system

Abstract

The system according to this embodiment aims to promote the widespread adoption and development of drone technology. [Solution] The system according to the embodiment comprises a provisioning unit, a strategy planning unit, and a piloting unit. The provisioning unit provides a platform for battles between drones piloted by AI agents. The strategy planning unit formulates strategies using generated AI on the platform provided by the provisioning unit. The piloting unit pilots the drones in real time based on the strategies formulated by the strategy planning 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 is a problem that the investment in the utilization and development of drones has a decreasing trend, and there are limitations in the popularization and development of drone technology.

[0005] The system according to the embodiment aims to promote the popularization and development of drone technology.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a provisioning unit, a strategy planning unit, and a piloting unit. The provisioning unit provides a platform for battles between drones piloted by AI agents. The strategy planning unit formulates strategies using generated AI on the platform provided by the provisioning unit. The piloting unit pilots the drones in real time based on the strategies formulated by the strategy planning unit. [Effects of the Invention]

[0007] The system according to this embodiment can promote the widespread adoption and development of drone technology. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 reception 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 reception 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 drone fight arena system according to an embodiment of the present invention is a system that provides drone battles as entertainment by utilizing generative AI. This system provides a platform for drone battles piloted by AI agents, and by using generative AI to formulate strategies and control the drones in real time, it can enhance technological development and the sense of realism for spectators, and also function as a technology trade show. For example, it provides a platform for drone battles piloted by AI agents. This platform incorporates elements of entertainment, a technology laboratory, and a technology trade show. Spectators can watch drone battles, and technology developers can test new technologies. In addition, sponsoring companies can promote their technologies through this platform. Strategies are formulated using generative AI, and drones are controlled in real time. The generative AI analyzes the movements of the drones and formulates the optimal strategy. For example, it determines the timing of attacks and defenses by considering the drone's position and speed, and the opponent's movements. The generative AI controls the drones in real time and can modify the strategy according to the battle situation. This can improve the drone's control performance. Furthermore, this platform aims to build a business ecosystem involving spectators, technology developers, and sponsors. Spectators can enjoy drone battles, and technology developers can test new technologies. Sponsoring companies can showcase their technologies. This aims to create a better society through the development and social implementation of drone technology. For example, spectators can watch drone battles through an online platform. Technology developers can learn drone piloting techniques and strategies in real time, accelerating technological advancements. Sponsoring companies can showcase their technologies through drone battle events and contribute to building a business ecosystem. In this way, the Drone Fight Arena system is a platform that provides drone battles as entertainment using generative AI, enhancing technology development and audience engagement, and functioning as a technology trade show.Furthermore, the aim is to build a business ecosystem that involves spectators, technology developers, and sponsors. This will allow the drone fight arena system to provide drone battles as entertainment, enhance technology development, and increase the sense of immersion for spectators.

[0029] The drone fight arena system according to this embodiment comprises a provisioning unit, a strategy planning unit, and a piloting unit. The provisioning unit provides a platform for battles between drones piloted by AI agents. The provisioning unit provides a platform that incorporates elements such as entertainment, a technology laboratory, and a technology trade show. The provisioning unit can provide a platform where spectators can watch drone battles and technology developers can test new technologies. The provisioning unit can also provide a platform where sponsoring companies can promote their technologies. For example, the provisioning unit enables spectators to watch drone battles through an online platform. The provisioning unit enables technology developers to test new technologies. The provisioning unit also enables sponsoring companies to promote their technologies. The strategy planning unit uses generative AI to formulate strategies on the platform provided by the provisioning unit. The strategy planning unit analyzes the position and speed of the drones and the movements of the opponents, for example, and formulates the optimal strategy. The strategy planning unit can use generative AI to analyze the movements of the drones and formulate the optimal strategy. For example, the strategy planning unit determines the timing of attacks and defenses by considering the drone's position and speed, as well as the opponent's movements. The strategy planning unit can also modify the strategy in real time using generative AI. For example, the strategy planning unit modifies the strategy according to the battle situation. The piloting unit controls the drone in real time based on the strategy planned by the strategy planning unit. The piloting unit controls the drone in real time using, for example, generative AI. The piloting unit can modify the strategy according to the battle situation using generative AI. For example, the piloting unit determines the timing of attacks and defenses by considering the drone's position and speed, as well as the opponent's movements. The piloting unit controls the drone in real time using generative AI and can modify the strategy according to the battle situation. As a result, the drone fight arena system according to this embodiment can provide drone battles as entertainment, enhance technological development, and increase the sense of realism for the audience.

[0030] The provider will provide a platform for drone battles controlled by AI agents. The provider will offer a platform that incorporates elements of entertainment, technology experimentation, and technology showrooms. Specifically, the provider can provide a platform where spectators can watch drone battles and technology developers can test new technologies. Spectators can watch drone battles in real time through the online platform, gaining detailed insights into the progress of the battles and the movements of the drones. Furthermore, the provider can also provide a platform where sponsoring companies can showcase their technologies. Sponsoring companies can equip drones with their technologies and demonstrate their performance through the battles. For example, the provider will enable spectators to watch drone battles via the online platform. Spectators can watch live streams of the battles and enjoy the drone movements and strategies in real time. The provider will also enable technology developers to test new technologies. Technology developers can equip drones with new sensors and algorithms and evaluate their performance through the battles. Furthermore, the provider will enable sponsoring companies to showcase their technologies. Sponsoring companies can equip drones with their own technology and demonstrate its performance through battles. This allows the provider to offer a platform that combines entertainment and technological development, creating an attractive environment for audiences, technology developers, and sponsoring companies.

[0031] The Strategy Planning Department uses generative AI to formulate strategies on the platform provided by the Service Provider Department. For example, the Strategy Planning Department analyzes the drone's position and speed, as well as the opponent's movements, to formulate the optimal strategy. Specifically, the Strategy Planning Department can use generative AI to analyze the drone's movements and formulate the optimal strategy. The generative AI analyzes the drone's position information, speed data, and the opponent's movement patterns in real time to determine the timing of attacks and defenses. For example, the Strategy Planning Department considers the drone's position and speed, as well as the opponent's movements, to determine the timing of attacks and defenses. The generative AI can learn from past battle data and derive the optimal strategy. Furthermore, the Strategy Planning Department can also use generative AI to modify strategies in real time. For example, the Strategy Planning Department modifies strategies according to the battle situation. The generative AI can analyze the progress of the battle and changes in the opponent's movements in real time to derive the optimal strategy. As a result, the Strategy Planning Department can optimize the drone's movements and formulate strategies for victory in battle. Furthermore, the Strategic Planning Department can use generative AI to simulate multiple scenarios and select the most effective strategy. This allows the Strategic Planning Department to demonstrate a high level of strategic thinking in drone battles and provide the optimal strategy for victory.

