System

Abstract

An object of a system according to an embodiment is to efficiently identify a vermin from a monitoring camera video and support efficient operation of resources.SOLUTION: A system according to an embodiment includes a video analysis unit, a suspicious part specification unit, and a notification unit. The video analysis unit includes a generation AI. The suspicious portion specification unit specifies a suspicious portion in which a harmful animal appears from the monitoring camera video analyzed by the video analysis unit. The notification unit notifies a person of the suspicious portion specified by the suspicious portion specifying unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, identifying pests from surveillance camera footage was a time-consuming and labor-intensive process, making it difficult to manage resources efficiently.

[0005] The system according to the embodiment aims to efficiently identify harmful animals from surveillance camera footage and support efficient resource management. [Means for solving the problem]

[0006] The system according to the embodiment includes a video analysis unit, a suspicious portion identification unit, and a notification unit. The video analysis unit is equipped with a generation AI. The suspicious portion identification unit identifies suspicious portions in which vermin are captured from surveillance camera footage analyzed by the video analysis unit. The notification unit notifies a person of the suspicious portions identified by the suspicious portion identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently identify harmful animals from surveillance camera footage and support efficient resource management. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The pest discrimination system according to an embodiment of the present invention is a system that automatically discriminates pests such as bears, deer, and wild boars captured on surveillance cameras. In this system, a generation AI analyzes surveillance camera footage and determines whether a pest has been captured on camera. As a result, the pest discrimination system automatically detects the presence of a pest and notifies humans, enabling efficient surveillance.

[0029] A pest identification system according to an embodiment includes a video analysis unit, a suspicious area identification unit, and a notification unit. The video analysis unit is equipped with a generation AI and analyzes surveillance camera footage. For example, the generation AI receives video data acquired from a surveillance camera as input and identifies animals in the footage using a deep learning model that has learned the characteristics of pests. The generation AI can also analyze movement in the footage using motion detection technology to identify the presence of pests. The generation AI can also analyze the shape and movement of animals using an image recognition algorithm to identify pests. For example, the generation AI extracts shape features of animals in the footage and uses them to identify pests such as bears, deer, and wild boars. The suspicious area identification unit identifies suspicious areas in the surveillance camera footage analyzed by the video analysis unit that may contain pests. For example, the generation AI identifies areas that match the characteristics of pests and marks them as suspicious. The generation AI can also analyze animal movements and behavior patterns to identify suspicious areas. Furthermore, the generation AI can focus its analysis on specific areas within the video to identify suspicious areas. For example, the generation AI can analyze patterns of animal movement within the video to identify areas likely to contain pests. The notification unit notifies humans of suspicious areas identified by the suspicious area identification unit. For example, the generation AI can extract a video clip of the suspicious area and send it to humans as an alert. The generation AI can also analyze the video of the suspicious area in real time and provide immediate notification. Furthermore, the generation AI can analyze the video of the suspicious area from multiple angles to identify pests with greater accuracy. For example, the generation AI can integrate video captured from multiple cameras to identify the presence of pests with high accuracy. As a result, the pest identification system according to the embodiment can automatically identify the presence of pests and notify humans, enabling efficient surveillance. For example, the generation AI can update pest sighting information in real time, enabling immediate response. The generation AI can also analyze the frequency of pest sightings and optimize hunter patrol schedules. Furthermore, the generative AI can analyze the success rate of capturing pests and suggest effective capture methods.

[0030] The video analysis unit can analyze the shape or movement of an animal and identify it as a pest. For example, the generation AI synchronizes with surveillance camera footage to analyze audio data and identify specific cries and sounds. For example, it can detect the roar of a bear or the hoofbeats of a boar and identify the presence of a pest based on that. Furthermore, when analyzing audio data, the generation AI distinguishes between surrounding environmental sounds and animal cries and extracts specific cries. For example, it removes the sound of wind and rain to detect the cries of pests with high accuracy. Furthermore, the generation AI analyzes audio data in real time and issues an alert if a specific cry is detected. For example, it sends a notification the moment a bear roar is detected. This allows for highly accurate identification of pests by analyzing the shape and movement of animals.

[0031] The notification unit can extract video clips of suspicious parts and send them to humans as alerts. For example, the generation AI in the notification unit analyzes infrared camera footage to detect animal body temperatures. For example, it identifies pests based on the body temperatures of bears and wild boars. The notification unit also uses an infrared camera to detect animal body temperatures, even at night, to confirm the presence of pests. For example, it can identify pests that are active at night with high accuracy. The notification unit also analyzes infrared camera footage in real time and issues an alert if it detects a specific body temperature pattern. For example, it sends a notification the moment a bear's body temperature pattern is detected. This allows humans to efficiently check the suspicious parts by sending video clips of suspicious parts as alerts.

[0032] The video analysis unit analyzes animal cries or sounds and can identify pests based on the audio data. For example, the generation AI synchronizes with surveillance camera footage to analyze audio data and identify specific cries or sounds. For example, it detects the roaring of a bear or the hoofbeats of a boar and identifies the presence of pests based on that. Furthermore, when analyzing audio data, the generation AI distinguishes between surrounding environmental sounds and animal cries and extracts specific cries. For example, it removes the sound of wind and rain to detect the cries of pests with high accuracy. Furthermore, the generation AI analyzes audio data in real time and issues an alert if a specific cry is detected. For example, it notifies the user the moment a bear roar is detected. This allows pests to be identified by analyzing animal cries and sounds.

[0033] The video analysis unit can detect animal body temperatures and identify pests using an infrared camera. For example, the generation AI in the video analysis unit analyzes infrared camera footage to detect animal body temperatures. For example, it can identify pests based on the body temperatures of bears and wild boars. The video analysis unit also uses an infrared camera to detect animal body temperatures, even at night, and confirm the presence of pests. For example, it can identify pests that are active at night with high accuracy. The video analysis unit also analyzes infrared camera footage in real time and issues an alert if it detects a specific body temperature pattern. For example, it sends a notification the moment a bear's body temperature pattern is detected. This makes it possible to detect animal body temperatures and identify pests using an infrared camera.

[0034] The video analysis unit can analyze damage or changes to plants and indirectly identify the presence of pests. For example, the generation AI in the video analysis unit analyzes damage to plants in the video and detects traces of pest damage. For example, the presence of pests can be identified based on damage to leaves or branches. The video analysis unit also analyzes the growth state of plants, and the generation AI detects abnormal changes. For example, it analyzes the sudden withering of a particular plant and infers the presence of pests. The video analysis unit also analyzes changes in the arrangement and shape of plants in the video and identifies traces of pest passage. For example, it infers the presence of pests based on the appearance of plants falling over. In this way, the presence of pests can be indirectly identified by analyzing damage and changes to plants.

