system

Abstract

The system according to this embodiment aims to effectively prevent damage to wildlife caused by predation and to solve problems such as the aging of hunting associations and the issue of compensation. [Solution] The system according to the embodiment comprises a detection unit, a deterrent unit, a permit transmission unit, a shotgun control unit, and a coordination unit. The detection unit detects wildlife. The deterrent unit deters wildlife detected by the detection unit. The permit transmission unit transmits a firing permit to wildlife not deterred by the deterrent unit. The shotgun control unit controls the shotgun based on the firing permit transmitted by the permit transmission unit. The coordination unit performs complex operations in cooperation with a data center.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , ,

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

Background Art

[0002] <​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​The system according to this embodiment comprises a detection unit, a deterrent unit, a permit transmission unit, a shotgun control unit, and a coordination unit. The detection unit detects wildlife. The deterrent unit deters wildlife detected by the detection unit. The permit transmission unit transmits a firing permit to wildlife not deterred by the deterrent unit. The shotgun control unit controls the shotgun based on the firing permit transmitted by the permit transmission unit. The coordination unit performs complex operations in cooperation with a data center. [Effects of the Invention]

[0007] The system according to this embodiment can effectively prevent damage to wildlife caused by predation and can solve problems such as the aging of hunting associations and the issue of compensation. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The wildlife prevention system according to an embodiment of the present invention is a system that automatically prevents damage caused by wildlife such as bears, wild boars, and deer, using a quadruped robot or drone equipped with an AI agent. This system performs complex operations such as wildlife detection, deterrence, transmission of firing permission, control of hunting rifles, and coordination with a data center. For example, the wildlife prevention system uses a quadruped robot or drone equipped with an AI agent to detect wildlife in real time using cameras and sensors. Next, the wildlife prevention system uses the AI ​​agent to take the optimal action against the detected wildlife to deter it. For example, the robot emits sound or light to startle the wildlife. Furthermore, if the wildlife is not deterred, the wildlife prevention system uses a hunting rifle mounted on the robot to exterminate it. Firing permission is transmitted to a human's smartphone via 5G communication, and firing is initiated when the human grants permission. If it is difficult to mount the AI ​​agent on the robot, it connects to a data center in real time using 5G communication to perform complex operations. This enables the combined operation of a quadruped robot and a drone. This system can effectively prevent damage caused by wildlife and solve challenges faced by local governments. The wildlife control system can perform complex operations such as wildlife detection, deterrence, transmission of firing permits, control of hunting rifles, and coordination with data centers.

[0029] The wildlife prevention system according to this embodiment comprises a detection unit, a deterrent unit, a permit transmission unit, a shotgun control unit, and a coordination unit. The detection unit detects wildlife. The detection unit detects wildlife in real time, for example, using a camera or sensor. The detection unit may include AI processing. The deterrent unit deters wildlife detected by the detection unit. The deterrent unit may startle wildlife by emitting sound or light, for example. The deterrent unit may include AI processing. The permit transmission unit transmits a firing permit to wildlife that is not deterred by the deterrent unit. The permit transmission unit transmits a firing permit to a human's smartphone, for example, via 5G communication. The permit transmission unit may include AI processing. The shotgun control unit controls the shotgun based on the firing permit transmitted by the permit transmission unit. The shotgun control unit may include AI processing. The coordination unit performs complex operations in cooperation with a data center. The coordination unit connects to a data center in real time, for example, using 5G communication, and performs complex operations. The coordination unit may include AI processing. As a result, the wildlife prevention system according to this embodiment can efficiently detect, drive away, transmit firing permission, control hunting rifles, and perform coordinated operations.

[0030] The detection unit detects wildlife. For example, it uses cameras and sensors to detect wildlife in real time. Specifically, cameras capture high-resolution images, and sensors use infrared and ultrasound to detect wildlife movement. These devices cover a wide area and can quickly detect the approach of wildlife. Furthermore, the detection unit incorporates AI, which analyzes collected video and sensor data to identify the type and behavioral patterns of wildlife. The AI ​​uses deep learning technology to learn from large amounts of data, enabling highly accurate recognition of wildlife characteristics. For example, the AI ​​analyzes the shape and movement of wildlife from camera footage to identify specific animals. It can also analyze sensor data to predict the speed and direction of wildlife movement. This allows the detection unit to detect wildlife approach early and respond quickly. Additionally, the detection unit can transmit collected data to a cloud server for sharing with other systems and departments. This enhances information sharing and collaboration across the entire system, resulting in more effective wildlife prevention.

[0031] The deterrent unit drives away wildlife detected by the detection unit. For example, the deterrent unit uses sounds or lights to startle the wildlife. Specifically, it uses devices that emit high-frequency sounds or bright lights to create an unpleasant stimulus for the wildlife. This is expected to cause the wildlife to flee the area. The deterrent unit incorporates AI, which selects the optimal deterrent method based on the type and behavior of the wildlife. For example, the AI ​​identifies the type of wildlife and generates the most effective sound and light patterns for that animal. The AI ​​also monitors the wildlife's reaction in real time and adjusts the deterrent method as needed. This allows the deterrent unit to respond effectively and flexibly to wildlife. Furthermore, the deterrent unit records the results of deterrents and stores them in a database. This allows for analysis of past deterrent effectiveness and helps in future countermeasures. For example, it can analyze which deterrent method was most effective for a particular type of wildlife and optimize the deterrent method based on the results. This allows the deterrent unit to continuously improve its effectiveness.

