System

Abstract

An object of a system according to an embodiment is to detect an obstacle during flight of a drone and appropriately adjust a flight parameter.SOLUTION: A system according to an embodiment includes a flight path generation unit, an obstacle detection unit, and a flight situation monitoring unit. The flight path generation unit generates a flight path based on an instruction from a user. The obstacle detection unit detects an obstacle in flight based on the flight route generated by the flight route generation unit. The flight situation monitoring unit monitors a flight situation based on the obstacle detected by the obstacle detection unit, and adjusts a flight parameter as necessary.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of making it difficult to properly detect obstacles and adjust flight parameters during drone flight.

[0005] The system of the embodiment aims to detect obstacles during drone flight and appropriately adjust flight parameters. [Means for solving the problem]

[0006] The system according to the embodiment includes a flight path generation unit, an obstacle detection unit, and a flight status monitoring unit. The flight path generation unit generates a flight path based on instructions from a user. The obstacle detection unit detects obstacles during flight based on the flight path generated by the flight path generation unit. The flight status monitoring unit monitors the flight status based on the obstacles detected by the obstacle detection unit and adjusts flight parameters as necessary. [Effects of the Invention]

[0007] The system according to the embodiment can detect obstacles during drone flight and adjust flight parameters accordingly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A drone flight system according to an embodiment of the present invention is a system that automates and optimizes drone flight using AI technology, thereby enabling drone flight to be performed more efficiently and safely.

[0029] A drone flight system according to an embodiment includes a flight path generation unit, an obstacle detection unit, and a flight status monitoring unit. The flight path generation unit generates a flight path based on user instructions. For example, if the user instructs the drone to "fly from this point to that point in the shortest distance," the flight path generation unit analyzes the instruction and calculates a route to reach the destination in the shortest distance while avoiding obstacles. The flight path generation unit also generates a flight path using a generation AI. For example, the generation AI receives the user's instructions as prompts and generates an optimal flight path. The obstacle detection unit detects obstacles during flight based on the flight path generated by the flight path generation unit. For example, the obstacle detection unit analyzes data from sensors mounted on the drone in real time to detect obstacles during flight. The obstacle detection unit also detects obstacles using the generation AI. For example, the generation AI identifies obstacles based on data from the sensors and instructs the drone to take evasive action. The flight status monitoring unit monitors the flight status based on obstacles detected by the obstacle detection unit and adjusts flight parameters as necessary. For example, if environmental conditions such as wind speed or temperature change, the flight status monitoring unit adjusts flight speed and altitude based on that information to maintain stable flight. Furthermore, if the remaining battery power is low, the flight status monitoring unit instructs the drone to head to the nearest charging station. This allows the drone flight system according to the embodiment to automate and optimize drone flight. For example, in the agricultural field, drone-based monitoring and management can be made more efficient, reducing work time and improving productivity. Furthermore, the system can be applied in a variety of fields, such as quickly assessing damage during disasters and inspecting infrastructure.

[0030] The flight path generation unit can learn the user's past flight history and propose a customized route based on the user's preferences. For example, the flight path generation unit uses a generation AI to analyze the user's past flight history and learn frequently used routes and preferred flight patterns. For example, for a user who prefers a particular landscape, the unit preferentially proposes routes that include that landscape. Furthermore, the flight path generation unit uses the generation AI to generate a customized route that reflects the user's individual flight style and preferences based on the user's flight history data. For example, for a user who prefers flying at low altitudes, the unit proposes a route that maintains a low altitude while avoiding obstacles. Furthermore, the flight path generation unit uses the generation AI to store the user's past flight history in the cloud, making it accessible from other devices. This provides a consistent customized route even when flying on different devices. This makes it possible to propose a customized route based on the user's preferences.

[0031] The flight path generation unit can acquire weather data in real time and generate a flight path optimized for weather conditions. In the flight path generation unit, for example, the generation AI uses a weather data API to acquire weather information in real time and optimize the flight path. For example, it automatically generates a route to avoid strong winds and rain. In addition, the flight path generation unit analyzes weather forecast data and proposes a route that responds to weather changes during flight. For example, it generates a detour route to avoid a predicted storm during flight. In addition, the flight path generation unit checks weather conditions before departure based on the user's flight plan and proposes the optimal departure time and route. For example, it instructs the user to start flight during a period of continuous clear weather. This makes it possible to generate a flight path optimized for weather conditions.