[0032] The piloting unit controls the drone in real time based on the strategy formulated by the strategic planning unit. The piloting unit uses, for example, generative AI to control the drone in real time. Specifically, the piloting unit can use generative AI to modify the strategy according to the battle situation. The generative AI analyzes the drone's position and speed data, as well as the opponent's movement patterns, in real time to generate optimal control instructions. For example, the piloting unit determines the timing of attacks and defenses by considering the drone's position and speed, and the opponent's movements. The generative AI can learn from past battle data to derive optimal control instructions. Furthermore, the piloting unit can use generative AI to control the drone in real time and modify the strategy according to the battle situation. For example, the piloting unit determines the timing of attacks and defenses by considering the drone's position and speed, and the opponent's movements. The generative AI can analyze the progress of the battle and changes in the opponent's movements in real time to derive optimal control instructions. This allows the piloting unit to optimize the drone's movements and control it towards victory. Additionally, the piloting unit can use generative AI to simulate multiple scenarios and select the most effective control instructions. This allows the control unit to demonstrate high piloting skills in drone battles and provide optimal maneuvers for victory.

[0033] The Strategic Planning Department can analyze the drone's position and speed, as well as the opponent's movements, using generative AI to formulate appropriate strategies. For example, the Strategic Planning Department uses generative AI to analyze the drone's position and speed, as well as the opponent's movements. The Strategic Planning Department can then formulate the optimal strategy. For instance, the Strategic Planning Department determines the timing of attacks and defenses by considering the drone's position and speed, as well as the opponent's movements. The Strategic Planning Department can also modify strategies in real time using generative AI. For example, the Strategic Planning Department modifies strategies according to the battle situation. This demonstrates that by using generative AI, the Strategic Planning Department can analyze the drone's position and speed, as well as the opponent's movements, and formulate the optimal strategy.

[0034] The control unit uses generative AI to operate the drone in real time and can modify its strategy according to the battle situation. For example, the control unit uses generative AI to operate the drone in real time. The control unit uses generative AI to modify its strategy according to the battle situation. For example, the control unit considers the drone's position and speed, as well as the opponent's movements, to determine the timing of attacks and defenses. The control unit uses generative AI to operate the drone in real time and can modify its strategy according to the battle situation. This allows the control unit to operate the drone in real time using generative AI and modify its strategy according to the battle situation.

[0035] The service provider can enable spectators to watch the drone battles through an online platform. The service provider can enable spectators to watch the drone battles through an online platform. The service provider can enable spectators to watch the drone battles through an online platform. For example, the service provider can enable spectators to watch the drone battles through an online platform. This allows spectators to watch the drone battles through an online platform. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0036] The provisioning unit can enable technology developers to try out new technologies. The provisioning unit can enable technology developers to try out new technologies. The provisioning unit can enable technology developers to try out new technologies. For example, the provisioning unit can enable technology developers to try out new technologies. This allows technology developers to try out new technologies. Some or all of the above-described processing in the provisioning unit may be performed using AI, for example, or without using AI.

[0037] The provisioning section can enable sponsoring companies to promote their technology. The provisioning section can enable sponsoring companies to promote their technology. The provisioning section can enable sponsoring companies to promote their technology. For example, the provisioning section can enable sponsoring companies to promote their technology. This allows sponsoring companies to promote their technology. Some or all of the processing described above in the provisioning section may be performed using AI, for example, or without using AI.

[0038] The service provider can analyze the technology developer's past experimental data and provide an optimal technology testing environment. For example, the service provider can propose an optimal testing environment based on the success rate of experiments conducted by the technology developer in the past. The service provider can reproduce a testing environment under specific conditions from the technology developer's past experimental data. The service provider can also analyze the technology developer's past experimental data and propose improvements to the testing environment. In this way, by analyzing the technology developer's past experimental data, an optimal technology testing environment can be provided. Some or all of the above processes in the service provider may be performed using AI, for example, or without using AI.

[0039] The service provider can customize the timing and content of ad displays based on the sponsor company's marketing strategy. For example, the service provider can adjust the timing of ad displays to match the sponsor company's target audience. The service provider can customize the content of ads based on the sponsor company's marketing strategy. The service provider can also analyze the sponsor company's past advertising effectiveness and propose the optimal ad display method. This allows for the customization of ad display timing and content based on the sponsor company's marketing strategy. Some or all of the above processes performed by the service provider may be carried out using AI, for example, or not using AI.

[0040] The service provider can provide a region-specific technology testing environment, taking into account the geographical location of the technology developer. For example, the service provider can provide a test environment that replicates region-specific climate conditions based on the location of the technology developer. The service provider can provide a test environment that takes into account the region's infrastructure and topography, based on the geographical location of the technology developer. The service provider can also provide a test environment that takes into account the region's laws, regulations, and safety standards, based on the location of the technology developer. In this way, by taking into account the geographical location of the technology developer, a region-specific technology testing environment can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0041] The service provider can analyze the social media activities of sponsoring companies and display relevant advertisements. For example, the service provider can display relevant advertisements based on popular posts on the sponsoring companies' social media. The service provider can analyze the social media activities of sponsoring companies and display advertisements tailored to the target audience. The service provider can also display relevant advertisements based on campaign information on the sponsoring companies' social media. In this way, relevant advertisements can be displayed by analyzing the social media activities of sponsoring companies. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0042] The Strategy Planning Department can predict the opponent's strategic patterns by referring to past battle data and formulate optimal countermeasures. For example, the Strategy Planning Department can predict the opponent's attack patterns from past battle data and formulate optimal defensive strategies. The Strategy Planning Department can analyze past battle data to find the opponent's weaknesses and formulate offensive strategies. The Strategy Planning Department can also predict the timing of the opponent's strategic changes based on past battle data and formulate countermeasures. In this way, by referring to past battle data, it is possible to predict the opponent's strategic patterns and formulate optimal countermeasures. Some or all of the above processes in the Strategy Planning Department may be performed using, for example, a generative AI, or without using a generative AI.

