System

Abstract

An object of a system according to an embodiment is to quickly and accurately identify the type and state of a trapped living thing and take an appropriate action.SOLUTION: A system according to an embodiment includes a camera, a sensor, a generation AI, and a notifying unit. The camera photographs the inside of the trap. The sensor detects movement of the living thing. The production AI analyzes the date acquired by the cameras and sensors. The notifying unit notifies the alert destination based on a result analyzed by the generated AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to quickly and accurately identify the type and condition of trapped organisms and take appropriate measures.

[0005] The system according to the embodiment aims to quickly and accurately identify the type and condition of a trapped organism and take appropriate action. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera, a sensor, a generation AI, and a notification unit. The camera captures images of the inside of the trap. The sensor detects the movement of the living creature. The generation AI analyzes the data acquired by the camera and the sensor. The notification unit notifies the destination based on the results of the analysis by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately identify the type and condition of a trapped organism and take appropriate action. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The monitoring system according to an embodiment of the present invention collects data on trapped creatures, analyzes it using a generation AI, and takes appropriate action. This allows the monitoring system to quickly identify the type and circumstances of the trapped creature and take appropriate action.

[0029] A monitoring system according to an embodiment includes a camera, a sensor, a generation AI, and a notification unit. The camera captures images of the inside of the trap. For example, the camera captures high-resolution images of the inside of the trap and collects video data. The camera also has a nighttime imaging function, allowing it to capture clear images even in the dark. The sensor detects the movement of living creatures. For example, an infrared sensor detects the body temperature and movement of living creatures. An ultrasonic sensor can detect the movement of living creatures using sound waves. The sensor also has a voice recognition function and can analyze the calls and sounds of living creatures to identify their species. The generation AI analyzes the data acquired by the camera and the sensor. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM) to identify the type and situation of the living creature. The generation AI can also use a multimodal generation AI to integrate and analyze multiple data. The generation AI also has an emotion estimation function and can analyze the emotional state of the trapped creature. The notification unit notifies the destination based on the results of the analysis by the generation AI. For example, the notification unit notifies the destination based on the results of the analysis by the generation AI. The notification unit also enables real-time responses via voice calls or video calls. This allows the monitoring system according to the embodiment to quickly identify the type and situation of the trapped creature and take appropriate action. For example, the notification unit can include detailed information about the health and emotional state of the trapped creature in the alert. The notification unit can also expand the scope of alerts to include local animal protection organizations and research institutions. Furthermore, the notification unit can use an emotion estimation function to automatically generate alert content based on the emotional state of the trapped creature.

[0030] The camera is equipped with a night-time shooting function, allowing it to collect clear images even at night. For example, the camera may be equipped with an infrared night vision function to collect clear images even at night. This allows for detailed observation of the movements of trapped creatures at night. The camera also uses a highly sensitive low-light camera to collect clear images even in moonlight or starlight. This improves the accuracy of nighttime monitoring. Furthermore, the camera may be equipped with an automatic exposure adjustment function to collect images at appropriate brightness even at night. This makes the images easier to see at night. This allows for detailed observation of the movements of trapped creatures even at night.

[0031] The sensor is equipped with a voice recognition function and can identify the species of living creatures by analyzing their cries and sounds. For example, the sensor can be equipped with a voice recognition function and analyze the cries of living creatures to identify their species. For example, it can identify bird calls and mammal calls. The sensor can also use voice recognition technology to analyze the cries of trapped creatures in real time to identify their species. For example, it can identify the cries of wild boars and deer. Furthermore, the sensor can be equipped with a voice database and match the cries of the creatures to identify their species. For example, it can identify creatures with specific call patterns. This makes it possible to analyze the cries and sounds of living creatures and identify their species.

[0032] The system is equipped with drones and can monitor traps over a wide area. For example, the system can equip the drone with a high-resolution camera and monitor traps over a wide area from the air. This allows for efficient monitoring of the entire trap installation area. The system also equips the drone with an infrared sensor, allowing for monitoring of traps even at night or in poor visibility. This allows for 24-hour monitoring. Furthermore, the system equips the drone with a GPS function, allowing for monitoring while accurately determining the trap installation locations. This allows for real-time location information of the traps. This allows for monitoring of traps over a wide area.