[0032] The flight path generation unit can refer to tourist destination guide information and automatically generate tourist routes for tourists. For example, the generation AI of the flight path generation unit retrieves tourist destination guide information from a database and generates the optimal tourist route for tourists. For example, it proposes a route that goes around major tourist spots. The flight path generation unit also takes into account the congestion status and business hours of the tourist destination and generates a route that allows tourists to tour efficiently. For example, it proposes time slots and routes that avoid congestion. The flight path generation unit also refers to information on the history and culture of the tourist destination and proposes routes that will be interesting to tourists. For example, it generates a route that goes around historical buildings and cultural events. This makes it possible to automatically generate tourist routes for tourists.

[0033] The flight route generation unit can generate optimal delivery routes according to the needs of the logistics industry. For example, the generation AI of the flight route generation unit analyzes delivery data from the logistics industry and generates optimal delivery routes. For example, it proposes a route that efficiently travels to multiple delivery destinations. The flight route generation unit also generates optimal delivery routes by acquiring traffic conditions and road congestion information in real time. For example, it proposes a detour route to avoid traffic jams. The flight route generation unit also generates optimal delivery routes by taking into account the priority of delivery destinations and time specifications. For example, it proposes a route that prioritizes delivery destinations with time specifications. This makes it possible to generate optimal delivery routes according to the needs of the logistics industry.

[0034] The obstacle detection unit can identify the type of obstacle and select the optimal avoidance action accordingly. For example, the generation AI analyzes data from the drone's camera and sensors to identify the type of obstacle. For example, it identifies different obstacles such as trees, buildings, and people. The generation AI then selects the optimal avoidance action depending on the type of obstacle. For example, it instructs the drone to climb if it is a tree, to detour if it is a building, and to stop if it is a person. The obstacle detection unit also uses a machine learning model to help the generation AI identify the type of obstacle, improving accuracy. For example, it can improve the accuracy of obstacle identification based on past data. This allows the generation AI to select the optimal avoidance action depending on the type of obstacle.

[0035] The obstacle detection unit works in cooperation with the air traffic management system to instruct the optimal evasive maneuver to avoid collisions with drones or flying objects. For example, the generation AI of the obstacle detection unit works in cooperation with the air traffic management system to obtain the positional information of other drones or flying objects in real time. For example, it grasps the flight paths of other drones. Furthermore, the generation AI of the obstacle detection unit instructs the optimal evasive maneuver to avoid collisions with other drones or flying objects. For example, if another drone is approaching, it instructs the drone to ascend or descend. Furthermore, the obstacle detection unit works in cooperation with the air traffic management system to allow the generation AI to adjust the flight paths of multiple drones to reduce the risk of collisions. For example, it changes the flight path or adjusts the flight speed. This allows the generation AI to instruct the optimal evasive maneuver to avoid collisions with other drones or flying objects.

[0036] The obstacle detection unit can be applied to obstacle detection and avoidance technology for underwater drones. For example, the generation AI analyzes data from the underwater drone's sensors to detect underwater obstacles. For example, it identifies obstacles such as rocks and seaweed. When the underwater drone approaches an obstacle, the generation AI instructs the drone to take the optimal avoidance action. For example, it changes the direction of travel to avoid the obstacle. The generation AI also uses a machine learning model to improve the underwater drone's obstacle detection and avoidance technology. For example, it increases the accuracy of obstacle detection based on past data. This can be applied to obstacle detection and avoidance technology for underwater drones.

[0037] The obstacle detection unit can be applied to obstacle detection and avoidance technology in autonomous driving systems for automobiles. For example, the generation AI analyzes data from the automobile's sensors to detect obstacles on the road. For example, it identifies other vehicles, pedestrians, and obstacles. When the automobile approaches an obstacle, the generation AI instructs the automobile to take the optimal avoidance action. For example, it changes the direction of travel to avoid the obstacle. The generation AI also uses a machine learning model to improve the obstacle detection and avoidance technology of the autonomous driving system. For example, it improves the accuracy of obstacle detection based on past data. This allows the technology to be applied to obstacle detection and avoidance technology in autonomous driving systems for automobiles.