[0035] The video analysis unit can analyze weather or environmental conditions and predict pest appearance patterns. For example, the generation AI in the video analysis unit analyzes weather data in the video and identifies pest appearance patterns under specific weather conditions. For example, it predicts pests that appear on rainy days. The video analysis unit also analyzes environmental conditions and the generation AI predicts pest appearance patterns. For example, it identifies pest activity times based on changes in temperature and humidity. The video analysis unit also analyzes seasonal variations in the video and predicts pest appearance patterns in specific seasons. For example, it identifies pests that become more active in spring. In this way, pest appearance patterns can be predicted by analyzing weather and environmental conditions.

[0036] The suspicious part identification unit analyzes video of suspicious parts from multiple angles, enabling it to identify pests with high accuracy. For example, the generation AI acquires video of suspicious parts from multiple cameras and analyzes it from different angles. For example, it checks the same location from multiple viewpoints and identifies the presence of pests with high accuracy. The generation AI also converts the video of suspicious parts into a 3D model and analyzes it in three dimensions. For example, it grasps and identifies the shape and movement of pests in three dimensions. The generation AI also analyzes video of suspicious parts along a time axis and tracks the animal's movements. For example, it analyzes and identifies the movement patterns of pests. This allows it to identify pests with high accuracy by analyzing from multiple angles.

[0037] The suspicious part identification unit can compare video of suspicious parts with past data and analyze the frequency or pattern of appearance. For example, the generation AI compares video of suspicious parts with past surveillance camera data and analyzes the frequency of appearance. For example, it identifies the frequency of appearance of vermin in a specific location. The suspicious part identification unit also analyzes the appearance pattern of suspicious parts based on past data. For example, it identifies patterns of vermin that appear at specific times or seasons. The suspicious part identification unit also compares video of suspicious parts with past data and detects similar patterns. For example, it identifies based on patterns of vermin detected in the same location in the past. This allows the frequency or pattern of appearance to be analyzed by comparing with past data.

[0038] The suspicious area identification unit can link footage of suspicious areas with footage from other surveillance cameras and track the movements of pests over a wide area. For example, the generation AI of the suspicious area identification unit links footage of suspicious areas with footage from other surveillance cameras and tracks the movements of pests over a wide area. For example, the same pest is tracked using multiple cameras and its movement path is identified. The suspicious area identification unit also analyzes footage from other surveillance cameras and the generation AI tracks the movements of animals in suspicious areas. For example, it confirms their appearance in different locations and identifies the movement pattern of the pest. The suspicious area identification unit also links footage of suspicious areas with other cameras and the generation AI tracks the movements of the pest in real time. For example, it monitors with multiple cameras simultaneously and identifies the location of the pest. This makes it possible to track the movements of pests over a wide area by linking footage from other surveillance cameras.

[0039] The suspicious part identification unit can build a system that analyzes video of suspicious parts in real time and issues an immediate notification. For example, the suspicious part identification unit builds a system in which a generation AI analyzes video of suspicious parts in real time and issues an immediate notification. For example, an alert is issued the moment a vermin is detected. The suspicious part identification unit also uses a real-time analysis system to enable the generation AI to instantly analyze video of suspicious parts and issue a notification. For example, it tracks the movements of vermin in real time and issues a notification. The suspicious part identification unit also develops a system in which a generation AI analyzes video of suspicious parts in real time and issues an immediate notification. For example, an alert is issued the moment a vermin is captured on camera. This allows for real-time analysis and immediate notification, enabling a rapid response.

[0040] The notification unit can notify humans of the detailed analysis report provided by the generation AI, enabling them to make a quick decision. For example, the notification unit allows humans to review the detailed analysis report provided by the generation AI and make a quick decision. For example, the notification unit refers to a report including the type of pest and its location information. The notification unit also allows humans to confirm the presence of pests based on the analysis report generated by the generation AI. For example, the notification unit refers to a report including video clips and analysis results. The notification unit also allows humans to review the detailed analysis report provided by the generation AI and quickly decide on countermeasures. For example, the notification unit refers to a report including the behavior patterns and appearance times of pests. In this way, by notifying humans of the detailed analysis report, humans can make a quick decision.

[0041] The notification unit notifies humans of similar past cases provided by the generation AI, thereby improving the accuracy of judgment. For example, the notification unit allows humans to refer to similar past cases provided by the generation AI and improve the accuracy of judgment. For example, it refers to cases of pests being detected in the same location in the past. The notification unit also allows humans to confirm the information provided by the generation AI based on similar past cases. For example, it identifies the behavioral patterns of pests based on past data. The notification unit also allows humans to refer to similar past cases provided by the generation AI and quickly decide on countermeasures. For example, it selects the optimal countermeasure based on past cases. In this way, by notifying humans of similar past cases, the accuracy of human judgment is improved.

[0042] The notification unit allows humans to visually confirm the movements of pests using the 3D model provided by the generation AI. For example, the notification unit allows humans to check the 3D model provided by the generation AI and visually understand the movements of pests. For example, the notification unit displays the movement path of the pest in a 3D model. The notification unit also allows humans to check the movements of the pest based on the 3D model generated by the generation AI. For example, the notification unit visually displays the behavior patterns of the pest in a 3D model. The notification unit also allows humans to check the 3D model provided by the generation AI and quickly decide on countermeasures. For example, the location information of the pest is displayed in a 3D model and the optimal countermeasure is selected. In this way, the 3D model allows humans to visually confirm the movements of the pest.

[0043] The notification unit allows humans to efficiently carry out the confirmation work using the audio guide provided by the generation AI. For example, humans use the audio guide provided by the generation AI to efficiently carry out the confirmation work. For example, the location and type of vermin are announced by audio. The notification unit also uses the audio guide to allow humans to confirm the information provided by the generation AI. For example, the behavior patterns and appearance times of the vermin are announced by audio. The notification unit also allows humans to use the audio guide provided by the generation AI to quickly decide on countermeasures. For example, the location information of the vermin is announced by audio, and the optimal countermeasure is selected. In this way, the audio guide allows humans to efficiently carry out the confirmation work.

[0044] The video analysis unit analyzes the behavioral patterns of pests and can grasp seasonal variations. In the video analysis unit, for example, the generation AI analyzes the behavioral patterns of pests and grasps seasonal variations. For example, it identifies behavioral patterns that differ between spring and autumn. In addition, the generation AI analyzes the behavioral patterns of pests based on seasonal data. For example, it identifies behavioral changes before and after hibernation. In addition, the generation AI analyzes the behavioral patterns of pests and grasps the periods when pests appear. For example, it identifies the breeding season and the period when they search for food. In this way, seasonal variations can be grasped by analyzing behavioral patterns.

