system

Abstract

The system according to this embodiment aims to quickly and efficiently construct communication infrastructure in times of disaster or in undeveloped areas. [Solution] The system according to the embodiment comprises an acquisition unit, a generation unit, and a control unit. The acquisition unit acquires location information and environmental information for each drone. The generation unit generates an optimal network construction plan based on the information acquired by the acquisition unit. The control unit controls each drone based on the network construction plan generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to quickly and efficiently construct a communication infrastructure in the event of a disaster or in unopened areas.

[0005] The system according to the embodiment aims to quickly and efficiently construct a communication infrastructure in the event of a disaster or in unopened areas.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, a generation unit, and a control unit. The acquisition unit acquires location information and environmental information for each drone. The generation unit generates an optimal network construction plan based on the information acquired by the acquisition unit. The control unit controls each drone based on the network construction plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly and efficiently construct communication infrastructure in times of disaster or in undeveloped areas. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The network construction system according to an embodiment of the present invention is a system that autonomously constructs an efficient cellular network in disaster areas and undeveloped regions with minimal human intervention, using a diverse fleet of drones equipped with generational AI. This network construction system utilizes various types of drones, including flying, ground, water, underwater, underground, and animal-shaped drones, as well as large drones for laying wired networks, to provide a rapid and flexible network infrastructure. Autonomous network construction by a fleet of generational AI-equipped drones enables the rapid and efficient development of communication infrastructure with minimal human intervention. By combining diverse drones, it can adapt to all terrains and environmental conditions, significantly reducing costs and time compared to conventional methods. For example, in the event of a disaster, flying drones assess the situation in the affected area from the air, while ground-based drones lay the network on the ground. Water and underwater drones are responsible for network construction in aquatic areas, and underground drones lay the network underground. Animal-shaped drones support network construction under specific environmental conditions. This mechanism eliminates communication disparities in disaster areas and undeveloped regions, realizing a society where everyone is connected. Furthermore, innovation in autonomous network construction will promote safe and efficient infrastructure development, contributing to saving lives and the early resumption of economic activity. This will enable the network construction system to rapidly and efficiently develop communication infrastructure in times of disaster and in undeveloped areas.

[0029] The network construction system according to the embodiment comprises an acquisition unit, a generation unit, and a control unit. The acquisition unit acquires location information and environmental information for each drone. The acquisition unit acquires location information for each drone using, for example, GPS data. The acquisition unit can also acquire environmental information using temperature sensors and humidity sensors. For example, the acquisition unit acquires drone location information in real time and collects environmental information periodically. Furthermore, the acquisition unit can also acquire wind speed information using wind speed sensors. The generation unit uses a generation AI to generate an optimal network construction plan based on the information acquired by the acquisition unit. For example, the generation AI receives location information and environmental information for each drone as input and outputs an optimal network construction plan. The generation unit can also improve the accuracy of the plan by having the generation AI refer to past network construction data. For example, the generation AI generates a similar plan based on past successful cases. The control unit controls each drone based on the network construction plan generated by the generation unit. The control unit controls the flight path of each drone according to the generated plan. The control unit can also optimize the control method considering the remaining battery level of the drones. For example, the control unit shortens the flight path of a drone whose battery level is low. As a result, the network construction system according to the embodiment acquires location and environmental information of each drone, generates an optimal network construction plan, and controls each drone, enabling efficient network construction.

[0030] The data acquisition unit acquires location and environmental information for each drone. For example, the unit acquires location information for each drone using GPS data. Specifically, GPS modules mounted on each drone collect location information in real time and transmit it to a central database. This allows the system to always know the precise location of each drone. The data acquisition unit can also acquire environmental information using temperature and humidity sensors. For example, temperature sensors mounted on drones measure the ambient temperature, and humidity sensors detect the humidity in the air. This environmental information is important for understanding factors that affect drone flight. Furthermore, the data acquisition unit can acquire wind speed information using wind speed sensors. Acquiring wind speed information is extremely important because it directly affects the flight stability of the drone. The data acquisition unit integrates data from these sensors and provides environmental information that is updated in real time. This allows the system to always know the optimal conditions for drone flight and take appropriate action. Furthermore, the data acquisition unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, if the wind speed changes rapidly, the acquisition unit can increase the frequency of data collection from the wind speed sensor, providing more detailed information. This allows the acquisition unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The generation unit uses a generation AI to generate an optimal network construction plan based on the information acquired by the acquisition unit. For example, the generation unit receives location and environmental information for each drone as input and outputs an optimal network construction plan. Specifically, the generation AI receives information such as the current location, flight range, battery level, wind speed, temperature, and humidity of each drone as input, analyzes this data, and generates an optimal network construction plan. The generation AI can also improve the accuracy of the plan by referring to past network construction data. For example, it can incorporate past success and failure cases as training data and generate the optimal plan in similar situations. In addition, the generation AI can perform simulations and compare multiple plans to select the most efficient plan. The generation unit can update the generated plan in real time and respond flexibly to environmental changes and the state of the drones. For example, if the wind speed changes rapidly, the generation AI will regenerate the plan based on the new wind speed data and optimize the drone's flight path. In this way, the generation unit can always provide a highly accurate network construction plan based on the latest information and support efficient network construction. Furthermore, the generation unit can continuously improve the accuracy of the plans by receiving the execution results of the generated plans as feedback and utilizing them as training data for the generating AI.