[0038] The flight status monitoring unit can optimize energy consumption during flight and make adjustments to extend battery life. For example, the generating AI analyzes the drone's energy consumption data in real time to optimize energy consumption during flight. For example, it adjusts flight speed and altitude to reduce energy consumption. The generating AI also optimizes flight paths and patterns to extend battery life. For example, it selects an energy-efficient route. The generating AI also monitors battery status and makes adjustments to optimize energy consumption. For example, it instructs appropriate charging and discharging patterns to prevent battery degradation. This allows for optimization of energy consumption during flight and adjustments to extend battery life.

[0039] The flight status monitoring unit monitors the flight status of the marine drone and can make adjustments according to wave height and currents. For example, the generation AI analyzes data from the marine drone's sensors and monitors wave height and currents in real time. For example, if wave height exceeds a certain level, the flight altitude is adjusted. In addition, to optimize the flight status of the marine drone, the generation AI instructs a flight pattern according to wave height and currents. For example, it selects a route that minimizes the impact of waves. In addition, the flight status monitoring unit monitors the flight status of the marine drone and optimizes energy consumption according to wave height and currents. For example, it selects an altitude and speed that are less affected by waves. This allows the flight status of the marine drone to be monitored and adjustments to be made according to wave height and currents.

[0040] The flight status monitoring unit monitors the flight status of the space exploration drone and can make adjustments according to the space environment. For example, the generation AI analyzes data from the space exploration drone's sensors and monitors the space environment in real time. For example, it detects cosmic radiation and temperature changes. In addition, to optimize the flight status of the space exploration drone, the generation AI instructs a flight pattern according to the space environment. For example, it selects a route to avoid the effects of radiation. In addition, the flight status monitoring unit monitors the flight status of the space exploration drone and optimizes energy consumption according to the space environment. For example, it selects a flight altitude and speed to respond to temperature changes. This allows the flight status of the space exploration drone to be monitored and adjustments to be made according to the space environment.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The flight path generator can suggest flight paths based on specific themes based on the user's hobbies and interests. For example, if the user is interested in historical buildings, it can generate a route that takes them around those buildings. For a user who loves nature, it can suggest a route that allows users to enjoy natural scenery. Furthermore, the flight path generator can store data related to the user's hobbies and interests in the cloud and make it accessible from other devices. This allows the user to be provided with customized routes based on the user's hobbies and interests.

[0043] The flight path generation unit can learn the user's past travel history and suggest new places the user has not visited. For example, it can analyze data on places the user has visited in the past and suggest tourist spots that the user has not yet visited. The flight path generation unit can also store the user's travel history data in the cloud and make it accessible from other devices. This makes it possible to suggest new tourist spots based on the user's travel history.

[0044] The flight path generation unit can analyze the behavioral data of the user's pet and suggest a flight path that the user can enjoy together with the pet. For example, the flight path generation unit can analyze the pet's behavioral patterns and generate a path that includes the pet's favorite places. The flight path generation unit can also store the pet's behavioral data in the cloud and make it accessible from other devices. This makes it possible to provide a customized path that the user and pet can enjoy together.

[0045] The flight path generator can collect user feedback and improve the flight path based on the feedback. For example, the flight path generator can analyze the feedback provided by the user after a flight and reflect it in the next flight path. The flight path generator can also store the user feedback data in the cloud and make it accessible from other devices. This allows the flight path generator to provide a customized flight path based on the user feedback.

