The control unit can estimate the user's emotions, for example, using facial recognition technology. For example, if the user is feeling stressed, the control unit can perform a rapid automatic landing. Conversely, if the user is relaxed, the control unit can perform a slow and safe automatic landing. Furthermore, if the user is in a hurry, the control unit can perform a rapid emergency stop. This allows for responses tailored to the user's needs by adjusting the automatic landing or emergency stop method 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. Some or all of the above-described processing in the control unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the control unit can input user image data acquired by facial recognition technology into a generative AI, which can then estimate the user's emotions.

[0094] The control unit can detect abnormalities such as low battery level or communication loss and perform an automatic landing or emergency stop. For example, the control unit can perform an automatic landing when the battery level falls below 20%. The control unit can also perform an emergency stop if communication is lost for 5 seconds or more. Furthermore, if the battery level drops, the control unit can select the nearest safe landing site and perform an automatic landing. For example, if the battery level drops, the control unit can select the optimal landing site based on the drone's current position and flight altitude and perform an automatic landing. This improves the safety of the drone by performing an automatic landing or emergency stop when an abnormality is detected. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can input battery level data and communication status data into a generative AI, which can then perform abnormality detection and control automatic landing or emergency stop.

[0095] The control unit can select the optimal landing site based on the drone's current position and flight altitude when an anomaly is detected. For example, if the current position is in an urban area, the control unit can select a building rooftop or a public square as the landing site. If the current position is in a forested area, the control unit can also select an open area or a road as the landing site. Furthermore, if the current position is at sea, the control unit can select the nearest ship or floating object as the landing site. For example, when an anomaly is detected, the control unit can input the drone's current position and flight altitude data into a generating AI, which can then select the optimal landing site. This enables a safe landing by selecting the optimal landing site when an anomaly is detected. Some or all of the above processing in the control unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the control unit can input the current position and flight altitude data into a generating AI, which can then select the optimal landing site.

[0096] The control unit can estimate the user's emotions and determine the priority of automatic landing or emergency stop based on the estimated emotions. The control unit can estimate the user's emotions, for example, using facial recognition technology. For example, if the user is feeling stressed, the control unit can prioritize a quick automatic landing. If the user is relaxed, the control unit can prioritize safety and prioritize an automatic landing. Furthermore, if the user is in a hurry, the control unit can prioritize an emergency stop. This allows for appropriate responses by determining the priority of automatic landing or emergency stop 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. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the control unit can input user image data acquired by facial recognition technology into a generative AI, which can then estimate the user's emotions.

[0097] The control unit can apply different control methods depending on the drone's flight purpose when an anomaly is detected. For example, in the case of delivery, the control unit can apply a control method that prioritizes the safety of the cargo. In the case of survey purposes, the control unit can also apply a control method that prioritizes data preservation. Furthermore, in the case of photography purposes, the control unit can apply a control method that prioritizes the protection of camera equipment. For example, when an anomaly is detected, the control unit can input the drone's flight purpose data into a generating AI, which can then select an appropriate control method. This allows for appropriate responses by applying different control methods depending on the flight purpose. Some or all of the above-described processing in the control unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the control unit can input flight purpose data into a generating AI, which can then select an appropriate control method.

[0098] The control unit can select the optimal emergency stop point based on the drone's flight path when an anomaly is detected. For example, the control unit can select a safe location along the flight path as the emergency stop point. The control unit can also avoid locations along the flight path where emergency stopping would be difficult. Furthermore, the control unit can set points along the flight path that allow for efficient emergency stopping. For example, when an anomaly is detected, the control unit can input the drone's flight path data into a generating AI, which can then select the optimal emergency stop point. This enables safe emergency stopping by selecting the optimal emergency stop point based on the flight path. Some or all of the above processing in the control unit may be performed using a generating AI, or without using a generating AI. For example, the control unit can input flight path data into a generating AI, which can then select the optimal emergency stop point.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The drone's automated flight support system can also include a health monitoring unit that monitors the user's health. This unit can monitor the user's health in real time, for example, by using sensors to measure the user's heart rate and blood pressure. For instance, if the user's heart rate becomes abnormally high, the drone's flight path can be changed to perform an emergency landing. Similarly, if the user's blood pressure fluctuates rapidly, the drone's flight speed can be adjusted to ensure safe flight. Furthermore, the health monitoring unit can optimize the flight path plan based on the user's health. For example, if the user is in good health, a longer flight path can be planned. This enables safe and efficient flight that takes the user's health into consideration.