[0033] The system is equipped with an underwater camera and underwater sensors, and can also capture aquatic organisms. For example, the system installs an underwater camera in a trap to monitor the capture status of aquatic organisms, thereby making underwater capture activities more efficient. The system also uses underwater sensors to detect the movements of aquatic organisms and grasp the capture status in real time. For example, it detects the movements of fish and crabs. Furthermore, the system links the underwater camera and sensors to identify the type and size of the aquatic organisms. For example, it combines and analyzes camera footage and sensor information. This also makes it possible to capture aquatic organisms.

[0034] Generative AI can perform a detailed analysis of the health of trapped creatures and detect signs of illness early on. Generative AI analyzes the health of trapped creatures, for example, from camera footage and sensor information. For example, it detects abnormalities in the skin or sluggish movement. Generative AI also analyzes the creature's body temperature and heart rate to detect abnormal values. For example, if the body temperature is too high, it will be judged to be a sign of fever. Generative AI can also analyze the creature's behavioral patterns to detect signs of illness early on. For example, it will detect abnormal behavior or loss of appetite. This allows for a detailed analysis of the health of trapped creatures and detect signs of illness early on.

[0035] The generation AI can learn the behavioral patterns of trapped creatures and use this information to help with the next capture. For example, the generation AI can learn the behavioral patterns of trapped creatures and use this information to help with the next capture. For example, it can analyze the behavior of creatures that are active at specific times of the day. The generation AI can also analyze the movement paths of creatures and optimize the placement of traps. For example, it can identify routes that creatures often take. Furthermore, the generation AI can learn the feeding patterns of creatures and optimize the placement of bait. For example, it can analyze the behavior of creatures that react to specific bait. In this way, the generation AI can learn the behavioral patterns of trapped creatures and use this information to help with the next capture.

[0036] The generation AI can perform DNA analysis of trapped organisms to identify their species. For example, the generation AI can perform DNA analysis of trapped organisms based on collected data to identify their species. For example, it can use DNA sequencing technology. The generation AI can also compare the results with a database of organisms' DNA to identify their species. For example, it can identify organisms with specific genetic patterns. Furthermore, the generation AI can identify the species based on the results of the organism's DNA analysis and register it in a database. For example, it can discover new species or confirm existing species. This allows the generation AI to perform DNA analysis of trapped organisms to identify their species.

[0037] The generation AI can analyze the cries and sounds of trapped creatures to identify their type and condition. For example, the generation AI can analyze the cries and sounds of trapped creatures to identify their type. For example, it can identify creatures with specific call patterns. The generation AI can also analyze the frequency of the cries and sounds of trapped creatures to identify their condition. For example, it can determine stress levels from changes in their cries. Furthermore, the generation AI can analyze the cries and sounds of trapped creatures in real time to identify their type and condition. For example, it can detect abnormal calls and issue an alert. This makes it possible to analyze the cries and sounds of trapped creatures to identify their type and condition.

[0038] The notification unit can also expand the recipients of the report to local animal protection organizations and research institutions. For example, the notification unit can expand the recipients of the report to local animal protection organizations and share information about trapped organisms. For example, the notification unit can send a message saying, "The trapped organism needs to be protected." The notification unit can also expand the recipients of the report to research institutions and provide data about trapped organisms. For example, the notification unit can send a message saying, "Please use the data about trapped organisms for research." The notification unit can also expand the recipients of the report to animal protection organizations and research institutions and share information about trapped organisms. For example, the notification unit can send a message saying, "The trapped organism needs to be protected and researched." This allows the recipients of the report to be expanded to local animal protection organizations and research institutions.