[0046] The flight path generation unit can analyze the user's exercise data and propose a flight path to enhance the exercise effect. For example, it can analyze the user's number of steps and calorie consumption and generate a route that will enhance the exercise effect. The flight path generation unit can also store the user's exercise data in the cloud and make it accessible from other devices. This allows the system to provide a customized route based on the user's exercise data.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The flight path generation unit generates a flight path based on instructions from the user. For example, if the user instructs, "I want you to fly from this point to that point in the shortest distance," the flight path generation unit analyzes the instruction and calculates a route that will reach the destination in the shortest distance while avoiding obstacles. The flight path generation unit also generates a flight path using a generation AI. For example, the generation AI receives the user's instructions as prompts and generates the optimal flight path. Step 2: The obstacle detection unit detects obstacles during flight based on the flight path generated by the flight path generation unit. For example, it analyzes data from sensors mounted on the drone in real time to detect obstacles during flight. The obstacle detection unit also detects obstacles using a generation AI. For example, the generation AI identifies obstacles based on data from the sensors and instructs the drone to take evasive action. Step 3: The flight status monitoring unit monitors the flight status based on the obstacles detected by the obstacle detection unit and adjusts flight parameters as necessary. For example, if environmental conditions such as wind speed or temperature change, the flight status monitoring unit adjusts flight speed and altitude based on that information to maintain stable flight. In addition, if the battery level becomes low, the flight status monitoring unit instructs the drone to head to the nearest charging station.

[0049] (Example 2) A drone flight system according to an embodiment of the present invention is a system that automates and optimizes drone flight using AI technology, thereby enabling drone flight to be performed more efficiently and safely.

[0050] A drone flight system according to an embodiment includes a flight path generation unit, an obstacle detection unit, and a flight status monitoring unit. The flight path generation unit generates a flight path based on user instructions. For example, if the user instructs the drone to "fly from this point to that point in the shortest distance," the flight path generation unit analyzes the instruction and calculates a route to reach the destination in the shortest distance while avoiding obstacles. The flight path generation unit also generates a flight path using a generation AI. For example, the generation AI receives the user's instructions as prompts and generates an optimal flight path. The obstacle detection unit detects obstacles during flight based on the flight path generated by the flight path generation unit. For example, the obstacle detection unit analyzes data from sensors mounted on the drone in real time to detect obstacles during flight. The obstacle detection unit also detects obstacles using the generation AI. For example, the generation AI identifies obstacles based on data from the sensors and instructs the drone to take evasive action. The flight status monitoring unit monitors the flight status based on obstacles detected by the obstacle detection unit and adjusts flight parameters as necessary. For example, if environmental conditions such as wind speed or temperature change, the flight status monitoring unit adjusts flight speed and altitude based on that information to maintain stable flight. Furthermore, if the remaining battery power is low, the flight status monitoring unit instructs the drone to head to the nearest charging station. This allows the drone flight system according to the embodiment to automate and optimize drone flight. For example, in the agricultural field, drone-based monitoring and management can be made more efficient, reducing work time and improving productivity. Furthermore, the system can be applied in a variety of fields, such as quickly assessing damage during disasters and inspecting infrastructure.

[0051] The flight path generation unit can learn the user's past flight history and propose a customized route based on the user's preferences. For example, the flight path generation unit uses a generation AI to analyze the user's past flight history and learn frequently used routes and preferred flight patterns. For example, for a user who prefers a particular landscape, the unit preferentially proposes routes that include that landscape. Furthermore, the flight path generation unit uses the generation AI to generate a customized route that reflects the user's individual flight style and preferences based on the user's flight history data. For example, for a user who prefers flying at low altitudes, the unit proposes a route that maintains a low altitude while avoiding obstacles. Furthermore, the flight path generation unit uses the generation AI to store the user's past flight history in the cloud, making it accessible from other devices. This provides a consistent customized route even when flying on different devices. This makes it possible to propose a customized route based on the user's preferences.

[0052] The flight path generation unit can acquire weather data in real time and generate a flight path optimized for weather conditions. In the flight path generation unit, for example, the generation AI uses a weather data API to acquire weather information in real time and optimize the flight path. For example, it automatically generates a route to avoid strong winds and rain. In addition, the flight path generation unit analyzes weather forecast data and proposes a route that responds to weather changes during flight. For example, it generates a detour route to avoid a predicted storm during flight. In addition, the flight path generation unit checks weather conditions before departure based on the user's flight plan and proposes the optimal departure time and route. For example, it instructs the user to start flight during a period of continuous clear weather. This makes it possible to generate a flight path optimized for weather conditions.