[0045] The video analysis unit can analyze the feeding patterns of pests and understand their impact on the food chain. For example, the generation AI in the video analysis unit analyzes the feeding patterns of pests and understands their impact on the food chain. For example, it identifies patterns of eating specific plants and animals. The generation AI in the video analysis unit also analyzes the impact of pests on the ecosystem based on the feeding patterns. For example, it identifies the impact if a specific food source decreases. The generation AI in the video analysis unit also analyzes the feeding patterns of pests and understands fluctuations in the food chain. For example, it identifies the impact on animals higher and lower in the food chain. In this way, by analyzing feeding patterns, it is possible to understand the impact of the food chain.

[0046] The video analysis unit can analyze the movement routes of pests and track changes in their habitats. In the video analysis unit, for example, the generation AI analyzes the movement routes of pests and tracks changes in their habitats. For example, it identifies seasonal movement patterns. In addition, the generation AI analyzes changes in the pests' habitats based on their movement routes. For example, it identifies breeding grounds and places where they search for food. In addition, the video analysis unit can analyze the movement routes of pests and track changes in their habitats. For example, it identifies changes in habitat due to environmental changes. In this way, it is possible to track changes in habitats by analyzing their movement routes.

[0047] The video analysis unit can analyze the breeding patterns of pests and predict changes in the population. In the video analysis unit, for example, the generation AI analyzes the breeding patterns of pests and predicts changes in the population. For example, it identifies the breeding season and the time of birth. The video analysis unit also uses the generation AI to predict changes in the population based on the breeding patterns. For example, it identifies the breeding success rate and the survival rate of offspring. The video analysis unit also uses the generation AI to analyze the breeding patterns of pests and predict changes in the population. For example, it identifies the effects of environmental conditions and food supply. In this way, by analyzing the breeding patterns, it is possible to predict changes in the population.

[0048] The notification unit allows the generation AI to analyze the frequency of pest animal appearances and optimize the hunter's patrol schedule. For example, the notification unit allows the generation AI to analyze the frequency of pest animal appearances and optimize the hunter's patrol schedule. For example, it may focus patrols on areas with high appearance frequencies. The notification unit also allows the generation AI to optimize the hunter's patrol schedule based on the appearance frequency. For example, it may concentrate patrols during specific time periods. The notification unit also allows the generation AI to analyze the frequency of pest animal appearances and optimize the hunter's patrol schedule. For example, it may adjust the schedule based on seasonal appearance patterns. In this way, the hunter's patrol schedule can be optimized by analyzing the appearance frequency.

[0049] The notification unit allows the generation AI to analyze the success rate of pest capture and suggest effective capture methods. For example, the notification unit allows the generation AI to analyze the success rate of pest capture and suggest effective capture methods. For example, it may suggest specific traps or capture techniques. The notification unit also allows the generation AI to suggest effective capture methods based on the capture success rate. For example, it may suggest capture methods at specific times or locations. The notification unit also allows the generation AI to analyze the success rate of pest capture and suggest effective capture methods. For example, it may suggest capture methods based on environmental conditions and the behavioral patterns of pests. In this way, effective capture methods can be suggested by analyzing the capture success rate.

[0050] The notification unit allows the generation AI to share information about pest sightings with other regions and take wide-area countermeasures. For example, the notification unit allows the generation AI to share information about pest sightings with other regions and take wide-area countermeasures. For example, information is shared with neighboring regions and countermeasures are implemented in cooperation. Furthermore, the notification unit allows the generation AI to work with other regions based on the sighting information and take wide-area countermeasures. For example, a patrol plan is made over a wide area. Furthermore, the notification unit allows the generation AI to share information about pest sightings with other regions and take wide-area countermeasures. For example, information is exchanged between regions and effective countermeasures are implemented. In this way, wide-area countermeasures can be taken by sharing sighting information with other regions.

[0051] The notification unit allows the generation AI to update the pest infestation information in real time, enabling an immediate response. The notification unit, for example, allows the generation AI to update the pest infestation information in real time, enabling an immediate response. For example, the information is updated the moment a pest is detected and a hunter is notified. The notification unit also uses a real-time update system to allow the generation AI to immediately update the pest infestation information and take countermeasures. For example, the movement of pests is tracked in real time and the information is updated. The notification unit also allows the generation AI to update the pest infestation information in real time, enabling an immediate response. For example, the information is updated the moment a pest is captured on camera and a hunter is notified. This allows the infestation information to be updated in real time, enabling an immediate response.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The video analysis unit can analyze the behavioral patterns of pests and understand seasonal variations. For example, the generation AI can analyze the behavioral patterns of pests and identify the different behavioral patterns in spring and autumn. The generation AI can also analyze the behavioral patterns of pests based on seasonal data and identify behavioral changes before and after hibernation. Furthermore, the generation AI can analyze seasonal behavioral patterns and identify the periods when pests appear. For example, it can identify the breeding season and the period when they search for food. In this way, seasonal variations can be understood by analyzing behavioral patterns.

[0054] The video analysis unit can analyze the feeding patterns of pests to understand their impact on the food chain. For example, the generation AI can analyze the feeding patterns of pests and identify patterns in which they eat specific plants and animals. Based on the feeding patterns, the generation AI can also analyze the impact of pests on the ecosystem. For example, it can identify the impact if a specific food source decreases. Furthermore, the generation AI can analyze the feeding patterns of pests to understand changes in the food chain. For example, it can identify the impact on animals higher and lower in the food chain. In this way, by analyzing feeding patterns, it is possible to understand the impact of the food chain.

[0055] The video analysis unit can analyze the movement routes of pests and track changes in their habitats. For example, the generation AI can analyze the movement routes of pests and identify seasonal movement patterns. The generation AI can also analyze changes in the pests' habitats based on their movement routes. For example, it can identify breeding grounds and food-hunting locations. Furthermore, the generation AI can analyze the movement routes of pests and track changes in their habitats. For example, it can identify habitat shifts due to environmental changes. In this way, it is possible to track changes in habitats by analyzing their movement routes.

[0056] The notification unit allows the generation AI to analyze the frequency of pest animal sightings and optimize the hunter's patrol schedule. For example, the generation AI can analyze the frequency of pest animal sightings and focus patrols on areas with high sighting frequency. The generation AI can also optimize the hunter's patrol schedule based on sighting frequency. For example, it can concentrate patrols during specific time periods. Furthermore, the generation AI can analyze the frequency of pest animal sightings and adjust the schedule based on seasonal sighting patterns. In this way, the hunter's patrol schedule can be optimized by analyzing sighting frequency.