[0032] The authorization transmission unit sends a firing permit to wildlife that is not driven away by the deterrent unit. The authorization transmission unit sends the firing permit to a human's smartphone, for example, via 5G communication. Specifically, the authorization transmission unit monitors the results of the deterrent unit and issues a firing permit if the deterrent fails. The authorization transmission unit incorporates AI to automatically detect deterrent failures and quickly send a firing permit. The AI ​​analyzes the results of the deterrent and determines whether the wildlife still poses a threat. For example, the AI ​​monitors the location and behavior of the wildlife and issues a firing permit if the deterrent has not been successful. The authorization transmission unit also evaluates the risks and safety of firing before sending a firing permit. For example, it considers the surrounding environment and human safety to determine whether firing is appropriate. This allows the authorization transmission unit to minimize the risks of firing while enabling a quick and appropriate response. Furthermore, the authorization transmission unit records the transmission history of firing permits and stores it in a database. This allows for analysis of past firing permit history to be used for future countermeasures. For example, by analyzing how firing permits were issued in specific situations and optimizing the criteria for firing permits based on the results, the permit transmission unit can continuously improve its effectiveness.

[0033] The gun control unit controls the gun based on the firing permit transmitted by the permit transmission unit. The gun control unit may also include AI processing. Specifically, upon receiving the firing permit, the gun control unit prepares the gun for firing. The AI ​​optimizes the timing and direction of firing to ensure effective shooting against wildlife. For example, the AI ​​analyzes the location and movement of wildlife in real time to calculate the optimal firing timing. The AI ​​also assesses the risks of firing and takes controls to ensure the safety of the surrounding area. This allows the gun control unit to improve the accuracy and safety of firing. Furthermore, the gun control unit records the results of firing and stores them in a database. This allows for analysis of past firing data to be used for future countermeasures. For example, it can analyze how firing occurred in specific situations and optimize the firing control algorithm based on the results. This allows the gun control unit to continuously improve its effectiveness.

[0034] The integration unit performs complex operations in conjunction with the data center. For example, the integration unit connects to the data center in real time using 5G communication to perform complex operations. Specifically, the integration unit receives instructions from the data center and adjusts the operation of the entire system. AI analyzes the instructions from the data center and determines the optimal operation. For example, based on the instructions from the data center, the AI ​​adjusts the operation of the detection unit, deterrent unit, permission transmission unit, and hunting rifle control unit. The integration unit also transmits data from the entire system to the data center and receives the analysis results from the data center. This allows the integration unit to optimize the operation of the entire system in real time. Furthermore, the integration unit performs system updates and maintenance through its collaboration with the data center. For example, it updates the system software and adds new functions based on instructions from the data center. The integration unit also monitors the system status and notifies the data center if an anomaly occurs. This allows the integration unit to improve the reliability and security of the system.

[0035] The detection unit can detect wildlife in real time using cameras and sensors. For example, the detection unit can detect wildlife using a camera. The camera can cover a wide area with high resolution. The detection unit can also detect wildlife using sensors. For example, infrared sensors or ultrasonic sensors can be used. This makes real-time detection of wildlife possible by using cameras and sensors. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input data from cameras and sensors into a generating AI and have the generating AI perform wildlife detection.

[0036] The deterrent unit can startle wildlife by emitting sound or light. For example, the deterrent unit can startle wildlife by emitting a loud sound. The sound can be high-frequency or low-frequency. The deterrent unit can also startle wildlife by emitting a bright light. The light can be, for example, a flashlight or laser beam. In this way, wildlife can be effectively driven away by using sound and light. Some or all of the above processing in the deterrent unit may be performed using AI or not. For example, the deterrent unit can input the generation of sound or light into a generating AI and have the generating AI perform the deterrent of wildlife.

[0037] The authorization transmission unit can transmit firing permission to a human's smartphone via 5G communication. The authorization transmission unit transmits firing permission at high speed and with low latency using 5G communication, for example. This allows for rapid transmission of firing permission by using 5G communication. Some or all of the above processing in the authorization transmission unit may be performed using AI or not. For example, the authorization transmission unit can input the transmission of firing permission to a generating AI and have the generating AI execute the transmission of firing permission.

[0038] The shotgun control unit can control the shotgun based on the firing permission transmitted by the permission transmission unit. For example, the shotgun control unit fires the shotgun when firing permission is transmitted. This enables remote pest control by controlling the shotgun based on firing permission. Some or all of the above processing in the shotgun control unit may be performed using AI or not. For example, the shotgun control unit can input firing permission into a generating AI and have the generating AI perform the control of the shotgun.

[0039] The collaboration unit can connect to the data center in real time using 5G communication and perform complex operations. For example, the collaboration unit can connect to the data center using 5G communication and perform complex operations by utilizing the processing power of the data center. This makes it possible to perform complex operations in cooperation with the data center by using 5G communication. Some or all of the above-mentioned processing in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input the data center collaboration into a generating AI and have the generating AI execute the complex operations.

[0040] The detection unit can analyze the behavioral patterns of wildlife during detection to optimize the timing of detection. For example, the detection unit can analyze the time periods when wildlife is searching for food and strengthen detection during those times. The detection unit can also analyze the time periods when wildlife is resting and weaken detection during those times. Furthermore, the detection unit can analyze the routes that wildlife travels and perform detection along those routes. In this way, the timing of detection can be optimized by analyzing the behavioral patterns of wildlife. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input wildlife behavioral data into a generating AI and have the generating AI perform behavioral pattern analysis.