[0032] The control unit controls each drone based on the network construction plan generated by the generation unit. For example, the control unit controls the flight path of each drone according to the generated plan. Specifically, the control unit monitors information such as the current position, flight speed, altitude, and battery level of each drone in real time and directs the optimal flight path based on the generated plan. The control unit can also optimize the control method considering the battery level of the drones. For example, it shortens the flight path of a drone with a low battery level and guides it to the nearest charging station. The control unit can also manage communication between drones and make adjustments to avoid collisions. For example, if multiple drones are flying in the same area, the control unit adjusts the flight path of each drone to minimize the risk of collision. Furthermore, the control unit can modify the flight path in real time in response to changes in environmental information. For example, if the wind speed changes rapidly, the control unit recalculates the flight path based on the new wind speed data to stabilize the drone's flight. This allows the control unit to efficiently and safely control each drone based on the generated plan, increasing the success rate of network construction. In addition, the control unit can record the drone flight data and use it for later analysis and improvement. This allows the control unit to continuously improve the overall system performance.

[0033] The generation unit generates network construction plans for use during disasters. For example, the generation unit's AI analyzes the situation during a disaster and generates an optimal network construction plan. The generation unit can also generate plans considering topographical information and the conditions of the affected area during a disaster. For example, the generation unit's AI analyzes aerial photographs of the affected area to identify locations suitable for network construction. Furthermore, the generation unit can predict communication demand during a disaster and generate plans based on that prediction. For example, the generation unit's AI predicts communication demand based on past disaster data and generates an optimal network construction plan. This makes it possible to quickly construct a network during a disaster.

[0034] The generation unit generates network construction plans for undeveloped areas. For example, the generation unit's AI analyzes the topographical information of the undeveloped area and generates an optimal network construction plan. The generation unit can also generate plans considering the environmental conditions of the undeveloped area. For example, the generation unit's AI analyzes weather data of the undeveloped area and identifies the best time for network construction. Furthermore, the generation unit can predict communication demand in the undeveloped area and generate a plan based on that. For example, the generation unit's AI predicts communication demand based on population data of the undeveloped area and generates an optimal network construction plan. This makes it possible to efficiently construct networks even in undeveloped areas.

[0035] The control unit controls flying drones, ground drones, surface drones, underwater drones, underground drones, and animal-shaped drones. For example, the control unit controls the flight paths of flying drones. The control unit can also control the movement paths of ground drones. For example, the control unit adjusts the path so that ground drones avoid obstacles. The control unit can also control the navigation paths of surface drones. For example, the control unit selects a path that minimizes the impact of waves for surface drones. Furthermore, the control unit can control the submersion paths of underwater drones. For example, the control unit selects a path that minimizes the impact of water currents for underwater drones. The control unit can also control the excavation paths of underground drones. For example, the control unit adjusts the excavation path so that underground drones adjust it according to geological conditions. The control unit can also control the movement paths of animal-shaped drones. For example, the control unit adjusts the path so that animal-shaped drones adapt to the natural environment. This makes it possible to build a network that can handle any terrain and environmental conditions by controlling a variety of drones. Some or all of the above-described processing in the control unit may be performed using AI, for example, or without using AI.

[0036] The acquisition unit adjusts the acquisition frequency when acquiring location and environmental information for each drone, taking into account the drone's battery level. For example, if the drone's battery level is low, the acquisition unit reduces the acquisition frequency to conserve battery power. If the battery level is sufficient, the acquisition unit can also increase the acquisition frequency to collect more detailed information. For example, if the battery level is moderate, the acquisition unit adjusts the acquisition frequency appropriately to maintain balance. This allows for efficient information acquisition by considering the drone's battery level. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0037] The acquisition unit acquires location and environmental information for each drone, and simultaneously acquires information to optimize the drone's flight path. For example, the acquisition unit acquires obstacle information along the drone's flight path and optimizes the path. The acquisition unit can also acquire weather information along the drone's flight path and adjust the path. For example, the acquisition unit acquires terrain information along the drone's flight path and plans the optimal path. By acquiring information to optimize the drone's flight path, efficient flight becomes possible. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0038] The acquisition unit monitors the status of the drone's sensors when acquiring location and environmental information for each drone, and issues an alert if an abnormality is detected. For example, if the drone's sensors detect abnormal data, the acquisition unit will immediately issue an alert. The acquisition unit can also activate a backup sensor and issue an alert if a sensor fails. For example, if a sensor overheats, the acquisition unit will take cooling measures and issue an alert. This allows for a quick response when an abnormality is detected by monitoring the status of the drone's sensors. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0039] The acquisition unit adjusts the type of information acquired when acquiring location and environmental information for each drone, taking into account the drone's communication status. For example, if the drone's communication status is good, the acquisition unit acquires detailed environmental information. If the communication status is unstable, the acquisition unit can also prioritize acquiring important location information. For example, if the communication status is poor, the acquisition unit acquires minimal information and acquires detailed information as soon as communication is restored. This makes it possible to acquire information efficiently by taking the drone's communication status into consideration. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0040] The acquisition unit acquires information about the flora and fauna surrounding each drone when acquiring location and environmental information for each drone. For example, the acquisition unit acquires the types and numbers of animals living around the drone. The acquisition unit can also acquire the types and conditions of plants growing around the drone. For example, the acquisition unit acquires ecosystem information of the surrounding flora and fauna and collects data for environmental protection. This makes it possible to collect data for environmental protection by acquiring information about the flora and fauna around the drone. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0041] The generation unit improves the accuracy of the plan by referring to past network construction data when generating the optimal network construction plan. For example, the generation unit can refer to past successful network construction plans and generate similar plans. The generation unit can also refer to past failed network construction plans and generate plans that avoid problems. For example, the generation unit analyzes past network construction data and generates the optimal plan. This improves the accuracy of the plan by referring to past network construction data. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI.