[0057] The notification unit allows the generation AI to analyze the success rate of pest capture and suggest effective capture methods. For example, the generation AI can analyze the success rate of pest capture and suggest specific traps or capture techniques. The generation AI can also suggest effective capture methods based on the capture success rate. For example, it can suggest capture methods for specific times of day or locations. Furthermore, the generation AI can analyze the success rate of pest capture and suggest capture methods based on environmental conditions and the behavioral patterns of the pest. In this way, effective capture methods can be suggested by analyzing the capture success rate.

[0058] The notification unit allows the generation AI to share information about pest sightings with other regions, enabling wide-area countermeasures to be taken. For example, the generation AI can share information about pest sightings with other regions, share the information with neighboring regions, and work together to implement countermeasures. Furthermore, based on the sighting information, the generation AI can also work with other regions to implement wide-area countermeasures. For example, it can create a wide-area patrol plan. Furthermore, the generation AI can share information about pest sightings with other regions, exchange information between regions, and implement effective countermeasures. This allows wide-area countermeasures to be taken by sharing sighting information with other regions.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The video analysis unit is equipped with a generative AI and analyzes surveillance camera footage. The generative AI receives video data from the surveillance camera as input and identifies animals in the footage using a deep learning model that has learned the characteristics of pests. It also uses motion detection technology and image recognition algorithms to analyze the shape and movement of animals and identify them as pests. Step 2: The suspicious area identification unit identifies suspicious areas from the surveillance camera footage analyzed by the video analysis unit where pests may be caught. The generation AI identifies areas that match the characteristics of pests and marks them as suspicious. It can also analyze animal movements and behavior patterns to identify suspicious areas. Step 3: The notification unit notifies humans of the suspicious parts identified by the suspicious part identification unit. The generation AI extracts video clips of suspicious parts and sends them to humans as alerts. It can also analyze suspicious parts in real time and send immediate notifications.

[0061] (Example 2) The pest discrimination system according to an embodiment of the present invention is a system that automatically discriminates pests such as bears, deer, and wild boars captured on surveillance cameras. In this system, a generation AI analyzes surveillance camera footage and determines whether a pest has been captured on camera. As a result, the pest discrimination system automatically detects the presence of a pest and notifies humans, enabling efficient surveillance.

[0062] A pest identification system according to an embodiment includes a video analysis unit, a suspicious area identification unit, and a notification unit. The video analysis unit is equipped with a generation AI and analyzes surveillance camera footage. For example, the generation AI receives video data acquired from a surveillance camera as input and identifies animals in the footage using a deep learning model that has learned the characteristics of pests. The generation AI can also analyze movement in the footage using motion detection technology to identify the presence of pests. The generation AI can also analyze the shape and movement of animals using an image recognition algorithm to identify pests. For example, the generation AI extracts shape features of animals in the footage and uses them to identify pests such as bears, deer, and wild boars. The suspicious area identification unit identifies suspicious areas in the surveillance camera footage analyzed by the video analysis unit that may contain pests. For example, the generation AI identifies areas that match the characteristics of pests and marks them as suspicious. The generation AI can also analyze animal movements and behavior patterns to identify suspicious areas. Furthermore, the generation AI can focus its analysis on specific areas within the video to identify suspicious areas. For example, the generation AI can analyze patterns of animal movement within the video to identify areas likely to contain pests. The notification unit notifies humans of suspicious areas identified by the suspicious area identification unit. For example, the generation AI can extract a video clip of the suspicious area and send it to humans as an alert. The generation AI can also analyze the video of the suspicious area in real time and provide immediate notification. Furthermore, the generation AI can analyze the video of the suspicious area from multiple angles to identify pests with greater accuracy. For example, the generation AI can integrate video captured from multiple cameras to identify the presence of pests with high accuracy. As a result, the pest identification system according to the embodiment can automatically identify the presence of pests and notify humans, enabling efficient surveillance. For example, the generation AI can update pest sighting information in real time, enabling immediate response. The generation AI can also analyze the frequency of pest sightings and optimize hunter patrol schedules. Furthermore, the generative AI can analyze the success rate of capturing pests and suggest effective capture methods.

[0063] The video analysis unit can analyze the shape or movement of an animal and identify it as a pest. For example, the generation AI synchronizes with surveillance camera footage to analyze audio data and identify specific cries and sounds. For example, it can detect the roar of a bear or the hoofbeats of a boar and identify the presence of a pest based on that. Furthermore, when analyzing audio data, the generation AI distinguishes between surrounding environmental sounds and animal cries and extracts specific cries. For example, it removes the sound of wind and rain to detect the cries of pests with high accuracy. Furthermore, the generation AI analyzes audio data in real time and issues an alert if a specific cry is detected. For example, it sends a notification the moment a bear roar is detected. This allows for highly accurate identification of pests by analyzing the shape and movement of animals.

[0064] The notification unit can extract video clips of suspicious parts and send them to humans as alerts. For example, the generation AI in the notification unit analyzes infrared camera footage to detect animal body temperatures. For example, it identifies pests based on the body temperatures of bears and wild boars. The notification unit also uses an infrared camera to detect animal body temperatures, even at night, to confirm the presence of pests. For example, it can identify pests that are active at night with high accuracy. The notification unit also analyzes infrared camera footage in real time and issues an alert if it detects a specific body temperature pattern. For example, it sends a notification the moment a bear's body temperature pattern is detected. This allows humans to efficiently check the suspicious parts by sending video clips of suspicious parts as alerts.

[0065] The video analysis unit analyzes animal cries or sounds and can identify pests based on the audio data. For example, the generation AI synchronizes with surveillance camera footage to analyze audio data and identify specific cries or sounds. For example, it detects the roaring of a bear or the hoofbeats of a boar and identifies the presence of pests based on that. Furthermore, when analyzing audio data, the generation AI distinguishes between surrounding environmental sounds and animal cries and extracts specific cries. For example, it removes the sound of wind and rain to detect the cries of pests with high accuracy. Furthermore, the generation AI analyzes audio data in real time and issues an alert if a specific cry is detected. For example, it notifies the user the moment a bear roar is detected. This allows pests to be identified by analyzing animal cries and sounds.

[0066] The video analysis unit can detect animal body temperatures and identify pests using an infrared camera. For example, the generation AI in the video analysis unit analyzes infrared camera footage to detect animal body temperatures. For example, it can identify pests based on the body temperatures of bears and wild boars. The video analysis unit also uses an infrared camera to detect animal body temperatures, even at night, and confirm the presence of pests. For example, it can identify pests that are active at night with high accuracy. The video analysis unit also analyzes infrared camera footage in real time and issues an alert if it detects a specific body temperature pattern. For example, it sends a notification the moment a bear's body temperature pattern is detected. This makes it possible to detect animal body temperatures and identify pests using an infrared camera.