[0041] The detection unit can improve detection accuracy by combining multiple sensors during detection. For example, the detection unit can combine a camera and an infrared sensor to simultaneously detect vision and heat. The detection unit can combine an acoustic sensor and a vibration sensor to simultaneously detect sound and vibration. Furthermore, the detection unit can combine radar and lidar to simultaneously detect distance and shape. In this way, detection accuracy can be improved by combining multiple sensors. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input data from multiple sensors into a generating AI and have the generating AI perform the task of improving detection accuracy.

[0042] The deterrent unit can apply different deterrent methods depending on the type of wildlife during deterrence. For example, it can deter bears by emitting a loud noise, deter wild boars by emitting a bright light, and deer by emitting vibrations. By applying deterrent methods appropriate to the type of wildlife, the unit can effectively deter them. Some or all of the above-described processes in the deterrent unit may be performed using AI, or they may not. For example, the deterrent unit can input wildlife type data into a generating AI and have the generating AI execute the application of deterrent methods.

[0043] The permit transmission unit can analyze the level of danger posed by wildlife and determine the priority of permits for firing when transmitting a permit. For example, the permit transmission unit will prioritize transmitting a permit if there is a high probability that wildlife will harm humans. If there is a high probability that wildlife will damage crops, the permit transmission unit may give a medium priority to the permit. Furthermore, if there is a low probability that wildlife will affect the natural environment, the permit transmission unit may give a low priority to the permit. In this way, the priority of permits for firing can be appropriately determined by analyzing the level of danger posed by wildlife. Some or all of the above processing in the permit transmission unit may be performed using AI or not. For example, the permit transmission unit can input wildlife danger data into a generating AI and have the generating AI perform the danger analysis.

[0044] The shotgun control unit can predict the movement of wildlife and optimize the timing of firing during shotgun control. For example, the shotgun control unit can predict the direction and speed of wildlife movement and fire at the optimal time. The shotgun control unit can predict where wildlife may stop and fire at that time. Furthermore, the shotgun control unit can consider the possibility of wildlife suddenly changing direction and adjust the timing of firing. In this way, the timing of firing can be optimized by predicting the movement of wildlife. Some or all of the above processing in the shotgun control unit may be performed using AI or not. For example, the shotgun control unit can input wildlife movement data into a generating AI and have the generating AI perform movement predictions.

[0045] The integration unit can estimate the processing capacity of the data center and adjust the integration method based on the estimated processing capacity. For example, if the data center has high processing capacity, the integration unit can perform complex integration operations. If the data center has low processing capacity, the integration unit can perform simple integration operations. Furthermore, if the data center has moderate processing capacity, the integration unit can perform appropriate integration operations. By adjusting the integration method based on the processing capacity of the data center, efficient integration becomes possible. Some or all of the above-described processes in the integration unit may be performed using AI or not. For example, the integration unit can input data center processing capacity data into a generating AI and have the generating AI perform processing capacity estimation.

[0046] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0047] The detection unit can detect the body temperature of wild animals and identify the species based on that temperature. For example, the detection unit can use an infrared sensor to detect the body temperature of wild animals and analyze the temperature data to identify species such as bears, wild boars, and deer. The detection unit can also monitor changes in body temperature in real time to understand the activity status of wild animals. Furthermore, by accumulating temperature data and comparing it with past data, the detection unit can predict the behavioral patterns of wild animals. As a result, by utilizing temperature data, it becomes possible to identify the species of wild animals and predict their behavior, enabling the implementation of more effective preventative measures.

[0048] The deterrent unit can repel wildlife by mimicking their calls. For example, it can repel other bears by mimicking the calls of bears. It can also repel wild boars by mimicking their calls. Furthermore, it can repel deer by mimicking the calls of deer. In this way, wildlife can be repelled in a natural manner by mimicking the calls of wildlife.

[0049] The authorization transmission unit can perform a safety check of the surroundings before transmitting permission to fire. For example, the authorization transmission unit can use cameras and sensors to check the surrounding situation and confirm that there are no humans or other animals. If there are humans or other animals in the vicinity, the authorization transmission unit can temporarily suspend the transmission of permission to fire. In addition, the authorization transmission unit can monitor the surrounding situation in real time and transmit permission to fire only when safety is confirmed. This allows for the transmission of permission to fire while ensuring the safety of the surroundings.

[0050] The shotgun control unit can use a combination of multiple sensors to accurately pinpoint the location of wildlife when firing. For example, it can combine a camera and an infrared sensor to simultaneously detect vision and heat. It can also combine an acoustic sensor and a vibration sensor to simultaneously detect sound and vibration. Furthermore, it can combine radar and lidar to simultaneously detect distance and shape. By combining multiple sensors, the shotgun control unit can accurately pinpoint the location of wildlife and improve firing accuracy.

[0051] The integration unit can use multiple communication methods in combination to ensure communication stability when integrating with a data center. For example, the integration unit can use both 5G and Wi-Fi communication to ensure communication stability. If 5G communication becomes unstable, the integration unit can switch to Wi-Fi communication. Furthermore, the integration unit can monitor the communication status in real time and select the optimal communication method. This ensures communication stability and enables smooth integration with the data center.