[0053] The flight path generation unit can use the emotion estimation function to analyze the user's emotional state and suggest a route that allows the user to enjoy relaxing scenery. The flight path generation unit, for example, uses the emotion estimation function to analyze the user's facial expressions and vocal tone to determine whether the user is relaxed. For example, if the user is relaxed, it can suggest a route that allows the user to enjoy natural scenery. The flight path generation unit can also monitor the user's emotional state in real time, and if the user is feeling stressed, it can generate a route that includes scenery that has a relaxing effect. For example, it can suggest a route that flies over a lake or a park. The flight path generation unit can also learn routes that the user found relaxing in the past based on the emotion estimation data and suggest a new route that includes similar scenery. For example, it can generate a route that revisits relaxation spots that the user has visited in the past. This makes it possible to suggest relaxing routes based on the user's emotional state.

[0054] The flight path generation unit can refer to tourist destination guide information and automatically generate tourist routes for tourists. For example, the generation AI of the flight path generation unit retrieves tourist destination guide information from a database and generates the optimal tourist route for tourists. For example, it proposes a route that goes around major tourist spots. The flight path generation unit also takes into account the congestion status and business hours of the tourist destination and generates a route that allows tourists to tour efficiently. For example, it proposes time slots and routes that avoid congestion. The flight path generation unit also refers to information on the history and culture of the tourist destination and proposes routes that will be interesting to tourists. For example, it generates a route that goes around historical buildings and cultural events. This makes it possible to automatically generate tourist routes for tourists.

[0055] The flight route generation unit can generate optimal delivery routes according to the needs of the logistics industry. For example, the generation AI of the flight route generation unit analyzes delivery data from the logistics industry and generates optimal delivery routes. For example, it proposes a route that efficiently travels to multiple delivery destinations. The flight route generation unit also generates optimal delivery routes by acquiring traffic conditions and road congestion information in real time. For example, it proposes a detour route to avoid traffic jams. The flight route generation unit also generates optimal delivery routes by taking into account the priority of delivery destinations and time specifications. For example, it proposes a route that prioritizes delivery destinations with time specifications. This makes it possible to generate optimal delivery routes according to the needs of the logistics industry.

[0056] The flight path generation unit uses the emotion estimation function to generate a flight path that does not cause stress to the user, thereby enhancing the relaxation effect. The flight path generation unit, for example, uses the emotion estimation function to monitor the user's stress level in real time and generate a flight path that does not cause stress. For example, it proposes a route that allows the user to enjoy natural scenery. The flight path generation unit also analyzes the user's emotional state and generates a flight path while providing music or narration that has a relaxing effect. For example, it proposes a flight path while playing relaxing music. The flight path generation unit also learns routes that the user found relaxing in the past based on the emotion estimation data and proposes a new route that includes similar scenery. For example, it generates a route that revisits a relaxation spot that the user has visited in the past. This allows the generation of a flight path that does not cause stress to the user, thereby enhancing the relaxation effect.

[0057] The obstacle detection unit can identify the type of obstacle and select the optimal avoidance action accordingly. For example, the generation AI analyzes data from the drone's camera and sensors to identify the type of obstacle. For example, it identifies different obstacles such as trees, buildings, and people. The generation AI then selects the optimal avoidance action depending on the type of obstacle. For example, it instructs the drone to climb if it is a tree, to detour if it is a building, and to stop if it is a person. The obstacle detection unit also uses a machine learning model to help the generation AI identify the type of obstacle, improving accuracy. For example, it can improve the accuracy of obstacle identification based on past data. This allows the generation AI to select the optimal avoidance action depending on the type of obstacle.

[0058] The obstacle detection unit works in cooperation with the air traffic management system to instruct the optimal evasive maneuver to avoid collisions with drones or flying objects. For example, the generation AI of the obstacle detection unit works in cooperation with the air traffic management system to obtain the positional information of other drones or flying objects in real time. For example, it grasps the flight paths of other drones. Furthermore, the generation AI of the obstacle detection unit instructs the optimal evasive maneuver to avoid collisions with other drones or flying objects. For example, if another drone is approaching, it instructs the drone to ascend or descend. Furthermore, the obstacle detection unit works in cooperation with the air traffic management system to allow the generation AI to adjust the flight paths of multiple drones to reduce the risk of collisions. For example, it changes the flight path or adjusts the flight speed. This allows the generation AI to instruct the optimal evasive maneuver to avoid collisions with other drones or flying objects.