[0067] The video analysis unit uses the emotion estimation function to analyze the behavior or reactions of surrounding animals when a pest is captured on camera, thereby inferring the presence of a pest. For example, the generation AI in the video analysis unit analyzes the behavior of animals in the video and detects the reactions of surrounding animals when a pest is captured on camera. For example, it analyzes the behavior of other animals fleeing to infer the presence of a pest. The video analysis unit also uses the emotion estimation function to analyze the stress responses of surrounding animals and identify the presence of a pest. For example, it identifies pests based on the animal's heart rate and behavior patterns. The video analysis unit also uses the generation AI to analyze the emotional state of animals in the video and detect abnormal behavior when a pest is captured on camera. For example, it analyzes the animal's alert behavior to infer the presence of a pest. This makes it possible to infer the presence of a pest by analyzing the behavior and reactions of surrounding animals.

[0068] The video analysis unit can analyze damage or changes to plants and indirectly identify the presence of pests. For example, the generation AI in the video analysis unit analyzes damage to plants in the video and detects traces of pest damage. For example, the presence of pests can be identified based on damage to leaves or branches. The video analysis unit also analyzes the growth state of plants, and the generation AI detects abnormal changes. For example, it analyzes the sudden withering of a particular plant and infers the presence of pests. The video analysis unit also analyzes changes in the arrangement and shape of plants in the video and identifies traces of pest passage. For example, it infers the presence of pests based on the appearance of plants falling over. In this way, the presence of pests can be indirectly identified by analyzing damage and changes to plants.

[0069] The video analysis unit can analyze weather or environmental conditions and predict pest appearance patterns. For example, the generation AI in the video analysis unit analyzes weather data in the video and identifies pest appearance patterns under specific weather conditions. For example, it predicts pests that appear on rainy days. The video analysis unit also analyzes environmental conditions and the generation AI predicts pest appearance patterns. For example, it identifies pest activity times based on changes in temperature and humidity. The video analysis unit also analyzes seasonal variations in the video and predicts pest appearance patterns in specific seasons. For example, it identifies pests that become more active in spring. In this way, pest appearance patterns can be predicted by analyzing weather and environmental conditions.

[0070] The video analysis unit uses the emotion estimation function to analyze human emotional reactions when a pest is captured on video, thereby inferring the presence of a pest. For example, the generation AI in the video analysis unit analyzes the facial expressions of humans in the video to detect emotional reactions when a pest is captured on video. For example, the presence of a pest is identified based on a human's surprised expression. The video analysis unit also uses the emotion estimation function to analyze the tone of human voices to identify emotional reactions when a pest is captured on video. For example, the presence of a pest is inferred based on voices of surprise or fear. The video analysis unit also analyzes the behavior of humans in the video to detect abnormal behavior when a pest is captured on video. For example, the presence of a pest is inferred based on the way a human suddenly runs away. In this way, the presence of a pest can be inferred by analyzing human emotional reactions.

[0071] The suspicious part identification unit analyzes video of suspicious parts from multiple angles, enabling it to identify pests with high accuracy. For example, the generation AI acquires video of suspicious parts from multiple cameras and analyzes it from different angles. For example, it checks the same location from multiple viewpoints and identifies the presence of pests with high accuracy. The generation AI also converts the video of suspicious parts into a 3D model and analyzes it in three dimensions. For example, it grasps and identifies the shape and movement of pests in three dimensions. The generation AI also analyzes video of suspicious parts along a time axis and tracks the animal's movements. For example, it analyzes and identifies the movement patterns of pests. This allows it to identify pests with high accuracy by analyzing from multiple angles.

[0072] The suspicious part identification unit can compare video of suspicious parts with past data and analyze the frequency or pattern of appearance. For example, the generation AI compares video of suspicious parts with past surveillance camera data and analyzes the frequency of appearance. For example, it identifies the frequency of appearance of vermin in a specific location. The suspicious part identification unit also analyzes the appearance pattern of suspicious parts based on past data. For example, it identifies patterns of vermin that appear at specific times or seasons. The suspicious part identification unit also compares video of suspicious parts with past data and detects similar patterns. For example, it identifies based on patterns of vermin detected in the same location in the past. This allows the frequency or pattern of appearance to be analyzed by comparing with past data.

[0073] The suspicious part identification unit uses the emotion estimation function to analyze human emotional reactions to video of suspicious parts and can infer the presence of pests. For example, the suspicious part identification unit uses the emotion estimation function to analyze human vocal tones in response to video of suspicious parts. For example, it can identify the presence of pests based on a human's surprised facial expression. The suspicious part identification unit also uses the emotion estimation function to analyze human vocal tones in response to video of suspicious parts. For example, it can infer the presence of pests based on voices of surprise or fear. The suspicious part identification unit also uses the emotion estimation function to analyze video of suspicious parts and detect human behavior. For example, it can identify the presence of pests based on the way a human suddenly runs away. In this way, the presence of pests can be inferred by analyzing human emotional reactions.

[0074] The suspicious area identification unit can link footage of suspicious areas with footage from other surveillance cameras and track the movements of pests over a wide area. For example, the generation AI of the suspicious area identification unit links footage of suspicious areas with footage from other surveillance cameras and tracks the movements of pests over a wide area. For example, the same pest is tracked using multiple cameras and its movement path is identified. The suspicious area identification unit also analyzes footage from other surveillance cameras and the generation AI tracks the movements of animals in suspicious areas. For example, it confirms their appearance in different locations and identifies the movement pattern of the pest. The suspicious area identification unit also links footage of suspicious areas with other cameras and the generation AI tracks the movements of the pest in real time. For example, it monitors with multiple cameras simultaneously and identifies the location of the pest. This makes it possible to track the movements of pests over a wide area by linking footage from other surveillance cameras.

[0075] The suspicious part identification unit can build a system that analyzes video of suspicious parts in real time and issues an immediate notification. For example, the suspicious part identification unit builds a system in which a generation AI analyzes video of suspicious parts in real time and issues an immediate notification. For example, an alert is issued the moment a vermin is detected. The suspicious part identification unit also uses a real-time analysis system to enable the generation AI to instantly analyze video of suspicious parts and issue a notification. For example, it tracks the movements of vermin in real time and issues a notification. The suspicious part identification unit also develops a system in which a generation AI analyzes video of suspicious parts in real time and issues an immediate notification. For example, an alert is issued the moment a vermin is captured on camera. This allows for real-time analysis and immediate notification, enabling a rapid response.