[0043] The Strategy Planning Unit can adjust the duration of the strategy based on the drone's battery level and aircraft status. For example, the Strategy Planning Unit can adjust the duration of the strategy based on the drone's battery level. The Strategy Planning Unit can adjust the duration of the strategy considering the drone's aircraft status. The Strategy Planning Unit can also comprehensively judge the drone's battery level and aircraft status and set the optimal duration of the strategy. This allows the duration of the strategy to be adjusted by considering the drone's battery level and aircraft status. Some or all of the above processing in the Strategy Planning Unit may be performed using, for example, generative AI, or without using generative AI.

[0044] The Strategic Planning Department can formulate strategies tailored to the terrain based on the drone's geographical location information. For example, the Strategic Planning Department can formulate strategies that take advantage of the terrain's characteristics based on the drone's geographical location information. The Strategic Planning Department can formulate strategies to avoid obstacles, taking into account the drone's geographical location information. The Strategic Planning Department can also formulate strategies that maximize the advantages of the terrain, taking into account the drone's geographical location information. In this way, by considering the drone's geographical location information, strategies tailored to the terrain can be formulated. Some or all of the above processes in the Strategic Planning Department may be performed using, for example, generative AI, or without using generative AI.

[0045] The Strategic Planning Department can formulate strategies based on the aircraft's durability, using the drone's past maintenance history. For example, the Strategic Planning Department can formulate strategies that take the aircraft's durability into consideration, based on the drone's past maintenance history. The Strategic Planning Department can formulate strategies to compensate for the aircraft's weaknesses by referring to the drone's maintenance history. The Strategic Planning Department can also formulate strategies that maximize the aircraft's durability, based on the drone's maintenance history. In this way, strategies based on the aircraft's durability can be formulated by referring to the drone's past maintenance history. Some or all of the above processes in the Strategic Planning Department may be performed, for example, using generative AI, or without using generative AI.

[0046] The control unit can analyze the drone's real-time data and make appropriate adjustments to its operation. For example, the control unit can optimize the operation based on the drone's real-time data. The control unit can analyze the drone's real-time data and suggest areas for improvement in operation. The control unit can also maximize the efficiency of operation based on the drone's real-time data. In this way, operation can be optimized by analyzing the drone's real-time data. Some or all of the above-described processes in the control unit may be performed using, for example, generative AI, or without using generative AI.

[0047] The control unit can improve the accuracy of obstacle avoidance by integrating the drone's sensor information. For example, the control unit can improve the accuracy of obstacle avoidance by integrating the drone's sensor information. Based on the drone's sensor information, the control unit can propose the optimal route for obstacle avoidance. The control unit can also analyze the drone's sensor information and propose areas for improvement in obstacle avoidance. In this way, the accuracy of obstacle avoidance can be improved by integrating the drone's sensor information. Some or all of the above processing in the control unit may be performed using, for example, generative AI, or without using generative AI.

[0048] The control unit can apply a control method appropriate to the terrain based on the drone's geographical location information. For example, the control unit can apply a control method that takes advantage of the terrain's characteristics based on the drone's geographical location information. The control unit can apply a control method that avoids obstacles, taking into account the drone's geographical location information. The control unit can also apply a control method that maximizes the advantages of the terrain, based on the drone's geographical location information. In this way, by taking into account the drone's geographical location information, a control method appropriate to the terrain can be applied. Some or all of the above processing in the control unit may be performed using, for example, generative AI, or without using generative AI.

[0049] The control unit can monitor the drone's communication status and adjust the control method based on the stability of the communication. For example, the control unit can monitor the drone's communication status and adjust the control method if the communication is unstable. Based on the drone's communication status, the control unit can apply the optimal control method when the communication is stable. The control unit can also analyze the drone's communication status and propose a control method that maximizes communication stability. In this way, by monitoring the drone's communication status, the control method can be adjusted based on the stability of the communication. Some or all of the above processing in the control unit may be performed using, for example, generative AI, or without using generative AI.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The strategic planning unit can monitor the drone's battery level in real time and adjust the strategy according to the battery's depletion. For example, if the battery level is low, the strategic planning unit can adopt a defensive strategy to conserve battery power. If the battery level is sufficient, the strategic planning unit can adopt an offensive strategy and actively attack the opponent. If the battery level is moderate, the strategic planning unit can adopt a balanced strategy to balance battery consumption with offensive capabilities. This allows the system to formulate the optimal strategy based on the drone's battery level. Some or all of the above-described processes in the strategic planning unit may be performed using, for example, generative AI, or without using generative AI.

[0052] The control unit can improve the accuracy of obstacle avoidance based on the drone's sensor information. For example, if there is an obstacle in front of the drone, the control unit can calculate the optimal avoidance route based on the sensor information and safely avoid the obstacle. The control unit can also monitor the drone's surroundings in real time and take immediate avoidance action when an obstacle approaches. Furthermore, the control unit can analyze the drone's sensor information, learn obstacle avoidance patterns, and perform avoidance actions more efficiently in the future. This improves the accuracy of the drone's obstacle avoidance. Some or all of the above processing in the control unit may be performed using, for example, generative AI, or without using generative AI.

[0053] The Strategic Planning Department can analyze past drone battle data and predict the opponent's strategic patterns. For example, it can predict the opponent's attack patterns from past battle data and formulate the optimal defensive strategy. It can also identify the opponent's weaknesses and formulate an offensive strategy. Furthermore, it can predict the timing of the opponent's strategic changes and formulate countermeasures. In this way, by referring to past battle data, it is possible to predict the opponent's strategic patterns and formulate the optimal countermeasures. Some or all of the above processing in the Strategic Planning Department may be performed using, for example, generative AI, or without using generative AI.