[0042] The generation unit optimizes the plan to minimize drone battery consumption when generating the optimal network construction plan. For example, the generation unit selects the shortest route to minimize drone battery consumption. The generation unit can also plan efficient flight patterns. For example, the generation unit minimizes battery consumption by including rest points. This minimizes drone battery consumption, enabling efficient network construction. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0043] The generation unit adjusts the plan content considering the drone's communication status when generating the optimal network construction plan. For example, if the drone's communication status is good, the generation unit generates a detailed plan. If the communication status is unstable, the generation unit can also generate a plan that prioritizes important tasks. For example, if the communication status is poor, the generation unit generates a plan that performs only the minimum necessary tasks. This makes it possible to build an efficient network by considering the drone's communication status. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0044] The generation unit generates an optimal network construction plan while considering information about the flora and fauna surrounding the drone. For example, the generation unit generates a plan by considering the movement patterns of animals living around the drone. The generation unit can also generate a plan by considering the growth status of plants growing in the surrounding area. For example, the generation unit generates a plan to protect the ecosystem of the surrounding flora and fauna. This makes it possible to construct an environmentally friendly network by considering information about the flora and fauna surrounding the drone. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0045] The control unit optimizes the control method when controlling each drone, taking into account the remaining battery level of the drone. For example, if the drone's battery level is low, the control unit adopts a control method that reduces power consumption. If the battery level is sufficient, the control unit can also adopt a more detailed control method. For example, if the battery level is moderate, the control unit adopts a balanced control method. This enables efficient drone control by considering the drone's battery level. Some or all of the above processing in the control unit may be performed using AI, for example, or without using AI.

[0046] The control unit optimizes the flight path of each drone in real time when controlling them. For example, the control unit acquires obstacle information along the drone's flight path in real time and optimizes the path. The control unit can also acquire weather information along the flight path in real time and adjust the path. For example, the control unit acquires terrain information along the flight path in real time and plans the optimal path. This enables efficient drone control by optimizing the drone's flight path in real time. Some or all of the above processing in the control unit may be performed using AI, for example, or without using AI.

[0047] The control unit adjusts the control method when controlling each drone, taking into account the drone's communication status. For example, if the drone's communication status is good, the control unit may adopt a detailed control method. If the communication status is unstable, the control unit may also adopt a control method that prioritizes important tasks. For example, if the communication status is poor, the control unit may adopt a control method that performs only the minimum necessary tasks. This allows for efficient drone control by taking into account the drone's communication status. Some or all of the above processing in the control unit may be performed using AI, for example, or without using AI.

[0048] The control unit adjusts the control method when controlling each drone, taking into account information about the flora and fauna surrounding the drone. For example, the control unit adjusts the control method by considering the movement patterns of animals living around the drone. The control unit can also adjust the control method by considering the growth status of plants growing in the surrounding area. For example, the control unit adopts a control method that protects the ecosystem of the surrounding flora and fauna. This makes it possible to control drones in an environmentally conscious manner by taking into account information about the flora and fauna surrounding the drone. Some or all of the above processing in the control unit may be performed using AI, for example, or without using AI.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The acquisition unit can also adjust the acquisition frequency when acquiring location and environmental information for each drone, taking into account the drone's battery level. For example, if the drone's battery level is low, the acquisition frequency can be reduced to conserve battery power. If the battery level is sufficient, the acquisition frequency can be increased to collect more detailed information. Furthermore, if the battery level is moderate, the acquisition frequency can be adjusted appropriately to maintain balance. This allows for efficient information acquisition by considering the drone's battery level. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0051] The generation unit can also improve the accuracy of the plan by referring to past network construction data when generating the optimal network construction plan. For example, it can refer to past successful network construction plans and generate a similar plan. It can also refer to past failed network construction plans and generate a plan that avoids the problems. Furthermore, it analyzes past network construction data and generates the optimal plan. In this way, the accuracy of the plan is improved by referring to past network construction data. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0052] The control unit can also optimize the control method when controlling each drone, taking into account the drone's battery level. For example, if the drone's battery level is low, it can adopt a control method that reduces power consumption. If the battery level is sufficient, it can adopt a more detailed control method. Furthermore, if the battery level is moderate, it can adopt a balanced control method. This allows for efficient drone control by considering the drone's battery level. Some or all of the above processing in the control unit may be performed using AI, for example, or without using AI.