[0052] The integration unit can estimate the processing capacity of the data center and adjust the integration method based on the estimated processing capacity. For example, if the data center has high processing capacity, the integration unit can perform complex integration operations. If the data center has low processing capacity, the integration unit can perform simple integration operations. Furthermore, if the data center has moderate processing capacity, the integration unit can perform appropriate integration operations. By adjusting the integration method based on the processing capacity of the data center, efficient integration becomes possible.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The detection unit detects wildlife. The detection unit detects wildlife in real time, for example, using a camera or sensor. The detection unit may include AI processing. Step 2: The deterrent unit deters the wildlife detected by the detection unit. The deterrent unit may, for example, emit sound or light to startle the wildlife. The deterrent unit may include AI processing. Step 3: The authorization transmission unit transmits a firing authorization to wildlife that is not driven away by the driving unit. The authorization transmission unit transmits the firing authorization to a human's smartphone, for example, via 5G communication. The authorization transmission unit may include AI processing. Step 4: The shotgun control unit controls the shotgun based on the firing permission transmitted by the permission transmission unit. The shotgun control unit may include AI processing. Step 5: The integration unit performs complex operations in conjunction with the data center. For example, the integration unit connects to the data center in real time using 5G communication and performs complex operations. The integration unit may include AI processing.

[0055] (Example of form 2) The wildlife prevention system according to an embodiment of the present invention is a system that automatically prevents damage caused by wildlife such as bears, wild boars, and deer, using a quadruped robot or drone equipped with an AI agent. This system performs complex operations such as wildlife detection, deterrence, transmission of firing permission, control of hunting rifles, and coordination with a data center. For example, the wildlife prevention system uses a quadruped robot or drone equipped with an AI agent to detect wildlife in real time using cameras and sensors. Next, the wildlife prevention system uses the AI ​​agent to take the optimal action against the detected wildlife to deter it. For example, the robot emits sound or light to startle the wildlife. Furthermore, if the wildlife is not deterred, the wildlife prevention system uses a hunting rifle mounted on the robot to exterminate it. Firing permission is transmitted to a human's smartphone via 5G communication, and firing is initiated when the human grants permission. If it is difficult to mount the AI ​​agent on the robot, it connects to a data center in real time using 5G communication to perform complex operations. This enables the combined operation of a quadruped robot and a drone. This system can effectively prevent damage caused by wildlife and solve challenges faced by local governments. The wildlife control system can perform complex operations such as wildlife detection, deterrence, transmission of firing permits, control of hunting rifles, and coordination with data centers.

[0056] The wildlife prevention system according to this embodiment comprises a detection unit, a deterrent unit, a permit transmission unit, a shotgun control unit, and a coordination unit. The detection unit detects wildlife. The detection unit detects wildlife in real time, for example, using a camera or sensor. The detection unit may include AI processing. The deterrent unit deters wildlife detected by the detection unit. The deterrent unit may startle wildlife by emitting sound or light, for example. The deterrent unit may include AI processing. The permit transmission unit transmits a firing permit to wildlife that is not deterred by the deterrent unit. The permit transmission unit transmits a firing permit to a human's smartphone, for example, via 5G communication. The permit transmission unit may include AI processing. The shotgun control unit controls the shotgun based on the firing permit transmitted by the permit transmission unit. The shotgun control unit may include AI processing. The coordination unit performs complex operations in cooperation with a data center. The coordination unit connects to a data center in real time, for example, using 5G communication, and performs complex operations. The coordination unit may include AI processing. As a result, the wildlife prevention system according to this embodiment can efficiently detect, drive away, transmit firing permission, control hunting rifles, and perform coordinated operations.

[0057] The detection unit detects wildlife. For example, it uses cameras and sensors to detect wildlife in real time. Specifically, cameras capture high-resolution images, and sensors use infrared and ultrasound to detect wildlife movement. These devices cover a wide area and can quickly detect the approach of wildlife. Furthermore, the detection unit incorporates AI, which analyzes collected video and sensor data to identify the type and behavioral patterns of wildlife. The AI ​​uses deep learning technology to learn from large amounts of data, enabling highly accurate recognition of wildlife characteristics. For example, the AI ​​analyzes the shape and movement of wildlife from camera footage to identify specific animals. It can also analyze sensor data to predict the speed and direction of wildlife movement. This allows the detection unit to detect wildlife approach early and respond quickly. Additionally, the detection unit can transmit collected data to a cloud server for sharing with other systems and departments. This enhances information sharing and collaboration across the entire system, resulting in more effective wildlife prevention.

[0058] The deterrent unit drives away wildlife detected by the detection unit. For example, the deterrent unit uses sounds or lights to startle the wildlife. Specifically, it uses devices that emit high-frequency sounds or bright lights to create an unpleasant stimulus for the wildlife. This is expected to cause the wildlife to flee the area. The deterrent unit incorporates AI, which selects the optimal deterrent method based on the type and behavior of the wildlife. For example, the AI ​​identifies the type of wildlife and generates the most effective sound and light patterns for that animal. The AI ​​also monitors the wildlife's reaction in real time and adjusts the deterrent method as needed. This allows the deterrent unit to respond effectively and flexibly to wildlife. Furthermore, the deterrent unit records the results of deterrents and stores them in a database. This allows for analysis of past deterrent effectiveness and helps in future countermeasures. For example, it can analyze which deterrent method was most effective for a particular type of wildlife and optimize the deterrent method based on the results. This allows the deterrent unit to continuously improve its effectiveness.