[0054] The service provider can analyze the technology developer's past experimental data and provide an optimal technology testing environment. For example, it can propose an optimal testing environment based on the success rate of experiments conducted by the technology developer in the past. It can also reproduce a testing environment under specific conditions. Furthermore, it can analyze past experimental data and suggest areas for improvement in the testing environment. In this way, by analyzing the technology developer's past experimental data, an optimal technology testing environment can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0055] The control unit can analyze real-time data from the drone and make appropriate adjustments to its controls. For example, it can optimize controls based on the drone's real-time data. It can also analyze real-time data and suggest areas for improvement in controls. Furthermore, it can maximize control efficiency based on real-time data. In this way, controls can be optimized by analyzing the drone's real-time data. Some or all of the above-described processes in the control unit may be performed using, for example, generative AI, or without using generative AI.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The provider will provide a platform for drone battles controlled by AI agents. The provider will offer a platform that incorporates elements of entertainment, a technology laboratory, and a technology trade show, where spectators can watch drone battles and technology developers can test new technologies. The provider will also provide a platform where sponsoring companies can showcase their technologies. For example, it will enable spectators to watch drone battles through an online platform, enable technology developers to test new technologies, and enable sponsoring companies to showcase their technologies. Step 2: The Strategy Planning Department uses generative AI to formulate strategies on the platform provided by the Supply Department. The Strategy Planning Department analyzes the drone's position and speed, as well as the opponent's movements, to formulate the optimal strategy. For example, the Strategy Planning Department determines the timing of attacks and defenses, taking into account the drone's position and speed, as well as the opponent's movements. The Strategy Planning Department can also modify the strategy in real time using generative AI. Step 3: The piloting unit controls the drone in real time based on the strategy formulated by the strategic planning unit. The piloting unit uses generated AI to control the drone in real time and can modify the strategy according to the battle situation. For example, the piloting unit considers the drone's position and speed, as well as the opponent's movements, to determine the timing of attacks and defenses.

[0058] (Example of form 2) The drone fight arena system according to an embodiment of the present invention is a system that provides drone battles as entertainment by utilizing generative AI. This system provides a platform for drone battles piloted by AI agents, and by using generative AI to formulate strategies and control the drones in real time, it can enhance technological development and the sense of realism for spectators, and also function as a technology trade show. For example, it provides a platform for drone battles piloted by AI agents. This platform incorporates elements of entertainment, a technology laboratory, and a technology trade show. Spectators can watch drone battles, and technology developers can test new technologies. In addition, sponsoring companies can promote their technologies through this platform. Strategies are formulated using generative AI, and drones are controlled in real time. The generative AI analyzes the movements of the drones and formulates the optimal strategy. For example, it determines the timing of attacks and defenses by considering the drone's position and speed, and the opponent's movements. The generative AI controls the drones in real time and can modify the strategy according to the battle situation. This can improve the drone's control performance. Furthermore, this platform aims to build a business ecosystem involving spectators, technology developers, and sponsors. Spectators can enjoy drone battles, and technology developers can test new technologies. Sponsoring companies can showcase their technologies. This aims to create a better society through the development and social implementation of drone technology. For example, spectators can watch drone battles through an online platform. Technology developers can learn drone piloting techniques and strategies in real time, accelerating technological advancements. Sponsoring companies can showcase their technologies through drone battle events and contribute to building a business ecosystem. In this way, the Drone Fight Arena system is a platform that provides drone battles as entertainment using generative AI, enhancing technology development and audience engagement, and functioning as a technology trade show.Furthermore, the aim is to build a business ecosystem that involves spectators, technology developers, and sponsors. This will allow the drone fight arena system to provide drone battles as entertainment, enhance technology development, and increase the sense of immersion for spectators.

[0059] The drone fight arena system according to this embodiment comprises a provisioning unit, a strategy planning unit, and a piloting unit. The provisioning unit provides a platform for battles between drones piloted by AI agents. The provisioning unit provides a platform that incorporates elements such as entertainment, a technology laboratory, and a technology trade show. The provisioning unit can provide a platform where spectators can watch drone battles and technology developers can test new technologies. The provisioning unit can also provide a platform where sponsoring companies can promote their technologies. For example, the provisioning unit enables spectators to watch drone battles through an online platform. The provisioning unit enables technology developers to test new technologies. The provisioning unit also enables sponsoring companies to promote their technologies. The strategy planning unit uses generative AI to formulate strategies on the platform provided by the provisioning unit. The strategy planning unit analyzes the position and speed of the drones and the movements of the opponents, for example, and formulates the optimal strategy. The strategy planning unit can use generative AI to analyze the movements of the drones and formulate the optimal strategy. For example, the strategy planning unit determines the timing of attacks and defenses by considering the drone's position and speed, as well as the opponent's movements. The strategy planning unit can also modify the strategy in real time using generative AI. For example, the strategy planning unit modifies the strategy according to the battle situation. The piloting unit controls the drone in real time based on the strategy planned by the strategy planning unit. The piloting unit controls the drone in real time using, for example, generative AI. The piloting unit can modify the strategy according to the battle situation using generative AI. For example, the piloting unit determines the timing of attacks and defenses by considering the drone's position and speed, as well as the opponent's movements. The piloting unit controls the drone in real time using generative AI and can modify the strategy according to the battle situation. As a result, the drone fight arena system according to this embodiment can provide drone battles as entertainment, enhance technological development, and increase the sense of realism for the audience.

[0060] The provider will provide a platform for drone battles controlled by AI agents. The provider will offer a platform that incorporates elements of entertainment, technology experimentation, and technology showrooms. Specifically, the provider can provide a platform where spectators can watch drone battles and technology developers can test new technologies. Spectators can watch drone battles in real time through the online platform, gaining detailed insights into the progress of the battles and the movements of the drones. Furthermore, the provider can also provide a platform where sponsoring companies can showcase their technologies. Sponsoring companies can equip drones with their technologies and demonstrate their performance through the battles. For example, the provider will enable spectators to watch drone battles via the online platform. Spectators can watch live streams of the battles and enjoy the drone movements and strategies in real time. The provider will also enable technology developers to test new technologies. Technology developers can equip drones with new sensors and algorithms and evaluate their performance through the battles. Furthermore, the provider will enable sponsoring companies to showcase their technologies. Sponsoring companies can equip drones with their own technology and demonstrate its performance through battles. This allows the provider to offer a platform that combines entertainment and technological development, creating an attractive environment for audiences, technology developers, and sponsoring companies.