[0053] The acquisition unit can also adjust the type of information acquired when acquiring location and environmental information for each drone, taking into account the drone's communication status. For example, if the drone's communication status is good, detailed environmental information can be acquired. If the communication status is unstable, important location information can be prioritized. Furthermore, if the communication status is poor, minimal information can be acquired, and detailed information can be acquired as soon as communication is restored. This makes it possible to acquire information efficiently by taking the drone's communication status into consideration. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0054] The control unit can also optimize the flight path of each drone in real time when controlling them. For example, it can acquire obstacle information along the drone's flight path in real time and optimize the path. It can also acquire weather information along the flight path in real time and adjust the path. Furthermore, it can acquire terrain information along the flight path in real time and plan the optimal path. By optimizing the drone's flight path in real time, efficient drone control becomes possible. Some or all of the above processing in the control unit may be performed using AI, for example, or without using AI.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The acquisition unit acquires location and environmental information for each drone. For example, it acquires location information for each drone using GPS data and environmental information using temperature and humidity sensors. Furthermore, it can also acquire wind speed information using a wind speed sensor. The acquisition unit acquires drone location information in real time and collects environmental information periodically. Step 2: The generation unit generates an optimal network construction plan based on the information acquired by the acquisition unit. The generation unit uses a generation AI to receive location information and environmental information for each drone as input and outputs an optimal network construction plan. The generation AI can also improve the accuracy of the plan by referring to past network construction data. For example, the generation AI can generate a similar plan based on past successful cases. Step 3: The control unit controls each drone based on the network construction plan generated by the generation unit. The control unit controls the flight path of each drone according to the generated plan and optimizes the control method considering the remaining battery level of the drones. For example, it shortens the flight path of a drone with a low battery level.

[0057] (Example of form 2) The network construction system according to an embodiment of the present invention is a system that autonomously constructs an efficient cellular network in disaster areas and undeveloped regions with minimal human intervention, using a diverse fleet of drones equipped with generational AI. This network construction system utilizes various types of drones, including flying, ground, water, underwater, underground, and animal-shaped drones, as well as large drones for laying wired networks, to provide a rapid and flexible network infrastructure. Autonomous network construction by a fleet of generational AI-equipped drones enables the rapid and efficient development of communication infrastructure with minimal human intervention. By combining diverse drones, it can adapt to all terrains and environmental conditions, significantly reducing costs and time compared to conventional methods. For example, in the event of a disaster, flying drones assess the situation in the affected area from the air, while ground-based drones lay the network on the ground. Water and underwater drones are responsible for network construction in aquatic areas, and underground drones lay the network underground. Animal-shaped drones support network construction under specific environmental conditions. This mechanism eliminates communication disparities in disaster areas and undeveloped regions, realizing a society where everyone is connected. Furthermore, innovation in autonomous network construction will promote safe and efficient infrastructure development, contributing to saving lives and the early resumption of economic activity. This will enable the network construction system to rapidly and efficiently develop communication infrastructure in times of disaster and in undeveloped areas.

[0058] The network construction system according to the embodiment comprises an acquisition unit, a generation unit, and a control unit. The acquisition unit acquires location information and environmental information for each drone. The acquisition unit acquires location information for each drone using, for example, GPS data. The acquisition unit can also acquire environmental information using temperature sensors and humidity sensors. For example, the acquisition unit acquires drone location information in real time and collects environmental information periodically. Furthermore, the acquisition unit can also acquire wind speed information using wind speed sensors. The generation unit uses a generation AI to generate an optimal network construction plan based on the information acquired by the acquisition unit. For example, the generation AI receives location information and environmental information for each drone as input and outputs an optimal network construction plan. The generation unit can also improve the accuracy of the plan by having the generation AI refer to past network construction data. For example, the generation AI generates a similar plan based on past successful cases. The control unit controls each drone based on the network construction plan generated by the generation unit. The control unit controls the flight path of each drone according to the generated plan. The control unit can also optimize the control method considering the remaining battery level of the drones. For example, the control unit shortens the flight path of a drone whose battery level is low. As a result, the network construction system according to the embodiment acquires location and environmental information of each drone, generates an optimal network construction plan, and controls each drone, enabling efficient network construction.

[0059] The data acquisition unit acquires location and environmental information for each drone. For example, the unit acquires location information for each drone using GPS data. Specifically, GPS modules mounted on each drone collect location information in real time and transmit it to a central database. This allows the system to always know the precise location of each drone. The data acquisition unit can also acquire environmental information using temperature and humidity sensors. For example, temperature sensors mounted on drones measure the ambient temperature, and humidity sensors detect the humidity in the air. This environmental information is important for understanding factors that affect drone flight. Furthermore, the data acquisition unit can acquire wind speed information using wind speed sensors. Acquiring wind speed information is extremely important because it directly affects the flight stability of the drone. The data acquisition unit integrates data from these sensors and provides environmental information that is updated in real time. This allows the system to always know the optimal conditions for drone flight and take appropriate action. Furthermore, the data acquisition unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, if the wind speed changes rapidly, the acquisition unit can increase the frequency of data collection from the wind speed sensor, providing more detailed information. This allows the acquisition unit to collect data efficiently and effectively, improving the overall system performance.

[0060] The generation unit uses a generation AI to generate an optimal network construction plan based on the information acquired by the acquisition unit. For example, the generation unit receives location and environmental information for each drone as input and outputs an optimal network construction plan. Specifically, the generation AI receives information such as the current location, flight range, battery level, wind speed, temperature, and humidity of each drone as input, analyzes this data, and generates an optimal network construction plan. The generation AI can also improve the accuracy of the plan by referring to past network construction data. For example, it can incorporate past success and failure cases as training data and generate the optimal plan in similar situations. In addition, the generation AI can perform simulations and compare multiple plans to select the most efficient plan. The generation unit can update the generated plan in real time and respond flexibly to environmental changes and the state of the drones. For example, if the wind speed changes rapidly, the generation AI will regenerate the plan based on the new wind speed data and optimize the drone's flight path. In this way, the generation unit can always provide a highly accurate network construction plan based on the latest information and support efficient network construction. Furthermore, the generation unit can continuously improve the accuracy of the plans by receiving the execution results of the generated plans as feedback and utilizing them as training data for the generating AI.