[0101] The drone's automated flight support system can also be equipped with an environmental data collection unit. This unit can collect environmental data during flight, for example, using air quality sensors and temperature sensors. For instance, it can measure the concentration of airborne pollutants using air quality sensors and adjust the flight route to select a less polluted path. It can also monitor temperature changes using temperature sensors and plan flight routes to minimize battery consumption. Furthermore, the environmental data collection unit can analyze the collected environmental data to help plan future flight routes. For example, it can plan optimal seasonal flight routes based on past environmental data. This enables safe and efficient flight that takes environmental data into consideration.

[0102] The drone's automated flight support system can further estimate the user's emotions and add entertainment elements to the flight route based on those emotions. For example, if the user is relaxed, the flight route can include beautiful scenery. If the user is stressed, including more natural landscapes in the flight route can enhance relaxation. Furthermore, if the user is excited, adventure elements can be added to the flight route. For example, a route flying over mountainous areas or coastlines can be planned. This allows for improved user satisfaction by optimizing the flight route with entertainment elements based on the user's emotions.

[0103] The drone's automated flight support system can further estimate the user's emotions and select in-flight music based on those emotions. For example, if the user is relaxed, calming music can be played. If the user is stressed, relaxing music can be played to reduce their stress. Furthermore, if the user is excited, energetic music can be played. In this way, selecting in-flight music based on the user's emotions can improve the user experience.

[0104] The drone's automated flight support system can further estimate the user's emotions and adjust the in-flight lighting based on those emotions. For example, if the user is relaxed, soft lighting can be used. If the user is stressed, relaxing lighting can be used to reduce their stress. Furthermore, if the user is excited, bright lighting can be used. In this way, adjusting the in-flight lighting based on the user's emotions can improve the user experience.

[0105] The drone's automated flight assistance system can further estimate the user's emotions and adjust in-flight notifications based on those emotions. For example, if the user is relaxed, notifications can be minimized. If the user is stressed, only important notifications can be displayed to reduce their stress. Furthermore, if the user is excited, detailed notifications can be displayed. This allows for an improved user experience by adjusting in-flight notifications based on the user's emotions.

[0106] The drone's automated flight support system can also be equipped with a cloud connectivity unit that uploads flight data to the cloud in real time. For example, the cloud connectivity unit can upload geographical data and obstacle data collected during flight to the cloud and share it with other drones. This allows other drones flying in the same area to plan safe flight routes using the latest data. Furthermore, the cloud connectivity unit can upload battery data and weather data during flight to the cloud for real-time analysis. This enables optimal flight management based on flight conditions. Additionally, the cloud connectivity unit can upload abnormal flight data to the cloud for rapid response. For example, if the battery level drops, it can search for the nearest charging point on the cloud and notify the drone. This enables safe and efficient flight utilizing cloud connectivity.

[0107] The drone's automated flight support system can also be equipped with a real-time analysis unit that analyzes flight data in real time. For example, the real-time analysis unit can analyze geographical data and obstacle data collected during flight in real time to optimize the flight route. This allows for the planning of the optimal flight route according to the conditions during flight. Furthermore, the real-time analysis unit can analyze battery data and weather data in real time to optimize flight management. In addition, the real-time analysis unit can analyze abnormal data during flight in real time and respond quickly. For example, if the battery level drops, it can search for the nearest charging point in real time and notify the drone. This enables safe and efficient flight utilizing real-time analysis.

[0108] The drone's automated flight support system can also be equipped with a visualization unit that visualizes data during flight. For example, the visualization unit can visualize geographical data and obstacle data collected during flight and provide this information to the user. This allows the user to intuitively understand the flight situation. The visualization unit can also visualize and provide battery data and weather data during flight. Furthermore, the visualization unit can visualize abnormal data during flight and enable rapid response. For example, if the battery level drops, it can search for the nearest charging point based on the visualized data and notify the drone. This enables safe and efficient flight utilizing visualization.