[0061] The Strategy Planning Department uses generative AI to formulate strategies on the platform provided by the Service Provider Department. For example, the Strategy Planning Department analyzes the drone's position and speed, as well as the opponent's movements, to formulate the optimal strategy. Specifically, the Strategy Planning Department can use generative AI to analyze the drone's movements and formulate the optimal strategy. The generative AI analyzes the drone's position information, speed data, and the opponent's movement patterns in real time to determine the timing of attacks and defenses. For example, the Strategy Planning Department considers the drone's position and speed, as well as the opponent's movements, to determine the timing of attacks and defenses. The generative AI can learn from past battle data and derive the optimal strategy. Furthermore, the Strategy Planning Department can also use generative AI to modify strategies in real time. For example, the Strategy Planning Department modifies strategies according to the battle situation. The generative AI can analyze the progress of the battle and changes in the opponent's movements in real time to derive the optimal strategy. As a result, the Strategy Planning Department can optimize the drone's movements and formulate strategies for victory in battle. Furthermore, the Strategic Planning Department can use generative AI to simulate multiple scenarios and select the most effective strategy. This allows the Strategic Planning Department to demonstrate a high level of strategic thinking in drone battles and provide the optimal strategy for victory.

[0062] The piloting unit controls the drone in real time based on the strategy formulated by the strategic planning unit. The piloting unit uses, for example, generative AI to control the drone in real time. Specifically, the piloting unit can use generative AI to modify the strategy according to the battle situation. The generative AI analyzes the drone's position and speed data, as well as the opponent's movement patterns, in real time to generate optimal control instructions. For example, the piloting unit determines the timing of attacks and defenses by considering the drone's position and speed, and the opponent's movements. The generative AI can learn from past battle data to derive optimal control instructions. Furthermore, the piloting unit can use generative AI to control the drone in real time and modify the strategy according to the battle situation. For example, the piloting unit determines the timing of attacks and defenses by considering the drone's position and speed, and the opponent's movements. The generative AI can analyze the progress of the battle and changes in the opponent's movements in real time to derive optimal control instructions. This allows the piloting unit to optimize the drone's movements and control it towards victory. Additionally, the piloting unit can use generative AI to simulate multiple scenarios and select the most effective control instructions. This allows the control unit to demonstrate high piloting skills in drone battles and provide optimal maneuvers for victory.

[0063] The Strategic Planning Department can analyze the drone's position and speed, as well as the opponent's movements, using generative AI to formulate appropriate strategies. For example, the Strategic Planning Department uses generative AI to analyze the drone's position and speed, as well as the opponent's movements. The Strategic Planning Department can then formulate the optimal strategy. For instance, the Strategic Planning Department determines the timing of attacks and defenses by considering the drone's position and speed, as well as the opponent's movements. The Strategic Planning Department can also modify strategies in real time using generative AI. For example, the Strategic Planning Department modifies strategies according to the battle situation. This demonstrates that by using generative AI, the Strategic Planning Department can analyze the drone's position and speed, as well as the opponent's movements, and formulate the optimal strategy.

[0064] The control unit uses generative AI to operate the drone in real time and can modify its strategy according to the battle situation. For example, the control unit uses generative AI to operate the drone in real time. The control unit uses generative AI to modify its strategy according to the battle situation. For example, the control unit considers the drone's position and speed, as well as the opponent's movements, to determine the timing of attacks and defenses. The control unit uses generative AI to operate the drone in real time and can modify its strategy according to the battle situation. This allows the control unit to operate the drone in real time using generative AI and modify its strategy according to the battle situation.

[0065] The service provider can enable spectators to watch the drone battles through an online platform. The service provider can enable spectators to watch the drone battles through an online platform. The service provider can enable spectators to watch the drone battles through an online platform. For example, the service provider can enable spectators to watch the drone battles through an online platform. This allows spectators to watch the drone battles through an online platform. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0066] The provisioning unit can enable technology developers to try out new technologies. The provisioning unit can enable technology developers to try out new technologies. The provisioning unit can enable technology developers to try out new technologies. For example, the provisioning unit can enable technology developers to try out new technologies. This allows technology developers to try out new technologies. Some or all of the above-described processing in the provisioning unit may be performed using AI, for example, or without using AI.

[0067] The provisioning section can enable sponsoring companies to promote their technology. The provisioning section can enable sponsoring companies to promote their technology. The provisioning section can enable sponsoring companies to promote their technology. For example, the provisioning section can enable sponsoring companies to promote their technology. This allows sponsoring companies to promote their technology. Some or all of the processing described above in the provisioning section may be performed using AI, for example, or without using AI.

[0068] The service provider can estimate the audience's emotions and adjust the viewing perspective and camera angles based on the estimated emotions. For example, if the audience is excited, the service provider can make the camera angles more dynamic to enhance the sense of realism. If the audience is relaxed, the service provider can provide stable camera angles, prioritizing viewing comfort. If the audience is focused, the service provider can also zoom in on important scenes and provide detailed perspectives. This enhances the sense of realism by adjusting the viewing perspective and camera angles according to the audience'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 service provider may be performed using AI, for example, or without AI.

[0069] The service provider can analyze the technology developer's past experimental data and provide an optimal technology testing environment. For example, the service provider can propose an optimal testing environment based on the success rate of experiments conducted by the technology developer in the past. The service provider can reproduce a testing environment under specific conditions from the technology developer's past experimental data. The service provider can also analyze the technology developer's past experimental data and propose improvements to the testing environment. In this way, by analyzing the technology developer's past experimental data, an optimal technology testing environment can be provided. Some or all of the above processes in the service provider may be performed using AI, for example, or without using AI.

[0070] The service provider can customize the timing and content of ad displays based on the sponsor company's marketing strategy. For example, the service provider can adjust the timing of ad displays to match the sponsor company's target audience. The service provider can customize the content of ads based on the sponsor company's marketing strategy. The service provider can also analyze the sponsor company's past advertising effectiveness and propose the optimal ad display method. This allows for the customization of ad display timing and content based on the sponsor company's marketing strategy. Some or all of the above processes performed by the service provider may be carried out using AI, for example, or not using AI.

[0071] The service provider can estimate the audience's emotions and provide interactive elements during the event based on those estimated emotions. For example, if the audience is excited, the service provider can provide real-time voting and commenting functions. If the audience is relaxed, the service provider can provide interactive quizzes and surveys. If the audience is focused, the service provider can also provide detailed information and statistical data. This enhances the viewing experience by providing interactive elements according to the audience's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI, for example, or not using AI.