[0061] The control unit controls each drone based on the network construction plan generated by the generation unit. For example, the control unit controls the flight path of each drone according to the generated plan. Specifically, the control unit monitors information such as the current position, flight speed, altitude, and battery level of each drone in real time and directs the optimal flight path based on the generated plan. The control unit can also optimize the control method considering the battery level of the drones. For example, it shortens the flight path of a drone with a low battery level and guides it to the nearest charging station. The control unit can also manage communication between drones and make adjustments to avoid collisions. For example, if multiple drones are flying in the same area, the control unit adjusts the flight path of each drone to minimize the risk of collision. Furthermore, the control unit can modify the flight path in real time in response to changes in environmental information. For example, if the wind speed changes rapidly, the control unit recalculates the flight path based on the new wind speed data to stabilize the drone's flight. This allows the control unit to efficiently and safely control each drone based on the generated plan, increasing the success rate of network construction. In addition, the control unit can record the drone flight data and use it for later analysis and improvement. This allows the control unit to continuously improve the overall system performance.

[0062] The generation unit generates network construction plans for use during disasters. For example, the generation unit's AI analyzes the situation during a disaster and generates an optimal network construction plan. The generation unit can also generate plans considering topographical information and the conditions of the affected area during a disaster. For example, the generation unit's AI analyzes aerial photographs of the affected area to identify locations suitable for network construction. Furthermore, the generation unit can predict communication demand during a disaster and generate plans based on that prediction. For example, the generation unit's AI predicts communication demand based on past disaster data and generates an optimal network construction plan. This makes it possible to quickly construct a network during a disaster.

[0063] The generation unit generates network construction plans for undeveloped areas. For example, the generation unit's AI analyzes the topographical information of the undeveloped area and generates an optimal network construction plan. The generation unit can also generate plans considering the environmental conditions of the undeveloped area. For example, the generation unit's AI analyzes weather data of the undeveloped area and identifies the best time for network construction. Furthermore, the generation unit can predict communication demand in the undeveloped area and generate a plan based on that. For example, the generation unit's AI predicts communication demand based on population data of the undeveloped area and generates an optimal network construction plan. This makes it possible to efficiently construct networks even in undeveloped areas.

[0064] The control unit controls flying drones, ground drones, surface drones, underwater drones, underground drones, and animal-shaped drones. For example, the control unit controls the flight paths of flying drones. The control unit can also control the movement paths of ground drones. For example, the control unit adjusts the path so that ground drones avoid obstacles. The control unit can also control the navigation paths of surface drones. For example, the control unit selects a path that minimizes the impact of waves for surface drones. Furthermore, the control unit can control the submersion paths of underwater drones. For example, the control unit selects a path that minimizes the impact of water currents for underwater drones. The control unit can also control the excavation paths of underground drones. For example, the control unit adjusts the excavation path so that underground drones adjust it according to geological conditions. The control unit can also control the movement paths of animal-shaped drones. For example, the control unit adjusts the path so that animal-shaped drones adapt to the natural environment. This makes it possible to build a network that can handle any terrain and environmental conditions by controlling a variety of drones. Some or all of the above-described processing in the control unit may be performed using AI, for example, or without using AI.

[0065] The acquisition unit adjusts the acquisition frequency when acquiring location and environmental information for each drone, taking into account the drone's battery level. For example, if the drone's battery level is low, the acquisition unit reduces the acquisition frequency to conserve battery power. If the battery level is sufficient, the acquisition unit can also increase the acquisition frequency to collect more detailed information. For example, if the battery level is moderate, the acquisition unit adjusts the acquisition frequency appropriately to maintain balance. This allows for efficient information acquisition by considering the drone's battery level. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0066] The acquisition unit acquires location and environmental information for each drone, and simultaneously acquires information to optimize the drone's flight path. For example, the acquisition unit acquires obstacle information along the drone's flight path and optimizes the path. The acquisition unit can also acquire weather information along the drone's flight path and adjust the path. For example, the acquisition unit acquires terrain information along the drone's flight path and plans the optimal path. By acquiring information to optimize the drone's flight path, efficient flight becomes possible. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0067] The acquisition unit monitors the status of the drone's sensors when acquiring location and environmental information for each drone, and issues an alert if an abnormality is detected. For example, if the drone's sensors detect abnormal data, the acquisition unit will immediately issue an alert. The acquisition unit can also activate a backup sensor and issue an alert if a sensor fails. For example, if a sensor overheats, the acquisition unit will take cooling measures and issue an alert. This allows for a quick response when an abnormality is detected by monitoring the status of the drone's sensors. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0068] The acquisition unit estimates the emotion of each drone when acquiring location and environmental information, and determines the priority of information to acquire based on the estimated emotion of the drone. For example, if the drone is stressed, the acquisition unit prioritizes acquiring important information. If the drone is relaxed, the acquisition unit may also prioritize acquiring detailed information. For example, if the drone is tense, the acquisition unit prioritizes information that can be acquired quickly. This enables efficient information acquisition by determining the priority of information based on the emotion of the drone. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI.