[0059] The obstacle detection unit can be applied to obstacle detection and avoidance technology for underwater drones. For example, the generation AI analyzes data from the underwater drone's sensors to detect underwater obstacles. For example, it identifies obstacles such as rocks and seaweed. When the underwater drone approaches an obstacle, the generation AI instructs the drone to take the optimal avoidance action. For example, it changes the direction of travel to avoid the obstacle. The generation AI also uses a machine learning model to improve the underwater drone's obstacle detection and avoidance technology. For example, it increases the accuracy of obstacle detection based on past data. This can be applied to obstacle detection and avoidance technology for underwater drones.

[0060] The obstacle detection unit can be applied to obstacle detection and avoidance technology in autonomous driving systems for automobiles. For example, the generation AI analyzes data from the automobile's sensors to detect obstacles on the road. For example, it identifies other vehicles, pedestrians, and obstacles. When the automobile approaches an obstacle, the generation AI instructs the automobile to take the optimal avoidance action. For example, it changes the direction of travel to avoid the obstacle. The generation AI also uses a machine learning model to improve the obstacle detection and avoidance technology of the autonomous driving system. For example, it improves the accuracy of obstacle detection based on past data. This allows the technology to be applied to obstacle detection and avoidance technology in autonomous driving systems for automobiles.

[0061] The obstacle detection unit uses the emotion estimation function to generate an avoidance action that makes the user feel safe, thereby improving flight safety. The obstacle detection unit, for example, uses the emotion estimation function to generate an avoidance action that makes the user feel safe. For example, the obstacle detection unit analyzes the user's facial expression and tone of voice to determine whether the user feels safe. Furthermore, the obstacle detection unit uses a generation AI to instruct the optimal avoidance method to generate an avoidance action that makes the user feel safe. For example, the obstacle detection unit performs an avoidance action before approaching an obstacle. Furthermore, the obstacle detection unit learns avoidance actions that have made the user feel safe in the past based on the emotion estimation data and generates a similar avoidance action. For example, it generates a route that avoids places that the user has felt safe in the past. This allows the obstacle detection unit to generate an avoidance action that makes the user feel safe, thereby improving flight safety.

[0062] The flight status monitoring unit can optimize energy consumption during flight and make adjustments to extend battery life. For example, the generating AI analyzes the drone's energy consumption data in real time to optimize energy consumption during flight. For example, it adjusts flight speed and altitude to reduce energy consumption. The generating AI also optimizes flight paths and patterns to extend battery life. For example, it selects an energy-efficient route. The generating AI also monitors battery status and makes adjustments to optimize energy consumption. For example, it instructs appropriate charging and discharging patterns to prevent battery degradation. This allows for optimization of energy consumption during flight and adjustments to extend battery life.

[0063] The flight status monitoring unit can use the emotion estimation function to provide real-time feedback on the flight status so that the user can enjoy flying with peace of mind. For example, the flight status monitoring unit uses the emotion estimation function to monitor the user's emotional state in real time and provide feedback so that the user can enjoy flying with peace of mind. For example, if the user is feeling anxious, the flight status monitoring unit provides a detailed explanation of the flight status. Furthermore, the flight status monitoring unit adjusts the flight status using a generation AI according to the user's emotional state to provide a sense of security. For example, the flight speed is adjusted to maintain stable flight. Furthermore, the flight status monitoring unit customizes the feedback on the flight status based on the emotion estimation data so that the user can enjoy flying with peace of mind. For example, feedback tailored to the user's preferences is provided. This allows real-time feedback on the flight status to be provided so that the user can enjoy flying with peace of mind.

[0064] The flight status monitoring unit monitors the flight status of the marine drone and can make adjustments according to wave height and currents. For example, the generation AI analyzes data from the marine drone's sensors and monitors wave height and currents in real time. For example, if wave height exceeds a certain level, the flight altitude is adjusted. In addition, to optimize the flight status of the marine drone, the generation AI instructs a flight pattern according to wave height and currents. For example, it selects a route that minimizes the impact of waves. In addition, the flight status monitoring unit monitors the flight status of the marine drone and optimizes energy consumption according to wave height and currents. For example, it selects an altitude and speed that are less affected by waves. This allows the flight status of the marine drone to be monitored and adjustments to be made according to wave height and currents.