[0072] The service provider can provide a region-specific technology testing environment, taking into account the geographical location of the technology developer. For example, the service provider can provide a test environment that replicates region-specific climate conditions based on the location of the technology developer. The service provider can provide a test environment that takes into account the region's infrastructure and topography, based on the geographical location of the technology developer. The service provider can also provide a test environment that takes into account the region's laws, regulations, and safety standards, based on the location of the technology developer. In this way, by taking into account the geographical location of the technology developer, a region-specific technology testing environment can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0073] The service provider can analyze the social media activities of sponsoring companies and display relevant advertisements. For example, the service provider can display relevant advertisements based on popular posts on the sponsoring companies' social media. The service provider can analyze the social media activities of sponsoring companies and display advertisements tailored to the target audience. The service provider can also display relevant advertisements based on campaign information on the sponsoring companies' social media. In this way, relevant advertisements can be displayed by analyzing the social media activities of sponsoring companies. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0074] The strategic planning unit can estimate the drone pilot's emotions and adjust the risk level of the strategy based on the estimated emotions. For example, if the pilot is tense, the strategic planning unit can formulate a low-risk strategy. If the pilot is relaxed, the strategic planning unit can formulate a high-risk strategy. If the pilot is excited, the strategic planning unit can also formulate a strategy that considers the balance between risk and reward. In this way, by adjusting the risk level of the strategy according to the drone pilot's emotions, a more appropriate strategy can be formulated. 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 strategic planning unit may be performed using AI, for example, or not using AI.

[0075] The Strategy Planning Department can predict the opponent's strategic patterns by referring to past battle data and formulate optimal countermeasures. For example, the Strategy Planning Department can predict the opponent's attack patterns from past battle data and formulate optimal defensive strategies. The Strategy Planning Department can analyze past battle data to find the opponent's weaknesses and formulate offensive strategies. The Strategy Planning Department can also predict the timing of the opponent's strategic changes based on past battle data and formulate countermeasures. In this way, by referring to past battle data, it is possible to predict the opponent's strategic patterns and formulate optimal countermeasures. Some or all of the above processes in the Strategy Planning Department may be performed using, for example, a generative AI, or without using a generative AI.

[0076] The Strategy Planning Unit can adjust the duration of the strategy based on the drone's battery level and aircraft status. For example, the Strategy Planning Unit can adjust the duration of the strategy based on the drone's battery level. The Strategy Planning Unit can adjust the duration of the strategy considering the drone's aircraft status. The Strategy Planning Unit can also comprehensively judge the drone's battery level and aircraft status and set the optimal duration of the strategy. This allows the duration of the strategy to be adjusted by considering the drone's battery level and aircraft status. Some or all of the above processing in the Strategy Planning Unit may be performed using, for example, generative AI, or without using generative AI.

[0077] The strategic planning unit can estimate the drone operator's emotions and adjust the aggressiveness of the strategy based on the estimated emotions. For example, if the operator is tense, the strategic planning unit can formulate a low-aggressive strategy. If the operator is relaxed, the strategic planning unit can formulate a high-aggressive strategy. If the operator is excited, the strategic planning unit can also formulate a strategy that considers a balance between aggression and defense. In this way, by adjusting the aggressiveness of the strategy according to the drone operator's emotions, a more appropriate strategy can be formulated. 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 strategic planning unit may be performed using, for example, generative AI, or without generative AI.

[0078] The Strategic Planning Department can formulate strategies tailored to the terrain based on the drone's geographical location information. For example, the Strategic Planning Department can formulate strategies that take advantage of the terrain's characteristics based on the drone's geographical location information. The Strategic Planning Department can formulate strategies to avoid obstacles, taking into account the drone's geographical location information. The Strategic Planning Department can also formulate strategies that maximize the advantages of the terrain, taking into account the drone's geographical location information. In this way, by considering the drone's geographical location information, strategies tailored to the terrain can be formulated. Some or all of the above processes in the Strategic Planning Department may be performed using, for example, generative AI, or without using generative AI.

[0079] The Strategic Planning Department can formulate strategies based on the aircraft's durability, using the drone's past maintenance history. For example, the Strategic Planning Department can formulate strategies that take the aircraft's durability into consideration, based on the drone's past maintenance history. The Strategic Planning Department can formulate strategies to compensate for the aircraft's weaknesses by referring to the drone's maintenance history. The Strategic Planning Department can also formulate strategies that maximize the aircraft's durability, based on the drone's maintenance history. In this way, strategies based on the aircraft's durability can be formulated by referring to the drone's past maintenance history. Some or all of the above processes in the Strategic Planning Department may be performed, for example, using generative AI, or without using generative AI.

[0080] The control unit can estimate the drone operator's emotions and adjust the precision of the controls based on the estimated emotions. For example, if the operator is tense, the control unit can enhance auxiliary functions to improve control precision. If the operator is relaxed, the control unit can relax auxiliary functions to increase the degree of freedom of control. If the operator is excited, the control unit can also adjust the balance between control precision and degree of freedom. This allows for more appropriate control by adjusting the control precision according to the drone operator'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.

[0081] The control unit can analyze the drone's real-time data and make appropriate adjustments to its operation. For example, the control unit can optimize the operation based on the drone's real-time data. The control unit can analyze the drone's real-time data and suggest areas for improvement in operation. The control unit can also maximize the efficiency of operation based on the drone's real-time data. In this way, operation can be optimized by analyzing the drone's real-time data. Some or all of the above-described processes in the control unit may be performed using, for example, generative AI, or without using generative AI.

[0082] The control unit can improve the accuracy of obstacle avoidance by integrating the drone's sensor information. For example, the control unit can improve the accuracy of obstacle avoidance by integrating the drone's sensor information. Based on the drone's sensor information, the control unit can propose the optimal route for obstacle avoidance. The control unit can also analyze the drone's sensor information and propose areas for improvement in obstacle avoidance. In this way, the accuracy of obstacle avoidance can be improved by integrating the drone's sensor information. Some or all of the above processing in the control unit may be performed using, for example, generative AI, or without using generative AI.

[0083] The control unit can estimate the drone operator's emotions and adjust the control speed based on the estimated emotions. For example, if the operator is tense, the control unit can slow down the control speed to increase safety. If the operator is relaxed, the control unit can increase the control speed to increase efficiency. If the operator is excited, the control unit can also adjust the balance between control speed and safety. This allows for more appropriate control by adjusting the control speed according to the drone operator'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.