[0069] The acquisition unit adjusts the type of information acquired when acquiring location and environmental information for each drone, taking into account the drone's communication status. For example, if the drone's communication status is good, the acquisition unit acquires detailed environmental information. If the communication status is unstable, the acquisition unit can also prioritize acquiring important location information. For example, if the communication status is poor, the acquisition unit acquires minimal information and acquires detailed information as soon as communication is restored. This makes it possible to acquire information efficiently by taking the drone's communication status into consideration. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0070] The acquisition unit acquires information about the flora and fauna surrounding each drone when acquiring location and environmental information for each drone. For example, the acquisition unit acquires the types and numbers of animals living around the drone. The acquisition unit can also acquire the types and conditions of plants growing around the drone. For example, the acquisition unit acquires ecosystem information of the surrounding flora and fauna and collects data for environmental protection. This makes it possible to collect data for environmental protection by acquiring information about the flora and fauna around the drone. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0071] The generation unit estimates the drone's emotions when generating an optimal network construction plan and adjusts the level of detail of the plan based on the estimated emotions. For example, if the drone is stressed, the generation unit generates a simple plan. If the drone is relaxed, the generation unit can also generate a detailed plan. For example, if the drone is tense, the generation unit generates a plan that can be executed quickly. This allows for efficient network construction by adjusting the level of detail of the plan based on the drone's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI.

[0072] The generation unit improves the accuracy of the plan by referring to past network construction data when generating the optimal network construction plan. For example, the generation unit can refer to past successful network construction plans and generate similar plans. The generation unit can also refer to past failed network construction plans and generate plans that avoid problems. For example, the generation unit analyzes past network construction data and generates the optimal plan. This improves the accuracy of the plan by referring to past network construction data. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI.

[0073] The generation unit optimizes the plan to minimize drone battery consumption when generating the optimal network construction plan. For example, the generation unit selects the shortest route to minimize drone battery consumption. The generation unit can also plan efficient flight patterns. For example, the generation unit minimizes battery consumption by including rest points. This minimizes drone battery consumption, enabling efficient network construction. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0074] The generation unit estimates the drone's emotions when generating an optimal network construction plan and prioritizes the plan based on the estimated emotions. For example, if the drone is stressed, the generation unit prioritizes important tasks. If the drone is relaxed, the generation unit may also prioritize detailed tasks. For example, if the drone is tense, the generation unit prioritizes tasks that can be executed quickly. This enables efficient network construction by prioritizing the plan based on the drone's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, or not using a generative AI.

[0075] The generation unit adjusts the plan content considering the drone's communication status when generating the optimal network construction plan. For example, if the drone's communication status is good, the generation unit generates a detailed plan. If the communication status is unstable, the generation unit can also generate a plan that prioritizes important tasks. For example, if the communication status is poor, the generation unit generates a plan that performs only the minimum necessary tasks. This makes it possible to build an efficient network by considering the drone's communication status. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0076] The generation unit generates an optimal network construction plan while considering information about the flora and fauna surrounding the drone. For example, the generation unit generates a plan by considering the movement patterns of animals living around the drone. The generation unit can also generate a plan by considering the growth status of plants growing in the surrounding area. For example, the generation unit generates a plan to protect the ecosystem of the surrounding flora and fauna. This makes it possible to construct an environmentally friendly network by considering information about the flora and fauna surrounding the drone. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0077] The control unit estimates the emotion of each drone when controlling it and adjusts the control method based on the estimated emotion. For example, if the drone is stressed, the control unit may employ a simple control method. If the drone is relaxed, the control unit may also employ a more detailed control method. For example, if the drone is tense, the control unit may employ a rapidly responsive control method. This allows for efficient drone control by adjusting the control method based on the drone's emotion. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not using AI.

[0078] The control unit optimizes the control method when controlling each drone, taking into account the remaining battery level of the drone. For example, if the drone's battery level is low, the control unit adopts a control method that reduces power consumption. If the battery level is sufficient, the control unit can also adopt a more detailed control method. For example, if the battery level is moderate, the control unit adopts a balanced control method. This enables efficient drone control by considering the drone's battery level. Some or all of the above processing in the control unit may be performed using AI, for example, or without using AI.

[0079] The control unit optimizes the flight path of each drone in real time when controlling them. For example, the control unit acquires obstacle information along the drone's flight path in real time and optimizes the path. The control unit can also acquire weather information along the flight path in real time and adjust the path. For example, the control unit acquires terrain information along the flight path in real time and plans the optimal path. This enables efficient drone control by optimizing the drone's flight path in real time. Some or all of the above processing in the control unit may be performed using AI, for example, or without using AI.

[0080] The control unit estimates the emotion of each drone when controlling it and determines control priorities based on the estimated emotion. For example, if a drone is stressed, the control unit prioritizes important tasks. If a drone is relaxed, the control unit may also prioritize detailed tasks. For example, if a drone is tense, the control unit prioritizes tasks that can be performed quickly. This enables efficient drone control by determining control priorities based on the drone's emotion. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not using AI.

[0081] The control unit adjusts the control method when controlling each drone, taking into account the drone's communication status. For example, if the drone's communication status is good, the control unit may adopt a detailed control method. If the communication status is unstable, the control unit may also adopt a control method that prioritizes important tasks. For example, if the communication status is poor, the control unit may adopt a control method that performs only the minimum necessary tasks. This allows for efficient drone control by taking into account the drone's communication status. Some or all of the above processing in the control unit may be performed using AI, for example, or without using AI.