[0059] The authorization transmission unit sends a firing permit to wildlife that is not driven away by the deterrent unit. The authorization transmission unit sends the firing permit to a human's smartphone, for example, via 5G communication. Specifically, the authorization transmission unit monitors the results of the deterrent unit and issues a firing permit if the deterrent fails. The authorization transmission unit incorporates AI to automatically detect deterrent failures and quickly send a firing permit. The AI ​​analyzes the results of the deterrent and determines whether the wildlife still poses a threat. For example, the AI ​​monitors the location and behavior of the wildlife and issues a firing permit if the deterrent has not been successful. The authorization transmission unit also evaluates the risks and safety of firing before sending a firing permit. For example, it considers the surrounding environment and human safety to determine whether firing is appropriate. This allows the authorization transmission unit to minimize the risks of firing while enabling a quick and appropriate response. Furthermore, the authorization transmission unit records the transmission history of firing permits and stores it in a database. This allows for analysis of past firing permit history to be used for future countermeasures. For example, by analyzing how firing permits were issued in specific situations and optimizing the criteria for firing permits based on the results, the permit transmission unit can continuously improve its effectiveness.

[0060] The gun control unit controls the gun based on the firing permit transmitted by the permit transmission unit. The gun control unit may also include AI processing. Specifically, upon receiving the firing permit, the gun control unit prepares the gun for firing. The AI ​​optimizes the timing and direction of firing to ensure effective shooting against wildlife. For example, the AI ​​analyzes the location and movement of wildlife in real time to calculate the optimal firing timing. The AI ​​also assesses the risks of firing and takes controls to ensure the safety of the surrounding area. This allows the gun control unit to improve the accuracy and safety of firing. Furthermore, the gun control unit records the results of firing and stores them in a database. This allows for analysis of past firing data to be used for future countermeasures. For example, it can analyze how firing occurred in specific situations and optimize the firing control algorithm based on the results. This allows the gun control unit to continuously improve its effectiveness.

[0061] The integration unit performs complex operations in conjunction with the data center. For example, the integration unit connects to the data center in real time using 5G communication to perform complex operations. Specifically, the integration unit receives instructions from the data center and adjusts the operation of the entire system. AI analyzes the instructions from the data center and determines the optimal operation. For example, based on the instructions from the data center, the AI ​​adjusts the operation of the detection unit, deterrent unit, permission transmission unit, and hunting rifle control unit. The integration unit also transmits data from the entire system to the data center and receives the analysis results from the data center. This allows the integration unit to optimize the operation of the entire system in real time. Furthermore, the integration unit performs system updates and maintenance through its collaboration with the data center. For example, it updates the system software and adds new functions based on instructions from the data center. The integration unit also monitors the system status and notifies the data center if an anomaly occurs. This allows the integration unit to improve the reliability and security of the system.

[0062] The detection unit can detect wildlife in real time using cameras and sensors. For example, the detection unit can detect wildlife using a camera. The camera can cover a wide area with high resolution. The detection unit can also detect wildlife using sensors. For example, infrared sensors or ultrasonic sensors can be used. This makes real-time detection of wildlife possible by using cameras and sensors. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input data from cameras and sensors into a generating AI and have the generating AI perform wildlife detection.

[0063] The deterrent unit can startle wildlife by emitting sound or light. For example, the deterrent unit can startle wildlife by emitting a loud sound. The sound can be high-frequency or low-frequency. The deterrent unit can also startle wildlife by emitting a bright light. The light can be, for example, a flashlight or laser beam. In this way, wildlife can be effectively driven away by using sound and light. Some or all of the above processing in the deterrent unit may be performed using AI or not. For example, the deterrent unit can input the generation of sound or light into a generating AI and have the generating AI perform the deterrent of wildlife.

[0064] The authorization transmission unit can transmit firing permission to a human's smartphone via 5G communication. The authorization transmission unit transmits firing permission at high speed and with low latency using 5G communication, for example. This allows for rapid transmission of firing permission by using 5G communication. Some or all of the above processing in the authorization transmission unit may be performed using AI or not. For example, the authorization transmission unit can input the transmission of firing permission to a generating AI and have the generating AI execute the transmission of firing permission.

[0065] The shotgun control unit can control the shotgun based on the firing permission transmitted by the permission transmission unit. For example, the shotgun control unit fires the shotgun when firing permission is transmitted. This enables remote pest control by controlling the shotgun based on firing permission. Some or all of the above processing in the shotgun control unit may be performed using AI or not. For example, the shotgun control unit can input firing permission into a generating AI and have the generating AI perform the control of the shotgun.

[0066] The collaboration unit can connect to the data center in real time using 5G communication and perform complex operations. For example, the collaboration unit can connect to the data center using 5G communication and perform complex operations by utilizing the processing power of the data center. This makes it possible to perform complex operations in cooperation with the data center by using 5G communication. Some or all of the above-mentioned processing in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input the data center collaboration into a generating AI and have the generating AI execute the complex operations.

[0067] The detection unit can estimate the emotions of wildlife and adjust the detection accuracy based on the estimated emotions. For example, if the wildlife is alert, the detection unit can increase its sensitivity to detect even subtle movements. If the wildlife is relaxed, the detection unit can decrease its sensitivity to reduce false positives. If the wildlife is excited, the detection unit can maintain a moderate sensitivity and detect at the appropriate time. This reduces false positives by adjusting the detection accuracy based on the wildlife's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input wildlife emotion data into a generative AI and have the generative AI perform emotion estimation.