[0076] The suspicious part identification unit uses the emotion estimation function to analyze the emotional reactions of other animals to video of suspicious parts, and can infer the presence of pests. For example, the generation AI of the suspicious part identification unit analyzes video of suspicious parts and detects the emotional reactions of other animals. For example, it identifies the presence of pests based on how other animals run away. The suspicious part identification unit also uses the emotion estimation function to analyze the behavior of other animals in response to video of suspicious parts. For example, it infers the presence of pests based on how other animals become wary. The suspicious part identification unit also uses the generation AI of the suspicious part identification unit to detect abnormal behavior of other animals. For example, it identifies the presence of pests based on how other animals suddenly run away. In this way, the presence of pests can be inferred by analyzing the emotional reactions of other animals.

[0077] The notification unit can notify humans of the detailed analysis report provided by the generation AI, enabling them to make a quick decision. For example, the notification unit allows humans to review the detailed analysis report provided by the generation AI and make a quick decision. For example, the notification unit refers to a report including the type of pest and its location information. The notification unit also allows humans to confirm the presence of pests based on the analysis report generated by the generation AI. For example, the notification unit refers to a report including video clips and analysis results. The notification unit also allows humans to review the detailed analysis report provided by the generation AI and quickly decide on countermeasures. For example, the notification unit refers to a report including the behavior patterns and appearance times of pests. In this way, by notifying humans of the detailed analysis report, humans can make a quick decision.

[0078] The notification unit notifies humans of similar past cases provided by the generation AI, thereby improving the accuracy of judgment. For example, the notification unit allows humans to refer to similar past cases provided by the generation AI and improve the accuracy of judgment. For example, it refers to cases of pests being detected in the same location in the past. The notification unit also allows humans to confirm the information provided by the generation AI based on similar past cases. For example, it identifies the behavioral patterns of pests based on past data. The notification unit also allows humans to refer to similar past cases provided by the generation AI and quickly decide on countermeasures. For example, it selects the optimal countermeasure based on past cases. In this way, by notifying humans of similar past cases, the accuracy of human judgment is improved.

[0079] The notification unit can use the emotion estimation function to monitor the emotional state of the human when checking and provide support to reduce stress. For example, the notification unit uses the emotion estimation function to monitor the emotional state of the human when checking and provide support to reduce stress. For example, it provides advice on how to relax when stress increases. The notification unit also uses the generation AI to analyze the emotional state of the human in real time and provide support to reduce stress. For example, it suggests a relaxation method according to the emotional state. The notification unit also uses the emotion estimation function to monitor the emotional state of the human when checking and provide feedback to reduce stress. For example, it plays relaxing music according to the emotional state. In this way, the burden on the human can be reduced by monitoring the emotional state and providing support to reduce stress.

[0080] The notification unit allows humans to visually confirm the movements of pests using the 3D model provided by the generation AI. For example, the notification unit allows humans to check the 3D model provided by the generation AI and visually understand the movements of pests. For example, the notification unit displays the movement path of the pest in a 3D model. The notification unit also allows humans to check the movements of the pest based on the 3D model generated by the generation AI. For example, the notification unit visually displays the behavior patterns of the pest in a 3D model. The notification unit also allows humans to check the 3D model provided by the generation AI and quickly decide on countermeasures. For example, the location information of the pest is displayed in a 3D model and the optimal countermeasure is selected. In this way, the 3D model allows humans to visually confirm the movements of the pest.

[0081] The notification unit allows humans to efficiently carry out the confirmation work using the audio guide provided by the generation AI. For example, humans use the audio guide provided by the generation AI to efficiently carry out the confirmation work. For example, the location and type of vermin are announced by audio. The notification unit also uses the audio guide to allow humans to confirm the information provided by the generation AI. For example, the behavior patterns and appearance times of the vermin are announced by audio. The notification unit also allows humans to use the audio guide provided by the generation AI to quickly decide on countermeasures. For example, the location information of the vermin is announced by audio, and the optimal countermeasure is selected. In this way, the audio guide allows humans to efficiently carry out the confirmation work.

[0082] The notification unit can use the emotion estimation function to analyze the emotional state of the person when checking and provide a notification at the optimal timing. The notification unit, for example, uses the emotion estimation function to analyze the emotional state of the person when checking and provide a notification at the optimal timing. For example, a notification is provided at a timing when stress is low. The notification unit also uses the generation AI to analyze the emotional state of the person in real time and provide a notification at the optimal timing. For example, a notification is provided at a timing according to the emotional state. The notification unit also uses the emotion estimation function to analyze the emotional state of the person when checking and provide a notification to reduce stress. For example, a relaxation method according to the emotional state is suggested. In this way, the burden on the person can be reduced by analyzing the emotional state and providing a notification at the optimal timing.

[0083] The video analysis unit analyzes the behavioral patterns of pests and can grasp seasonal variations. In the video analysis unit, for example, the generation AI analyzes the behavioral patterns of pests and grasps seasonal variations. For example, it identifies behavioral patterns that differ between spring and autumn. In addition, the generation AI analyzes the behavioral patterns of pests based on seasonal data. For example, it identifies behavioral changes before and after hibernation. In addition, the generation AI analyzes the behavioral patterns of pests and grasps the periods when pests appear. For example, it identifies the breeding season and the period when they search for food. In this way, seasonal variations can be grasped by analyzing behavioral patterns.

[0084] The video analysis unit can analyze the feeding patterns of pests and understand their impact on the food chain. For example, the generation AI in the video analysis unit analyzes the feeding patterns of pests and understands their impact on the food chain. For example, it identifies patterns of eating specific plants and animals. The generation AI in the video analysis unit also analyzes the impact of pests on the ecosystem based on the feeding patterns. For example, it identifies the impact if a specific food source decreases. The generation AI in the video analysis unit also analyzes the feeding patterns of pests and understands fluctuations in the food chain. For example, it identifies the impact on animals higher and lower in the food chain. In this way, by analyzing feeding patterns, it is possible to understand the impact of the food chain.

[0085] The video analysis unit uses the emotion estimation function to analyze the emotional reactions of other animals to the behavior of pests, making it possible to understand interactions in the ecosystem. For example, the video analysis unit uses the emotion estimation function to have the generation AI analyze the emotional reactions of other animals to the behavior of pests. For example, it understands interactions based on the wariness of other animals. The video analysis unit also has the generation AI analyze the emotional reactions of other animals and identify interactions in the ecosystem. For example, it analyzes behavioral changes in other animals due to the appearance of a pest. The video analysis unit also uses the emotion estimation function to have the generation AI analyze the emotional reactions of other animals and understand the balance of the ecosystem. For example, it identifies the impact that the behavior of a pest has on other animals. In this way, it is possible to understand interactions in the ecosystem by analyzing the emotional reactions of other animals.