[0082] The control unit adjusts the control method when controlling each drone, taking into account information about the flora and fauna surrounding the drone. For example, the control unit adjusts the control method by considering the movement patterns of animals living around the drone. The control unit can also adjust the control method by considering the growth status of plants growing in the surrounding area. For example, the control unit adopts a control method that protects the ecosystem of the surrounding flora and fauna. This makes it possible to control drones in an environmentally conscious manner by taking into account information about the flora and fauna surrounding the drone. Some or all of the above processing in the control unit may be performed using AI, for example, or without using AI.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] The acquisition unit can also estimate the emotions of each drone when acquiring location and environmental information, and determine the priority of information to acquire based on the estimated emotions of the drones. For example, if a drone is stressed, the acquisition of important information will be prioritized. If a drone is relaxed, the acquisition of detailed information may be prioritized. Furthermore, if a drone is tense, information that can be acquired quickly will be prioritized. This enables efficient information acquisition by prioritizing information based on the emotions of the drones. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0085] The generation unit can also estimate the drone's emotions when generating an optimal network construction plan and adjust the level of detail of the plan based on the estimated emotions. For example, if the drone is stressed, it can generate a simple plan. If the drone is relaxed, it can generate a detailed plan. Furthermore, if the drone is tense, it can generate a plan that can be executed quickly. This allows for efficient network construction by adjusting the level of detail of the plan based on the drone's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI.

[0086] The control unit can also estimate the emotions of each drone when controlling them and adjust the control method based on the estimated emotions. For example, if a drone is stressed, a simple control method may be adopted. If a drone is relaxed, a more detailed control method may be adopted. Furthermore, if a drone is tense, a quickly executable control method may be adopted. This allows for efficient drone control by adjusting the control method based on the drone's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI.

[0087] The control unit can also estimate the emotions of each drone when controlling them and determine control priorities based on the estimated emotions. For example, if a drone is stressed, it can prioritize important tasks. If a drone is relaxed, it can also prioritize detailed tasks. Furthermore, if a drone is tense, it can prioritize tasks that can be performed quickly. This enables efficient drone control by determining control priorities based on the drone's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI.

[0088] The generation unit can also estimate the emotions of the drones when generating an optimal network construction plan, and prioritize the plan based on the estimated emotions of the drones. For example, if a drone is stressed, important tasks will be prioritized. If a drone is relaxed, detailed tasks may be prioritized. Furthermore, if a drone is tense, tasks that can be performed quickly will be prioritized. This enables efficient network construction by prioritizing the plan based on the emotions of the drones. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI.

[0089] The acquisition unit can also adjust the acquisition frequency when acquiring location and environmental information for each drone, taking into account the drone's battery level. For example, if the drone's battery level is low, the acquisition frequency can be reduced to conserve battery power. If the battery level is sufficient, the acquisition frequency can be increased to collect more detailed information. Furthermore, if the battery level is moderate, the acquisition frequency can be adjusted appropriately to maintain balance. This allows for efficient information acquisition by considering the drone's battery level. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0090] The generation unit can also improve the accuracy of the plan by referring to past network construction data when generating the optimal network construction plan. For example, it can refer to past successful network construction plans and generate a similar plan. It can also refer to past failed network construction plans and generate a plan that avoids the problems. Furthermore, it analyzes past network construction data and generates the optimal plan. In this way, the accuracy of the plan is improved by referring to past network construction data. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0091] The control unit can also optimize the control method when controlling each drone, taking into account the drone's battery level. For example, if the drone's battery level is low, it can adopt a control method that reduces power consumption. If the battery level is sufficient, it can adopt a more detailed control method. Furthermore, if the battery level is moderate, it can adopt a balanced control method. This allows for efficient drone control by considering the drone's battery level. Some or all of the above processing in the control unit may be performed using AI, for example, or without using AI.

[0092] The acquisition unit can also adjust the type of information acquired when acquiring location and environmental information for each drone, taking into account the drone's communication status. For example, if the drone's communication status is good, detailed environmental information can be acquired. If the communication status is unstable, important location information can be prioritized. Furthermore, if the communication status is poor, minimal information can be acquired, and detailed information can be acquired as soon as communication is restored. This makes it possible to acquire information efficiently by taking the drone's communication status into consideration. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI.

[0093] The control unit can also optimize the flight path of each drone in real time when controlling them. For example, it can acquire obstacle information along the drone's flight path in real time and optimize the path. It can also acquire weather information along the flight path in real time and adjust the path. Furthermore, it can acquire terrain information along the flight path in real time and plan the optimal path. By optimizing the drone's flight path in real time, efficient drone control becomes possible. Some or all of the above processing in the control unit may be performed using AI, for example, or without using AI.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The acquisition unit acquires location and environmental information for each drone. For example, it acquires location information for each drone using GPS data and environmental information using temperature and humidity sensors. Furthermore, it can also acquire wind speed information using a wind speed sensor. The acquisition unit acquires drone location information in real time and collects environmental information periodically. Step 2: The generation unit generates an optimal network construction plan based on the information acquired by the acquisition unit. The generation unit uses a generation AI to receive location information and environmental information for each drone as input and outputs an optimal network construction plan. The generation AI can also improve the accuracy of the plan by referring to past network construction data. For example, the generation AI can generate a similar plan based on past successful cases. Step 3: The control unit controls each drone based on the network construction plan generated by the generation unit. The control unit controls the flight path of each drone according to the generated plan and optimizes the control method considering the remaining battery level of the drones. For example, it shortens the flight path of a drone with a low battery level.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0098] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0099] Each of the multiple elements described above, including the acquisition unit, generation unit, and control unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires location information and environmental information for each drone using the camera 42 and sensors of the smart device 14. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates an optimal network construction plan using generation AI. The control unit is implemented in the control unit 46A of the smart device 14 and controls each drone based on the generated plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0103] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0104] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0106] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0107] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0108] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0109] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0110] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0111] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0114] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the acquisition unit, generation unit, and control unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires location information and environmental information for each drone using the camera 42 and sensors of the smart glasses 214. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates an optimal network construction plan using generation AI. The control unit is implemented, for example, by the control unit 46A of the smart glasses 214, and controls each drone based on the generated plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements, including the acquisition unit, generation unit, and control unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires location information and environmental information for each drone using the camera 42 and sensors of the headset terminal 314. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates an optimal network construction plan using generation AI. The control unit is implemented in the control unit 46A of the headset terminal 314 and controls each drone based on the generated plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0140] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements, including the acquisition unit, generation unit, and control unit described above, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires location information and environmental information for each drone using the camera 42 and sensors of the robot 414. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates an optimal network construction plan using generated AI. The control unit is implemented, for example, by the control unit 46A of the robot 414, and controls each drone based on the generated plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0149] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0151] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0152] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0153] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0157] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0158] 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.