[0068] The detection unit can analyze the behavioral patterns of wildlife during detection to optimize the timing of detection. For example, the detection unit can analyze the time periods when wildlife is searching for food and strengthen detection during those times. The detection unit can also analyze the time periods when wildlife is resting and weaken detection during those times. Furthermore, the detection unit can analyze the routes that wildlife travels and perform detection along those routes. In this way, the timing of detection can be optimized by analyzing the behavioral patterns of wildlife. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input wildlife behavioral data into a generating AI and have the generating AI perform behavioral pattern analysis.

[0069] The detection unit can improve detection accuracy by combining multiple sensors during detection. For example, the detection unit can combine a camera and an infrared sensor to simultaneously detect vision and heat. The detection unit can combine an acoustic sensor and a vibration sensor to simultaneously detect sound and vibration. Furthermore, the detection unit can combine radar and lidar to simultaneously detect distance and shape. In this way, detection accuracy can be improved by combining multiple sensors. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input data from multiple sensors into a generating AI and have the generating AI perform the task of improving detection accuracy.

[0070] The deterrent unit can estimate the emotions of wildlife and adjust its deterrent method based on the estimated emotions. For example, if wildlife is agitated, the deterrent unit can use strong sounds or lights to deter it. If wildlife is relaxed, the deterrent unit can use gentle sounds or lights to deter it. If wildlife is alert, the deterrent unit can use moderate sounds or lights to deter it. This allows for effective deterrence by adjusting the deterrent method based on the wildlife's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the deterrent unit may be performed using AI or not. For example, the deterrent unit can input wildlife emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The deterrent unit can apply different deterrent methods depending on the type of wildlife during deterrence. For example, it can deter bears by emitting a loud noise, deter wild boars by emitting a bright light, and deer by emitting vibrations. By applying deterrent methods appropriate to the type of wildlife, the unit can effectively deter them. Some or all of the above-described processes in the deterrent unit may be performed using AI, or they may not. For example, the deterrent unit can input wildlife type data into a generating AI and have the generating AI execute the application of deterrent methods.

[0072] The permission transmission unit can estimate human emotions and adjust the timing of sending permission to fire based on the estimated emotions. For example, if the person is tense, the permission transmission unit can delay sending permission to fire. If the person is relaxed, the permission transmission unit can speed up sending permission to fire. If the person is excited, the permission transmission unit can maintain a moderate level of sending permission to fire. This allows for sending permission to fire at an appropriate time by adjusting the timing of sending permission based on human emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the permission transmission unit may be performed using AI or not. For example, the permission transmission unit can input human emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The permit transmission unit can analyze the level of danger posed by wildlife and determine the priority of permits for firing when transmitting a permit. For example, the permit transmission unit will prioritize transmitting a permit if there is a high probability that wildlife will harm humans. If there is a high probability that wildlife will damage crops, the permit transmission unit may give a medium priority to the permit. Furthermore, if there is a low probability that wildlife will affect the natural environment, the permit transmission unit may give a low priority to the permit. In this way, the priority of permits for firing can be appropriately determined by analyzing the level of danger posed by wildlife. Some or all of the above processing in the permit transmission unit may be performed using AI or not. For example, the permit transmission unit can input wildlife danger data into a generating AI and have the generating AI perform the danger analysis.

[0074] The shotgun control unit can estimate human emotions and adjust the shotgun's control method based on the estimated emotions. For example, if the person is tense, the shotgun control unit will control the shotgun carefully. If the person is relaxed, the shotgun control unit will control the shotgun quickly. If the person is excited, the shotgun control unit will control the shotgun moderately. This allows for appropriate control by adjusting the shotgun's control method based on human emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the shotgun control unit may be performed using AI or not. For example, the shotgun control unit can input human emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The shotgun control unit can predict the movement of wildlife and optimize the timing of firing during shotgun control. For example, the shotgun control unit can predict the direction and speed of wildlife movement and fire at the optimal time. The shotgun control unit can predict where wildlife may stop and fire at that time. Furthermore, the shotgun control unit can consider the possibility of wildlife suddenly changing direction and adjust the timing of firing. In this way, the timing of firing can be optimized by predicting the movement of wildlife. Some or all of the above processing in the shotgun control unit may be performed using AI or not. For example, the shotgun control unit can input wildlife movement data into a generating AI and have the generating AI perform movement predictions.

[0076] The integration unit can estimate the processing capacity of the data center and adjust the integration method based on the estimated processing capacity. For example, if the data center has high processing capacity, the integration unit can perform complex integration operations. If the data center has low processing capacity, the integration unit can perform simple integration operations. Furthermore, if the data center has moderate processing capacity, the integration unit can perform appropriate integration operations. By adjusting the integration method based on the processing capacity of the data center, efficient integration becomes possible. Some or all of the above-described processes in the integration unit may be performed using AI or not. For example, the integration unit can input data center processing capacity data into a generating AI and have the generating AI perform processing capacity estimation.

[0077] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0078] The detection unit can detect the body temperature of wild animals and identify the species based on that temperature. For example, the detection unit can use an infrared sensor to detect the body temperature of wild animals and analyze the temperature data to identify species such as bears, wild boars, and deer. The detection unit can also monitor changes in body temperature in real time to understand the activity status of wild animals. Furthermore, by accumulating temperature data and comparing it with past data, the detection unit can predict the behavioral patterns of wild animals. As a result, by utilizing temperature data, it becomes possible to identify the species of wild animals and predict their behavior, enabling the implementation of more effective preventative measures.