[0109] The drone's automated flight support system can also be equipped with a voice notification unit that provides voice notifications of flight data. For example, the voice notification unit can notify the user of geographical data and obstacle data collected during flight. This allows the user to understand the flight situation without relying on visual cues. The voice notification unit can also provide the user with voice notifications of battery and weather data during flight. Furthermore, the voice notification unit can provide voice notifications of abnormal flight data, enabling quick responses. For example, if the battery level is low, it can voice-notify the user of the nearest charging point and guide the drone. This enables safe and efficient flight utilizing voice notifications.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects geographic and obstacle data. The data collection unit collects geographic and obstacle data using, for example, GPS data and sensors. For example, the data collection unit can collect information on the terrain, building locations, and obstacles such as trees in the area where the drone is flying. The data collection unit can also analyze the drone's flight history and select the optimal data collection method. For example, the data collection unit can identify the optimal altitude and speed for data collection from past flight history. Furthermore, the data collection unit can estimate the user's emotions and adjust the timing of geographic and obstacle data collection based on the estimated user emotions. For example, if the user is feeling stressed, the data collection unit can delay the collection timing and wait until the user is relaxed. Step 2: The analysis unit analyzes the collected data using AI and plans an efficient flight route. For example, the analysis unit can analyze the collected data using AI and plan a route that allows the drone to reach its destination in the shortest distance, or a route that avoids obstacles. The analysis unit can also plan a flight route while considering the flight routes of other flying objects. For example, the analysis unit can monitor the routes of other flying objects in real time and plan a route that avoids collisions. Step 3: The control unit manages the flight based on battery life and weather conditions. For example, the control unit can monitor the battery level and change the flight route if the battery level falls below a certain level. The control unit can also collect weather data using a weather database and real-time weather sensors and analyze it with AI. For example, the control unit can retrieve historical weather data from the weather database and plan the flight route. Step 4: The control unit performs an automatic landing or emergency stop if an abnormality is detected. The control unit can, for example, detect an abnormality such as low battery level or communication failure and perform an automatic landing or emergency stop. For example, the control unit can perform an automatic landing if the battery level falls below a certain level.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] For example, the collection unit can collect geographic data and obstacle data using the camera 42 and sensors of the smart device 14. The analysis unit uses AI to analyze the data collected by the specific processing unit 290 of the data processing device 12 and plans an efficient flight route. The management unit uses the specific processing unit 290 of the data processing device 12 to manage the flight based on battery life and weather conditions. The control unit performs an automatic landing or emergency stop if an abnormality is detected by the control unit 46A of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