[0159] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0160] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0161] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0162] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0164] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0165] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0166] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0167] (Note 1) An acquisition unit that acquires location information and environmental information for each drone, A generation unit generates an optimal network construction plan based on the information acquired by the acquisition unit, The system includes a control unit that controls each drone based on the network construction plan generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Generate a network construction plan for use during disasters. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate network construction plans for undeveloped areas. The system described in Appendix 1, characterized by the features described herein. (Note 4) The control unit, Controlling flying drones, ground drones, surface drones, underwater drones, underground drones, and animal-shaped drones. The system described in Appendix 1, characterized by the features described herein. (Note 5) The acquisition unit is, When acquiring location and environmental information for each drone, the acquisition frequency is adjusted considering the drone's battery level. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, When acquiring location and environmental information for each drone, information to optimize the drone's flight path is also acquired simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, When acquiring location and environmental information for each drone, the system monitors the status of the drone's sensors and issues an alert if an anomaly is detected. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When acquiring location and environmental information for each drone, the system estimates the drone's emotions and prioritizes the information to be acquired based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring location and environmental information for each drone, the type of information acquired is adjusted considering the drone's communication status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When acquiring location and environmental information for each drone, information on the flora and fauna surrounding the drone is also acquired simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating the optimal network construction plan, the system estimates the drones' emotions and adjusts the level of detail in the plan based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating the optimal network construction plan, we refer to past network construction data to improve the accuracy of the plan. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating the optimal network configuration plan, the plan is optimized to minimize drone battery consumption. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating the optimal network construction plan, the system estimates the emotions of the drones and prioritizes the plan based on the estimated emotions of the drones. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating the optimal network construction plan, the plan's content is adjusted to take into account the drone's communication conditions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating the optimal network construction plan, the plan is generated while taking into account information about flora and fauna surrounding the drone. The system described in Appendix 1, characterized by the features described herein. (Note 17) The control unit, When controlling each drone, the system estimates the drone's emotions and adjusts the control method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The control unit, When controlling each drone, the control method is optimized taking into account the drone's battery level. The system described in Appendix 1, characterized by the features described herein. (Note 19) The control unit, When controlling each drone, the flight path of each drone is optimized in real time. The system described in Appendix 1, characterized by the features described herein. (Note 20) The control unit, When controlling each drone, the system estimates the drone's emotions and determines control priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The control unit, When controlling each drone, the control method is adjusted considering the drone's communication status. The system described in Appendix 1, characterized by the features described herein. (Note 22) The control unit, When controlling each drone, the control method is adjusted by taking into account information about the flora and fauna surrounding the drone. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires location information and environmental information for each drone, A generation unit generates an optimal network construction plan based on the information acquired by the acquisition unit, The system includes a control unit that controls each drone based on the network construction plan generated by the generation unit. A system characterized by the following features.

2. The generating unit is Generate a network construction plan for use during disasters. The system according to feature 1.

3. The generating unit is Generate network construction plans for undeveloped areas. The system according to feature 1.

4. The control unit, Controlling flying drones, ground drones, surface drones, underwater drones, underground drones, and animal-shaped drones. The system according to feature 1.

5. The acquisition unit is, When acquiring location and environmental information for each drone, the acquisition frequency is adjusted considering the drone's battery level. The system according to feature 1.

6. The acquisition unit is, When acquiring location and environmental information for each drone, information to optimize the drone's flight path is also acquired simultaneously. The system according to feature 1.

7. The acquisition unit is, When acquiring location and environmental information for each drone, the system monitors the status of the drone's sensors and issues an alert if an anomaly is detected. The system according to feature 1.

8. The acquisition unit is, When acquiring location and environmental information for each drone, the system estimates the drone's emotions and prioritizes the information to be acquired based on the estimated emotions. The system according to feature 1.

9. The acquisition unit is, When acquiring location and environmental information for each drone, the type of information acquired is adjusted considering the drone's communication status. The system according to feature 1.

10. The acquisition unit is, When acquiring location and environmental information for each drone, information on the flora and fauna surrounding the drone is also acquired simultaneously. The system according to feature 1.

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