[0079] The deterrent unit can repel wildlife by mimicking their calls. For example, it can repel other bears by mimicking the calls of bears. It can also repel wild boars by mimicking their calls. Furthermore, it can repel deer by mimicking the calls of deer. In this way, wildlife can be repelled in a natural manner by mimicking the calls of wildlife.

[0080] The authorization transmission unit can perform a safety check of the surroundings before transmitting permission to fire. For example, the authorization transmission unit can use cameras and sensors to check the surrounding situation and confirm that there are no humans or other animals. If there are humans or other animals in the vicinity, the authorization transmission unit can temporarily suspend the transmission of permission to fire. In addition, the authorization transmission unit can monitor the surrounding situation in real time and transmit permission to fire only when safety is confirmed. This allows for the transmission of permission to fire while ensuring the safety of the surroundings.

[0081] The shotgun control unit can use a combination of multiple sensors to accurately pinpoint the location of wildlife when firing. For example, it can combine a camera and an infrared sensor to simultaneously detect vision and heat. It can also combine an acoustic sensor and a vibration sensor to simultaneously detect sound and vibration. Furthermore, it can combine radar and lidar to simultaneously detect distance and shape. By combining multiple sensors, the shotgun control unit can accurately pinpoint the location of wildlife and improve firing accuracy.

[0082] The integration unit can use multiple communication methods in combination to ensure communication stability when integrating with a data center. For example, the integration unit can use both 5G and Wi-Fi communication to ensure communication stability. If 5G communication becomes unstable, the integration unit can switch to Wi-Fi communication. Furthermore, the integration unit can monitor the communication status in real time and select the optimal communication method. This ensures communication stability and enables smooth integration with the data center.

[0083] The detection unit can estimate the emotions of wildlife and adjust the timing of detection based on the estimated emotions. For example, if the wildlife is alert, the detection unit can increase its sensitivity to detect even minute movements. If the wildlife is relaxed, the detection unit can decrease its sensitivity to reduce false positives. Furthermore, if the wildlife is excited, the detection unit can maintain a moderate sensitivity and detect at the appropriate time. In this way, false positives can be reduced by adjusting the timing of detection based on the emotions of wildlife.

[0084] The deterrent unit can estimate the emotions of the wildlife and adjust its deterrent method based on those estimates. For example, if the wildlife is agitated, the deterrent unit can use strong sounds or lights to deter it. If the wildlife is relaxed, it can use gentle sounds or lights to deter it. If the wildlife is alert, it can use moderate sounds or lights to deter it. This allows for effective deterrence by adjusting the deterrent method based on the wildlife's emotions.

[0085] The authorization transmitter can estimate a person's emotions and adjust the timing of the firing authorization based on those emotions. For example, if the person is tense, the authorization transmitter can delay the firing authorization. If the person is relaxed, the authorization transmitter can speed up the firing authorization. If the person is excited, the authorization transmitter can maintain a moderate level of alertness. By adjusting the timing of the firing authorization based on the person's emotions, the system can transmit the firing authorization at the appropriate time.

[0086] The shotgun control unit can estimate human emotions and adjust the shotgun's control method based on those estimated emotions. For example, if the person is tense, the control unit will control the shotgun cautiously. If the person is relaxed, the control unit will control the shotgun quickly. If the person is excited, the control unit will control the shotgun moderately. This allows for appropriate control by adjusting the shotgun's control method based on human emotions.

[0087] The integration unit can estimate the processing capacity of the data center and adjust the integration method based on the estimated processing capacity. For example, if the data center has high processing capacity, the integration unit can perform complex integration operations. If the data center has low processing capacity, the integration unit can perform simple integration operations. Furthermore, if the data center has moderate processing capacity, the integration unit can perform appropriate integration operations. By adjusting the integration method based on the processing capacity of the data center, efficient integration becomes possible.

[0088] The following briefly describes the processing flow for example form 2.

[0089] Step 1: The detection unit detects wildlife. The detection unit detects wildlife in real time, for example, using a camera or sensor. The detection unit may include AI processing. Step 2: The deterrent unit deters the wildlife detected by the detection unit. The deterrent unit may, for example, emit sound or light to startle the wildlife. The deterrent unit may include AI processing. Step 3: The authorization transmission unit transmits a firing authorization to wildlife that is not driven away by the driving unit. The authorization transmission unit transmits the firing authorization to a human's smartphone, for example, via 5G communication. The authorization transmission unit may include AI processing. Step 4: The shotgun control unit controls the shotgun based on the firing permission transmitted by the permission transmission unit. The shotgun control unit may include AI processing. Step 5: The integration unit performs complex operations in conjunction with the data center. For example, the integration unit connects to the data center in real time using 5G communication and performs complex operations. The integration unit may include AI processing.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] Each of the multiple elements described above, including the detection unit, deterrent unit, permission transmission unit, gun control unit, and coordination unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the detection unit uses the camera 42 and sensors of the smart device 14 to detect wildlife in real time and the control unit 46A performs AI processing. The deterrent unit uses the output device 40 of the smart device 14 to emit sound and light to startle wildlife. The permission transmission unit uses the communication I / F 44 of the smart device 14 to transmit permission to fire via 5G communication. The gun control unit controls the gun based on the permission to fire using the specific processing unit 290 of the data processing unit 12. The coordination unit connects to a data center in real time using the communication I / F 26 of the data processing unit 12 and performs complex operations. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0094] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Each of the multiple elements described above, including the detection unit, deterrent unit, permission transmission unit, gun control unit, and coordination unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the detection unit uses the camera 42 and sensors of the smart glasses 214 to detect wildlife in real time and the control unit 46A performs AI processing. The deterrent unit uses the speaker 240 of the smart glasses 214 to emit sound and light to startle wildlife. The permission transmission unit uses the communication I / F 44 of the smart glasses 214 to transmit permission to fire via 5G communication. The gun control unit controls the gun based on the permission to fire using the specific processing unit 290 of the data processing unit 12. The coordination unit connects to a data center in real time using the communication I / F 26 of the data processing unit 12 and performs complex operations. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0110] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the detection unit, deterrent unit, permission transmission unit, gun control unit, and coordination unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the detection unit uses the camera 42 and sensors of the headset terminal 314 to detect wildlife in real time, and the control unit 46A performs AI processing. The deterrent unit uses the speaker 240 of the headset terminal 314 to emit sound and light to startle wildlife. The permission transmission unit uses the communication I / F 44 of the headset terminal 314 to transmit permission to fire via 5G communication. The gun control unit controls the gun based on the permission to fire using the specific processing unit 290 of the data processing unit 12. The coordination unit connects to a data center in real time using the communication I / F 26 of the data processing unit 12 and performs complex operations. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0126] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In 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.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 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.