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

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0129] 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.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0131] For example, the data collection unit can collect geographic data and obstacle data using the camera 42 and sensors of the smart glasses 214. The analysis unit uses AI to analyze the data collected by the identification processing unit 290 of the data processing device 12 and plans an efficient flight route. The management unit uses the identification processing unit 290 of the data processing device 12 to manage the flight based on battery life and weather conditions. The control unit performs an automatic landing or emergency stop if an abnormality is detected by the control unit 46A of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] For example, the data collection unit can collect geographic data and obstacle data using the camera 42 and sensors of the headset terminal 314. The analysis unit uses AI to analyze the data collected by the specific processing unit 290 of the data processing device 12 and plans an efficient flight route. The management unit uses the specific processing unit 290 of the data processing device 12 to manage the flight based on battery life and weather conditions. The control unit performs an automatic landing or emergency stop if an abnormality is detected by the control unit 46A of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] For example, the collection unit can collect geographic data and obstacle data using the camera 42 and sensors of the robot 414. The analysis unit uses AI to analyze the data collected by the specific processing unit 290 of the data processing device 12 and plans an efficient flight route. The management unit uses the specific processing unit 290 of the data processing device 12 to manage the flight based on battery life and weather conditions. The control unit performs an automatic landing or emergency stop if an abnormality is detected by the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A collection unit that collects geographic data and obstacle data, An analysis unit analyzes the data collected by the aforementioned collection unit to plan the flight path of the flying object, A control unit that manages the flight based on the battery life of the aforementioned flying object and the weather, The system includes a control unit that performs an automatic landing or emergency stop when an abnormality is detected (for example, low battery level or communication failure). A system characterized by the following features. (Note 2) The aforementioned collection unit is Geographic and obstacle data are collected using GPS data and specific sensors (e.g., LiDAR sensors). The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed using AI to plan flight routes that are fuel-efficient and time-efficient. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, The system monitors the battery level and changes the flight path if the battery level falls below a certain point. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, Weather data is collected using weather databases and real-time weather sensors (e.g., temperature sensors, humidity sensors), and then analyzed using AI. The system described in Appendix 1, characterized by the features described herein. (Note 6) The control unit, The system detects abnormalities such as low battery level or communication interruption (for example, battery level below 20% or communication interruption for 5 seconds or more) and performs an automatic landing or emergency stop. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions (for example, using facial recognition technology) and adjusts the timing of geographic and obstacle data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze drone flight history and select the most efficient method for data collection. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting geographic and obstacle data, the collection accuracy is adjusted based on the drone's flight altitude and speed (for example, collecting data over a wide area with low accuracy at high altitudes, and over a narrow area with high accuracy at low altitudes). The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the user's emotions (for example, using facial recognition technology) and prioritizes the data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting geographic and obstacle data, adjust the collection range according to the drone's flight purpose. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting geographic and obstacle data, the collection points are optimized based on the drone's flight path. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the flight route planning method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Plan the flight path of the flying object, taking into account the flight paths of other flying objects. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Planning the flight path of an autonomous flying object. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and determines the priority of flight routes based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When planning flight routes, different analysis algorithms are applied depending on geographical characteristics and types of obstacles. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When planning flight routes, optimize the route according to the drone's flight purpose. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, The system estimates the user's emotions and adjusts flight management methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, Monitor battery level and change flight path as needed. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, Weather data is collected using weather databases and real-time weather sensors, and then analyzed using AI. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, The system estimates user emotions and determines flight management priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, During flight management, the management method is adjusted based on the drone's flight altitude and speed. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, During flight management, optimize the management method according to the drone's flight purpose. The system described in Appendix 1, characterized by the features described herein. (Note 25) The control unit, It estimates the user's emotions and adjusts the automatic landing and emergency stop methods based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The control unit, The system detects abnormalities such as low battery level or communication failure and initiates an automatic landing or emergency stop. The system described in Appendix 1, characterized by the features described herein. (Note 27) The control unit, When an anomaly is detected, the system selects the optimal landing site based on the drone's current position and flight altitude. The system described in Appendix 1, characterized by the features described herein. (Note 28) The control unit, The system estimates the user's emotions and determines the priority of automatic landings and emergency stops based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The control unit, When an anomaly is detected, different control methods are applied depending on the drone's flight purpose. The system described in Appendix 1, characterized by the features described herein. (Note 30) The control unit, When an anomaly is detected, the system selects the optimal emergency stop point based on the drone's flight path. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects geographic data and obstacle data, An analysis unit analyzes the data collected by the aforementioned collection unit to plan the flight path of the flying object, A control unit that manages the flight based on the battery life of the aforementioned flying object and the weather, The vehicle includes a control unit that performs automatic landing or emergency stop when an abnormality is detected. A system characterized by the following features.

2. The aforementioned collection unit is Use GPS data and specific sensors to collect geographic and obstacle data. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed using AI to plan flight routes that are fuel-efficient and time-efficient. The system according to feature 1.

4. The aforementioned management department, The system monitors the battery level and changes the flight path if the battery level falls below a certain point. The system according to feature 1.

5. The aforementioned management department, Weather data is collected using weather databases and real-time weather sensors, and then analyzed using AI. The system according to feature 1.

6. The control unit, The system detects abnormalities such as low battery level or communication failure and initiates an automatic landing or emergency stop. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of geographic and obstacle data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze drone flight history and select the most efficient method for data collection. The system according to feature 1.

9. The aforementioned collection unit is When collecting geographic and obstacle data, the collection accuracy is adjusted based on the drone's flight altitude and speed. The system according to feature 1.

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