[0084] The control unit can apply a control method appropriate to the terrain based on the drone's geographical location information. For example, the control unit can apply a control method that takes advantage of the terrain's characteristics based on the drone's geographical location information. The control unit can apply a control method that avoids obstacles, taking into account the drone's geographical location information. The control unit can also apply a control method that maximizes the advantages of the terrain, based on the drone's geographical location information. In this way, by taking into account the drone's geographical location information, a control method appropriate to the terrain can be applied. Some or all of the above processing in the control unit may be performed using, for example, generative AI, or without using generative AI.

[0085] The control unit can monitor the drone's communication status and adjust the control method based on the stability of the communication. For example, the control unit can monitor the drone's communication status and adjust the control method if the communication is unstable. Based on the drone's communication status, the control unit can apply the optimal control method when the communication is stable. The control unit can also analyze the drone's communication status and propose a control method that maximizes communication stability. In this way, by monitoring the drone's communication status, the control method can be adjusted based on the stability of the communication. Some or all of the above processing in the control unit may be performed using, for example, generative AI, or without using generative AI.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The service provider can estimate the audience's emotions and customize the viewing experience based on those emotions. For example, if the audience is excited, the service provider can play highlight scenes in real time to further enhance their excitement. If the audience is relaxed, the service provider can provide relaxing music and commentary to make the viewing experience more comfortable. If the audience is focused, the service provider can provide detailed statistical data and strategic explanations to deepen their understanding. This allows for the provision of a customized viewing experience tailored to the audience's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0088] The strategic planning unit can monitor the drone's battery level in real time and adjust the strategy according to the battery's depletion. For example, if the battery level is low, the strategic planning unit can adopt a defensive strategy to conserve battery power. If the battery level is sufficient, the strategic planning unit can adopt an offensive strategy and actively attack the opponent. If the battery level is moderate, the strategic planning unit can adopt a balanced strategy to balance battery consumption with offensive capabilities. This allows the system to formulate the optimal strategy based on the drone's battery level. Some or all of the above-described processes in the strategic planning unit may be performed using, for example, generative AI, or without using generative AI.

[0089] The control unit can improve the accuracy of obstacle avoidance based on the drone's sensor information. For example, if there is an obstacle in front of the drone, the control unit can calculate the optimal avoidance route based on the sensor information and safely avoid the obstacle. The control unit can also monitor the drone's surroundings in real time and take immediate avoidance action when an obstacle approaches. Furthermore, the control unit can analyze the drone's sensor information, learn obstacle avoidance patterns, and perform avoidance actions more efficiently in the future. This improves the accuracy of the drone's obstacle avoidance. Some or all of the above processing in the control unit may be performed using, for example, generative AI, or without using generative AI.

[0090] The service provider can estimate the audience's emotions and provide interactive elements during the event based on those estimated emotions. For example, if the audience is excited, real-time voting and commenting functions can be provided to facilitate interaction among audience members. If the audience is relaxed, interactive quizzes and surveys can be provided to enhance the viewing experience. If the audience is focused, detailed information and statistical data can be provided to deepen their understanding. In this way, the viewing experience can be improved by providing interactive elements that respond to the audience'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 service provider may be performed using AI, for example, or without AI.

[0091] The Strategic Planning Department can analyze past drone battle data and predict the opponent's strategic patterns. For example, it can predict the opponent's attack patterns from past battle data and formulate the optimal defensive strategy. It can also identify the opponent's weaknesses and formulate an offensive strategy. Furthermore, it can predict the timing of the opponent's strategic changes and formulate countermeasures. In this way, by referring to past battle data, it is possible to predict the opponent's strategic patterns and formulate the optimal countermeasures. Some or all of the above processing in the Strategic Planning Department may be performed using, for example, generative AI, or without using generative AI.

[0092] The control unit can estimate the drone operator's emotions and adjust the precision of the controls based on the estimated emotions. For example, if the operator is tense, the assistive functions can be enhanced to improve control precision. If the operator is relaxed, the assistive functions can be relaxed to increase the degree of freedom of control. Also, if the operator is excited, the balance between control precision and degree of freedom can be adjusted. This allows for more appropriate control by adjusting the control precision according to the drone operator'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.

[0093] The service provider can analyze the technology developer's past experimental data and provide an optimal technology testing environment. For example, it can propose an optimal testing environment based on the success rate of experiments conducted by the technology developer in the past. It can also reproduce a testing environment under specific conditions. Furthermore, it can analyze past experimental data and suggest areas for improvement in the testing environment. In this way, by analyzing the technology developer's past experimental data, an optimal technology testing environment can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0094] The strategy planning unit can estimate the drone pilot's emotions and adjust the risk level of the strategy based on the estimated emotions. For example, if the pilot is tense, a low-risk strategy can be formulated. If the pilot is relaxed, a high-risk strategy can be formulated. If the pilot is excited, a strategy that considers the balance between risk and reward can be formulated. In this way, by adjusting the risk level of the strategy according to the drone pilot's emotions, a more appropriate strategy can be formulated. 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 strategy planning unit may be performed using AI, for example, or without using AI.

[0095] The control unit can analyze real-time data from the drone and make appropriate adjustments to its controls. For example, it can optimize controls based on the drone's real-time data. It can also analyze real-time data and suggest areas for improvement in controls. Furthermore, it can maximize control efficiency based on real-time data. In this way, controls can be optimized by analyzing the drone's real-time data. Some or all of the above-described processes in the control unit may be performed using, for example, generative AI, or without using generative AI.