[0065] The flight status monitoring unit monitors the flight status of the space exploration drone and can make adjustments according to the space environment. For example, the generation AI analyzes data from the space exploration drone's sensors and monitors the space environment in real time. For example, it detects cosmic radiation and temperature changes. In addition, to optimize the flight status of the space exploration drone, the generation AI instructs a flight pattern according to the space environment. For example, it selects a route to avoid the effects of radiation. In addition, the flight status monitoring unit monitors the flight status of the space exploration drone and optimizes energy consumption according to the space environment. For example, it selects a flight altitude and speed to respond to temperature changes. This allows the flight status of the space exploration drone to be monitored and adjustments to be made according to the space environment.

[0066] The flight status monitoring unit uses the emotion estimation function to adjust the flight status so that the user does not feel stressed, thereby providing a comfortable flight experience. The flight status monitoring unit, for example, uses the emotion estimation function to monitor the user's stress level in real time and adjust the flight status. For example, if the user is feeling stressed, the flight speed is adjusted to maintain stable flight. The flight status monitoring unit also adjusts the flight status using a generation AI according to the user's emotional state, providing a comfortable flight experience. For example, the flight altitude is adjusted so that the user can enjoy the scenery. The flight status monitoring unit also customizes the flight status based on the emotion estimation data so that the user does not feel stressed. For example, it provides a flight pattern tailored to the user's preferences. This allows the flight status to be adjusted so that the user does not feel stressed, thereby providing a comfortable flight experience.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The flight path generation unit can monitor the user's health condition and suggest a flight path that takes health into consideration. For example, it can measure the user's heart rate and blood pressure with a sensor and generate a flight path that corresponds to the user's health condition. If the user's heart rate is high, it can suggest a route that includes relaxing scenery. The flight path generation unit can also store the user's health data in the cloud and analyze the user's health condition based on past data. This makes it possible to provide a customized route based on the user's health condition.

[0069] The flight path generator can suggest flight paths based on specific themes based on the user's hobbies and interests. For example, if the user is interested in historical buildings, it can generate a route that takes them around those buildings. For a user who loves nature, it can suggest a route that allows users to enjoy natural scenery. Furthermore, the flight path generator can store data related to the user's hobbies and interests in the cloud and make it accessible from other devices. This allows the user to be provided with customized routes based on the user's hobbies and interests.

[0070] The flight path generation unit can analyze the user's emotional state and generate a flight path while providing music and narration according to the user's emotions. For example, if the user is relaxed, the flight path generation unit can suggest a flight path while playing relaxing music. On the other hand, if the user is excited, the flight path generation unit can also suggest energetic music. Furthermore, the flight path generation unit can monitor the user's emotional state in real time and provide music and narration according to the user's emotions. This makes it possible to provide a customized flight path based on the user's emotional state.

[0071] The flight path generation unit can learn the user's past travel history and suggest new places the user has not visited. For example, it can analyze data on places the user has visited in the past and suggest tourist spots that the user has not yet visited. The flight path generation unit can also store the user's travel history data in the cloud and make it accessible from other devices. This makes it possible to suggest new tourist spots based on the user's travel history.

[0072] The flight path generation unit can use the emotion estimation function to suggest an adventure path that will excite the user. For example, it can analyze the user's facial expression and voice tone to determine whether the user is excited. For an excited user, it can suggest a path that includes adventurous scenery such as mountainous areas and valleys. The flight path generation unit can also monitor the user's emotional state in real time and generate a path that will maintain the user's excitement. This allows it to suggest an adventure path based on the user's emotional state.

[0073] The flight path generation unit can analyze the behavioral data of the user's pet and suggest a flight path that the user can enjoy together with the pet. For example, the flight path generation unit can analyze the pet's behavioral patterns and generate a path that includes the pet's favorite places. The flight path generation unit can also store the pet's behavioral data in the cloud and make it accessible from other devices. This makes it possible to provide a customized path that the user and pet can enjoy together.