[0142] Each of the multiple elements described above, including the detection unit, deterrent unit, permission transmission unit, gun control unit, and coordination unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the detection unit uses the camera 42 and sensors of the robot 414 to detect wildlife in real time and the control unit 46A performs AI processing. The deterrent unit uses the speaker 240 of the robot 414 to emit sound and light to startle wildlife. The permission transmission unit uses the communication I / F 44 of the robot 414 to transmit permission to fire via 5G communication. The gun control unit controls the gun based on the permission to fire using the specific processing unit 290 of the data processing unit 12. The coordination unit connects to a data center in real time using the communication I / F 26 of the data processing unit 12 and performs complex operations. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] (Note 1) A detection unit for detecting wildlife, A deterrent unit that drives away wild animals detected by the detection unit, A permit transmission unit that transmits permission to fire at wild animals that are not driven away by the aforementioned deterrent unit, A shotgun control unit that controls the shotgun based on the firing permission transmitted by the permission transmission unit, It includes a collaboration unit that performs complex operations in conjunction with the data center. A system characterized by the following features. (Note 2) The detection unit is Using cameras and sensors to detect wildlife in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned deterrent unit is, It emits sounds and lights to startle wildlife. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned permission transmission unit, Sending permission to fire to a human's smartphone via 5G communication. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned shotgun control unit, The hunting rifle is controlled based on the firing permission transmitted by the aforementioned permission transmission unit. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned linkage unit is, Using 5G communication, connect to data centers in real time and perform complex operations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The detection unit is It estimates the emotions of wild animals and adjusts the accuracy of detection based on the estimated emotions of the wild animals. The system described in Appendix 1, characterized by the features described herein. (Note 8) The detection unit is During detection, the behavioral patterns of the wildlife are analyzed to optimize the timing of detection. The system described in Appendix 1, characterized by the features described herein. (Note 9) The detection unit is During detection, multiple sensors are combined to improve detection accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned deterrent unit is, The system estimates the emotions of wild animals and adjusts the deterrent methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned deterrent unit is, When driving away animals, different methods of deterrence should be applied depending on the type of wildlife. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned permission transmission unit, It estimates human emotions and adjusts the timing of sending firing authorization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned permission transmission unit, When submitting a permit, the level of danger to wildlife is analyzed to determine the priority of permits for firing. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned shotgun control unit, It estimates human emotions and adjusts the hunting rifle's control method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned shotgun control unit, When controlling a hunting rifle, predict the movement of wildlife and optimize the timing of firing. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned linkage unit is, We estimate the processing capacity of the data center and adjust the collaboration method based on the estimated processing capacity. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A detection unit for detecting wildlife, A deterrent unit that drives away wild animals detected by the detection unit, A permit transmission unit that transmits permission to fire at wild animals that are not driven away by the aforementioned deterrent unit, A shotgun control unit that controls the shotgun based on the firing permission transmitted by the permission transmission unit, It includes a collaboration unit that performs complex operations in conjunction with the data center. A system characterized by the following features.

2. The detection unit is Using cameras and sensors to detect wildlife in real time. The system according to feature 1.

3. The aforementioned deterrent unit is, It emits sounds and lights to startle wildlife. The system according to feature 1.

4. The aforementioned permission transmission unit, Sending permission to fire to a human's smartphone via 5G communication. The system according to feature 1.

5. The aforementioned shotgun control unit, The hunting rifle is controlled based on the firing permission transmitted by the aforementioned permission transmission unit. The system according to feature 1.

6. The aforementioned linkage unit is, Using 5G communication, connect to data centers in real time and perform complex operations. The system according to feature 1.

7. The detection unit is It estimates the emotions of wildlife and adjusts the accuracy of detection based on the estimated emotions of the wildlife. The system according to feature 1.

8. The detection unit is During detection, the behavioral patterns of the wildlife are analyzed to optimize the timing of detection. The system according to feature 1.

9. The detection unit is During detection, multiple sensors are combined to improve detection accuracy. The system according to feature 1.

10. The aforementioned deterrent unit is, The system estimates the emotions of wild animals and adjusts the deterrent methods based on those estimated emotions. The system according to feature 1.

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