[0086] The video analysis unit can analyze the movement routes of pests and track changes in their habitats. In the video analysis unit, for example, the generation AI analyzes the movement routes of pests and tracks changes in their habitats. For example, it identifies seasonal movement patterns. In addition, the generation AI analyzes changes in the pests' habitats based on their movement routes. For example, it identifies breeding grounds and places where they search for food. In addition, the video analysis unit can analyze the movement routes of pests and track changes in their habitats. For example, it identifies changes in habitat due to environmental changes. In this way, it is possible to track changes in habitats by analyzing their movement routes.

[0087] The video analysis unit can analyze the breeding patterns of pests and predict changes in the population. In the video analysis unit, for example, the generation AI analyzes the breeding patterns of pests and predicts changes in the population. For example, it identifies the breeding season and the time of birth. The video analysis unit also uses the generation AI to predict changes in the population based on the breeding patterns. For example, it identifies the breeding success rate and the survival rate of offspring. The video analysis unit also uses the generation AI to analyze the breeding patterns of pests and predict changes in the population. For example, it identifies the effects of environmental conditions and food supply. In this way, by analyzing the breeding patterns, it is possible to predict changes in the population.

[0088] The video analysis unit uses the emotion estimation function to analyze human emotional responses to the behavior of pests and can propose measures for coexistence. For example, the video analysis unit uses the emotion estimation function to have the generation AI analyze human emotional responses to the behavior of pests. For example, it identifies behaviors that humans find frightening. The video analysis unit also has the generation AI analyze human emotional responses and propose measures for coexistence. For example, it proposes safety measures for places where pests appear. The video analysis unit also uses the emotion estimation function to have the generation AI analyze human emotional responses and propose educational programs for coexistence. For example, it teaches people how to interact safely with pests. In this way, by analyzing human emotional responses, it is possible to propose measures for coexistence.

[0089] The notification unit allows the generation AI to analyze the frequency of pest animal appearances and optimize the hunter's patrol schedule. For example, the notification unit allows the generation AI to analyze the frequency of pest animal appearances and optimize the hunter's patrol schedule. For example, it may focus patrols on areas with high appearance frequencies. The notification unit also allows the generation AI to optimize the hunter's patrol schedule based on the appearance frequency. For example, it may concentrate patrols during specific time periods. The notification unit also allows the generation AI to analyze the frequency of pest animal appearances and optimize the hunter's patrol schedule. For example, it may adjust the schedule based on seasonal appearance patterns. In this way, the hunter's patrol schedule can be optimized by analyzing the appearance frequency.

[0090] The notification unit allows the generation AI to analyze the success rate of pest capture and suggest effective capture methods. For example, the notification unit allows the generation AI to analyze the success rate of pest capture and suggest effective capture methods. For example, it may suggest specific traps or capture techniques. The notification unit also allows the generation AI to suggest effective capture methods based on the capture success rate. For example, it may suggest capture methods at specific times or locations. The notification unit also allows the generation AI to analyze the success rate of pest capture and suggest effective capture methods. For example, it may suggest capture methods based on environmental conditions and the behavioral patterns of pests. In this way, effective capture methods can be suggested by analyzing the capture success rate.

[0091] The notification unit can use the emotion estimation function to monitor the hunter's emotional state and provide support to reduce stress. For example, the notification unit uses the emotion estimation function to monitor the hunter's emotional state and provide support to reduce stress. For example, the notification unit provides advice on how to relax when stress increases. The notification unit also uses the generation AI to analyze the hunter's emotional state in real time and provide support to reduce stress. For example, the notification unit suggests relaxation methods according to the emotional state. The notification unit also uses the emotion estimation function to monitor the hunter's emotional state and provide feedback to reduce stress. For example, the notification unit plays relaxing music according to the emotional state. This monitoring of the emotional state and providing support to reduce stress can reduce the burden on the hunter.

[0092] The notification unit allows the generation AI to share information about pest sightings with other regions and take wide-area countermeasures. For example, the notification unit allows the generation AI to share information about pest sightings with other regions and take wide-area countermeasures. For example, information is shared with neighboring regions and countermeasures are implemented in cooperation. Furthermore, the notification unit allows the generation AI to work with other regions based on the sighting information and take wide-area countermeasures. For example, a patrol plan is made over a wide area. Furthermore, the notification unit allows the generation AI to share information about pest sightings with other regions and take wide-area countermeasures. For example, information is exchanged between regions and effective countermeasures are implemented. In this way, wide-area countermeasures can be taken by sharing sighting information with other regions.

[0093] The notification unit allows the generation AI to update the pest infestation information in real time, enabling an immediate response. The notification unit, for example, allows the generation AI to update the pest infestation information in real time, enabling an immediate response. For example, the information is updated the moment a pest is detected and a hunter is notified. The notification unit also uses a real-time update system to allow the generation AI to immediately update the pest infestation information and take countermeasures. For example, the movement of pests is tracked in real time and the information is updated. The notification unit also allows the generation AI to update the pest infestation information in real time, enabling an immediate response. For example, the information is updated the moment a pest is captured on camera and a hunter is notified. This allows the infestation information to be updated in real time, enabling an immediate response.

[0094] The notification unit can use the emotion estimation function to analyze the emotional state of the hunter and issue a dispatch command at the optimal timing. The notification unit, for example, uses the emotion estimation function to analyze the emotional state of the hunter and issue a dispatch command at the optimal timing. For example, the notification unit issues a dispatch command at a timing when stress is low. The notification unit also uses the generation AI to analyze the emotional state of the hunter in real time and issue a dispatch command at the optimal timing. For example, the notification unit issues a dispatch command at a timing according to the emotional state. The notification unit also uses the emotion estimation function to analyze the emotional state of the hunter and issue a dispatch command to reduce stress. For example, the notification unit suggests a relaxation method according to the emotional state. In this way, the burden on the hunter can be reduced by analyzing the emotional state and issuing a dispatch command at the optimal timing.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The video analysis unit can analyze the behavioral patterns of pests and understand seasonal variations. For example, the generation AI can analyze the behavioral patterns of pests and identify the different behavioral patterns in spring and autumn. The generation AI can also analyze the behavioral patterns of pests based on seasonal data and identify behavioral changes before and after hibernation. Furthermore, the generation AI can analyze seasonal behavioral patterns and identify the periods when pests appear. For example, it can identify the breeding season and the period when they search for food. In this way, seasonal variations can be understood by analyzing behavioral patterns.