[0074] The flight path generation unit can use the emotion estimation function to generate a flight path that allows the user to enjoy a safe flight. For example, it can analyze the user's facial expressions and voice tone to determine whether the user feels safe. For a user who feels safe, it can suggest a route that includes peaceful scenery. The flight path generation unit can also monitor the user's emotional state in real time and generate a route that maintains a sense of safety. This makes it possible to suggest a safe route based on the user's emotional state.

[0075] The flight path generator can collect user feedback and improve the flight path based on the feedback. For example, the flight path generator can analyze the feedback provided by the user after a flight and reflect it in the next flight path. The flight path generator can also store the user feedback data in the cloud and make it accessible from other devices. This allows the flight path generator to provide a customized flight path based on the user feedback.

[0076] The flight path generation unit can analyze the user's exercise data and propose a flight path to enhance the exercise effect. For example, it can analyze the user's number of steps and calorie consumption and generate a route that will enhance the exercise effect. The flight path generation unit can also store the user's exercise data in the cloud and make it accessible from other devices. This allows the system to provide a customized route based on the user's exercise data.

[0077] The flight path generation unit can use the emotion estimation function to generate a flight path while providing music and narration that will help the user relax. For example, it can analyze the user's facial expression and tone of voice to determine whether the user is relaxed. For a user who is relaxed, it can suggest a flight path while playing music that has a relaxing effect. The flight path generation unit can also monitor the user's emotional state in real time and generate a path to maintain the relaxation effect. This makes it possible to suggest a relaxing path based on the user's emotional state.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The flight path generation unit generates a flight path based on instructions from the user. For example, if the user instructs, "I want you to fly from this point to that point in the shortest distance," the flight path generation unit analyzes the instruction and calculates a route that will reach the destination in the shortest distance while avoiding obstacles. The flight path generation unit also generates a flight path using a generation AI. For example, the generation AI receives the user's instructions as prompts and generates the optimal flight path. Step 2: The obstacle detection unit detects obstacles during flight based on the flight path generated by the flight path generation unit. For example, it analyzes data from sensors mounted on the drone in real time to detect obstacles during flight. The obstacle detection unit also detects obstacles using a generation AI. For example, the generation AI identifies obstacles based on data from the sensors and instructs the drone to take evasive action. Step 3: The flight status monitoring unit monitors the flight status based on the obstacles detected by the obstacle detection unit and adjusts flight parameters as necessary. For example, if environmental conditions such as wind speed or temperature change, the flight status monitoring unit adjusts flight speed and altitude based on that information to maintain stable flight. In addition, if the battery level becomes low, the flight status monitoring unit instructs the drone to head to the nearest charging station.

[0080] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0082] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0086] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0087] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0088] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0089] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0090] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0091] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0093] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0094] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0121] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0130] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0131] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0132] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0133] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0134] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0135] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0136] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0137] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0138] 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.

[0139] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0140] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0141] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0142] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0143] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0144] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0145] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0146] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0147] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a flight path generation unit that generates a flight path based on an instruction from a user; an obstacle detection unit that detects obstacles during flight based on the flight path generated by the flight path generation unit; a flight status monitoring unit that monitors flight status based on the obstacles detected by the obstacle detection unit and adjusts flight parameters as necessary. A system characterized by:

2. The flight path generation unit Learns the user's past flight history and suggests customized routes based on the user's preferences 2. The system of claim 1.

3. The flight path generation unit Obtaining real-time weather data and generating flight paths optimized for weather conditions 2. The system of claim 1.

4. The flight path generation unit Analyzing the user's emotional state and suggesting a route that allows for relaxing scenery 2. The system of claim 1.

5. The flight path generation unit Automatically generate sightseeing routes for tourists by referencing tourist destination guide information 2. The system of claim 1.

6. The flight path generation unit Generate optimal delivery routes according to the needs of the logistics industry 2. The system of claim 1.

7. The flight path generation unit To generate a flight path that does not cause stress to the user, thereby enhancing the relaxation effect 2. The system of claim 1.

8. The obstacle detection unit Identifying the type of obstacle and selecting the optimal avoidance action accordingly 2. The system of claim 1.

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