[0096] The service provider can estimate the audience's emotions and adjust the viewing perspective and camera angles based on the estimated emotions. For example, if the audience is excited, the camera angles can be made more dynamic to enhance the sense of realism. If the audience is relaxed, a stable camera angle can be provided to prioritize viewing comfort. If the audience is focused, important scenes can be zoomed in on to provide a detailed perspective. In this way, the sense of realism can be enhanced by adjusting the viewing perspective and camera angles according to the audience'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 service provider may be performed using AI, for example, or without AI.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The provider will provide a platform for drone battles controlled by AI agents. The provider will offer a platform that incorporates elements of entertainment, a technology laboratory, and a technology trade show, where spectators can watch drone battles and technology developers can test new technologies. The provider will also provide a platform where sponsoring companies can showcase their technologies. For example, it will enable spectators to watch drone battles through an online platform, enable technology developers to test new technologies, and enable sponsoring companies to showcase their technologies. Step 2: The Strategy Planning Department uses generative AI to formulate strategies on the platform provided by the Supply Department. The Strategy Planning Department analyzes the drone's position and speed, as well as the opponent's movements, to formulate the optimal strategy. For example, the Strategy Planning Department determines the timing of attacks and defenses, taking into account the drone's position and speed, as well as the opponent's movements. The Strategy Planning Department can also modify the strategy in real time using generative AI. Step 3: The piloting unit controls the drone in real time based on the strategy formulated by the strategic planning unit. The piloting unit uses generated AI to control the drone in real time and can modify the strategy according to the battle situation. For example, the piloting unit considers the drone's position and speed, as well as the opponent's movements, to determine the timing of attacks and defenses.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the provisioning unit, strategy planning unit, and piloting unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the provisioning unit is implemented by the control unit 46A of the smart device 14, enabling spectators to watch the drone battle through an online platform. The strategy planning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the drone's movements using generated AI and formulates an optimal strategy. The piloting unit is implemented, for example, by the control unit 46A of the smart device 14, which can pilot the drone in real time and modify the strategy according to the battle situation. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the provisioning unit, strategy planning unit, and piloting unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the provisioning unit is implemented by the control unit 46A of the smart glasses 214, enabling spectators to watch the drone battle through an online platform. The strategy planning unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the drone's movements using generated AI and formulates an optimal strategy. The piloting unit is implemented, for example, by the control unit 46A of the smart glasses 214, which can pilot the drone in real time and modify the strategy according to the battle situation. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the provisioning unit, strategy planning unit, and piloting unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the provisioning unit is implemented by the control unit 46A of the headset terminal 314, enabling spectators to watch the drone battle through an online platform. The strategy planning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the drone's movements using generated AI and formulates an optimal strategy. The piloting unit is implemented, for example, by the control unit 46A of the headset terminal 314, which can pilot the drone in real time and modify the strategy according to the battle situation. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the provisioning unit, strategy planning unit, and control unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the provisioning unit is implemented by the control unit 46A of the robot 414, enabling spectators to watch the drone battle through an online platform. The strategy planning unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the drone's movements using generated AI and formulates an optimal strategy. The control unit is implemented by the control unit 46A of the robot 414, which can control the drone in real time and modify the strategy according to the battle situation. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) The service provider offers a platform for drones controlled by AI agents to engage in battles, A strategic planning unit that formulates strategies using generated AI on the platform provided by the aforementioned provision unit, The system includes a control unit that operates the drone in real time based on the strategy formulated by the aforementioned strategic planning unit. A system characterized by the following features. (Note 2) The aforementioned Strategic Planning Department, The generated AI analyzes the drone's position and speed, as well as the opponent's movements, to formulate an appropriate strategy. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned control unit is The AI ​​generates the drones to be controlled in real time, and the strategy is modified according to the battle situation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, This will allow spectators to watch the drone battles through an online platform. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, This allows technology developers to experiment with new technologies. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, This allows sponsoring companies to showcase their own technologies. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, The system estimates the audience's emotions and adjusts the viewing perspective and camera angles based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, We analyze past experimental data from technology developers and provide an appropriate technology testing environment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned supply unit is, Customize the timing and content of ad displays based on the sponsoring company's marketing strategy. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned supply unit is, It estimates the audience's emotions and provides interactive elements during the game based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned supply unit is, Based on the geographical location information of technology developers, we provide region-specific technology testing environments. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned supply unit is, We analyze the social media activity of our sponsoring companies and display relevant advertisements. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned Strategic Planning Department, The system estimates the emotions of the drone pilot and adjusts the risk level of the strategy based on the estimated emotions of the pilot. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned Strategic Planning Department, Based on past battle data, predict the opponent's strategic patterns and devise appropriate countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned Strategic Planning Department, The duration of the strategy is adjusted based on the drone's battery level and aircraft status. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned Strategic Planning Department, The system estimates the emotions of the drone pilot and adjusts the aggressiveness of the strategy based on the estimated emotions of the pilot. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned Strategic Planning Department, Based on the drone's geographical location information, strategies are formulated according to the terrain. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned Strategic Planning Department, Based on the drone's past maintenance history, a strategy will be developed that is based on the aircraft's durability. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned control unit is The system estimates the emotions of the drone pilot and adjusts the precision of the controls based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned control unit is Analyze real-time drone data and make appropriate adjustments to the controls. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned control unit is Integrating drone sensor data improves the accuracy of obstacle avoidance. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned control unit is The system estimates the drone pilot's emotions and adjusts the flight speed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned control unit is Based on the drone's geographical location information, a control method appropriate to the terrain is applied. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned control unit is The system monitors the drone's communication status and adjusts the control method based on the stability of the communication. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The provider division offers a platform for drones piloted by AI agents to engage in battles, A strategy planning unit that uses generated AI to formulate strategies on the platform provided by the aforementioned provision unit, The system includes a control unit that operates the drone in real time based on the strategy formulated by the aforementioned strategic planning unit. A system characterized by the following features.

2. The aforementioned Strategic Planning Department, The generated AI analyzes the drone's position and speed, as well as the opponent's movements, to formulate an appropriate strategy. The system according to feature 1.

3. The aforementioned control unit is The AI ​​generates the drones to be controlled in real time, and the strategy is modified according to the battle situation. The system according to feature 1.

4. The aforementioned supply unit is, This will allow spectators to watch the drone battles through an online platform. The system according to feature 1.

5. The aforementioned supply unit is, This allows technology developers to experiment with new technologies. The system according to feature 1.

6. The aforementioned supply unit is, This allows sponsoring companies to showcase their own technologies. The system according to feature 1.

7. The aforementioned supply unit is, The system estimates the audience's emotions and adjusts the viewing perspective and camera angles based on those estimated emotions. The system according to feature 1.

8. The aforementioned supply unit is, We analyze past experimental data from technology developers and provide an appropriate technology testing environment. The system according to feature 1.

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