[0097] The video analysis unit can analyze the feeding patterns of pests to understand their impact on the food chain. For example, the generation AI can analyze the feeding patterns of pests and identify patterns in which they eat specific plants and animals. Based on the feeding patterns, the generation AI can also analyze the impact of pests on the ecosystem. For example, it can identify the impact if a specific food source decreases. Furthermore, the generation AI can analyze the feeding patterns of pests to understand changes in the food chain. For example, it can identify the impact on animals higher and lower in the food chain. In this way, by analyzing feeding patterns, it is possible to understand the impact of the food chain.

[0098] The video analysis unit can use the emotion estimation function to analyze the emotional reactions of other animals to the behavior of pests and understand interactions in the ecosystem. For example, the generation AI can analyze the emotional reactions of other animals to the behavior of pests and understand interactions based on the other animals' wariness. The generation AI can also analyze the emotional reactions of other animals to identify interactions in the ecosystem. For example, it can analyze changes in the behavior of other animals due to the appearance of a pest. Furthermore, using the emotion estimation function, the generation AI can analyze the emotional reactions of other animals and understand the balance of the ecosystem. For example, it can identify the impact that the behavior of pests has on other animals. This makes it possible to understand interactions in the ecosystem by analyzing the emotional reactions of other animals.

[0099] The video analysis unit can analyze the movement routes of pests and track changes in their habitats. For example, the generation AI can analyze the movement routes of pests and identify seasonal movement patterns. The generation AI can also analyze changes in the pests' habitats based on their movement routes. For example, it can identify breeding grounds and food-hunting locations. Furthermore, the generation AI can analyze the movement routes of pests and track changes in their habitats. For example, it can identify habitat shifts due to environmental changes. In this way, it is possible to track changes in habitats by analyzing their movement routes.

[0100] The video analysis unit can use the emotion estimation function to analyze human emotional responses to pest behavior and propose measures for coexistence. For example, the generation AI can analyze human emotional responses to pest behavior and identify behaviors that frighten humans. The generation AI can also analyze human emotional responses and propose measures for coexistence. For example, it can propose safety measures for pest-infested areas. Furthermore, using the emotion estimation function, the generation AI can analyze human emotional responses and propose educational programs for coexistence. For example, it can educate people on how to interact safely with pests. In this way, by analyzing human emotional responses, it can propose measures for coexistence.

[0101] The notification unit allows the generation AI to analyze the frequency of pest animal sightings and optimize the hunter's patrol schedule. For example, the generation AI can analyze the frequency of pest animal sightings and focus patrols on areas with high sighting frequency. The generation AI can also optimize the hunter's patrol schedule based on sighting frequency. For example, it can concentrate patrols during specific time periods. Furthermore, the generation AI can analyze the frequency of pest animal sightings and adjust the schedule based on seasonal sighting patterns. In this way, the hunter's patrol schedule can be optimized by analyzing sighting frequency.

[0102] The notification unit allows the generation AI to analyze the success rate of pest capture and suggest effective capture methods. For example, the generation AI can analyze the success rate of pest capture and suggest specific traps or capture techniques. The generation AI can also suggest effective capture methods based on the capture success rate. For example, it can suggest capture methods for specific times of day or locations. Furthermore, the generation AI can analyze the success rate of pest capture and suggest capture methods based on environmental conditions and the behavioral patterns of the pest. In this way, effective capture methods can be suggested by analyzing the capture success rate.

[0103] The notification unit can use the emotion estimation function to monitor the hunter's emotional state and provide support to reduce stress. For example, the emotion estimation function can be used to monitor the hunter's emotional state and provide advice on how to relax when stress increases. The generation AI can also analyze the hunter's emotional state in real time and provide support to reduce stress. For example, it can suggest relaxation methods based on the emotional state. Furthermore, the emotion estimation function can be used to monitor the hunter's emotional state and provide feedback to reduce stress. For example, it can play relaxing music based on the emotional state. This can reduce the burden on the hunter by monitoring the emotional state and providing support to reduce stress.

[0104] The notification unit allows the generation AI to share information about pest sightings with other regions, enabling wide-area countermeasures to be taken. For example, the generation AI can share information about pest sightings with other regions, share the information with neighboring regions, and work together to implement countermeasures. Furthermore, based on the sighting information, the generation AI can also work with other regions to implement wide-area countermeasures. For example, it can create a wide-area patrol plan. Furthermore, the generation AI can share information about pest sightings with other regions, exchange information between regions, and implement effective countermeasures. This allows wide-area countermeasures to be taken by sharing sighting information with other regions.

[0105] The notification unit can use the emotion estimation function to analyze the emotional state of the hunter and issue a dispatch command at the optimal timing. For example, the emotion estimation function can be used to analyze the emotional state of the hunter and issue a dispatch command at a timing when stress is low. The generation AI can also analyze the emotional state of the hunter in real time and issue a dispatch command at the optimal timing. For example, a dispatch command can be issued at a timing based on the emotional state. Furthermore, the emotion estimation function can be used to analyze the emotional state of the hunter and issue a dispatch command to reduce stress. For example, a relaxation method based on the emotional state can be suggested. This allows the burden on the hunter to be reduced by analyzing the emotional state and issuing a dispatch command at the optimal timing.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The video analysis unit is equipped with a generative AI and analyzes surveillance camera footage. The generative AI receives video data from the surveillance camera as input and identifies animals in the footage using a deep learning model that has learned the characteristics of pests. It also uses motion detection technology and image recognition algorithms to analyze the shape and movement of animals and identify them as pests. Step 2: The suspicious area identification unit identifies suspicious areas from the surveillance camera footage analyzed by the video analysis unit where pests may be caught. The generation AI identifies areas that match the characteristics of pests and marks them as suspicious. It can also analyze animal movements and behavior patterns to identify suspicious areas. Step 3: The notification unit notifies humans of the suspicious parts identified by the suspicious part identification unit. The generation AI extracts video clips of suspicious parts and sends them to humans as alerts. It can also analyze suspicious parts in real time and send immediate notifications.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0152] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A video analysis unit equipped with generative AI, a suspicious area identifying unit that identifies a suspicious area in which a harmful animal is captured from the surveillance camera video analyzed by the video analyzing unit; a notification unit that notifies a person of the suspicious portion identified by the suspicious portion identification unit. A system characterized by:

2. The video analysis unit Analyzing the shape or movement of the animal and identifying it as a pest 2. The system of claim 1.

3. The suspicious portion identification unit Analyze the video of the suspicious area from multiple angles and identify the pest with high accuracy.

2. The system of claim 1.

4. The notification unit The detailed analysis report provided by the generating AI is notified to the human, enabling quick decision-making.

2. The system of claim 1.

5. The video analysis unit The behavior or reaction of animals in the vicinity when the pest is captured is analyzed to estimate the presence of the pest.

2. The system of claim 1.

Images