[0039] The notification unit can add voice calls or video calls to the alerting means, enabling a real-time response. The notification unit, for example, adds voice calls to the alerting means, enabling a real-time response. For example, the status of a trapped creature is reported by voice. The notification unit also adds video calls to the alerting means, enabling a real-time response. For example, a video of the trapped creature is shared via video call. The notification unit also adds voice calls or video calls to the alerting means, encouraging a real-time response. For example, a quick response is provided via voice calls or video calls in an emergency. This allows voice calls or video calls to be added to the alerting means, enabling a real-time response.

[0040] The notification unit can include detailed map information of the trap's installation location and the capture status in the content of the alert. For example, the notification unit includes detailed map information of the trap's installation location in the content of the alert. For example, the notification unit may notify, "Please refer to the map below for the trap's installation location." The notification unit also includes detailed map information of the capture status in the content of the alert. For example, the notification unit may notify, "Please refer to the map below for the capture status of the trapped creature." The notification unit also includes detailed map information of the trap's installation location and the capture status in the content of the alert to prompt appropriate action. For example, the notification unit may notify, "Please refer to the map below for the trap's installation location and the capture status." This allows the notification unit to include detailed map information of the trap's installation location and the capture status in the content of the alert.

[0041] The system can build a hunter network and add a real-time information sharing function, enabling a rapid response. For example, the system can add a real-time information sharing function to the hunter network, enabling a rapid response. For example, information about trapped creatures can be shared in real time. The system can also add a real-time chat function to information sharing within the network, encouraging a rapid response. For example, information can be shared via chat in an emergency. The system can also add a real-time information sharing function to the hunter network, enabling a rapid response. For example, the location of traps and the status of captures can be shared in real time. In this way, by building a hunter network and adding a real-time information sharing function, a rapid response is possible.

[0042] The system can build a hunter network and add audio and video messages to information sharing within the network, thereby facilitating communication. The system, for example, adds audio messages to information sharing within the network, thereby facilitating communication. For example, the status of a trapped creature is reported by audio. The system can also add video messages to information sharing within the network, thereby facilitating communication. For example, footage of a trapped creature is shared by video message. The system can also add audio and video messages to information sharing within the network, thereby facilitating communication. For example, information can be quickly shared by audio and video messages in an emergency. In this way, a hunter network can be built, and adding audio and video messages to information sharing within the network can facilitate communication.

[0043] The system can build a hunter network, expand it into an international network, and enable global information sharing. For example, the system can expand the hunter network into an international network and enable global information sharing. For example, information can be shared with hunters in different countries. The system can also build an international hunter network and promote global information sharing. For example, international hunting events and research results can be shared. Furthermore, the system can expand the hunter network internationally and enable global information sharing. For example, hunters in different regions can collaborate and share information. This can expand the hunter network into an international network and enable global information sharing.

[0044] The system builds a hunter network and adds an AI-based automatic translation function to information sharing within the network, thereby facilitating communication between different languages. For example, the system adds an AI-based automatic translation function to information sharing within the network, thereby facilitating communication between different languages. For example, automatic translation from English to Japanese is performed. The system also introduces an AI-based automatic translation function into the network to promote information sharing between different languages. For example, automatic translation from Spanish to French is performed. The system also adds an AI-based automatic translation function to information sharing within the network, thereby facilitating communication between different languages. For example, automatic translation from Chinese to English is performed. In this way, the system builds a hunter network and adds an AI-based automatic translation function to information sharing within the network, thereby facilitating communication between different languages.

[0045] Generative AI can analyze ecological survey data in detail and predict changes in the ecosystem. For example, generative AI analyzes ecological survey data using AI and predicts changes in the ecosystem. For example, it analyzes animal migration patterns and breeding status. Generative AI also uses AI to predict the impact of environmental changes from ecological survey data. For example, it analyzes the impact of climate change on animal ecology. Generative AI also analyzes ecological survey data in detail using AI and predicts changes in the ecosystem. For example, it predicts the increase or decrease of specific animal species. This makes it possible to analyze ecological survey data in detail and predict changes in the ecosystem.

[0046] The generation AI can integrate the ecological survey data with other environmental data to conduct comprehensive ecosystem research. The generation AI can, for example, integrate the ecological survey data with other environmental data to conduct comprehensive ecosystem research. For example, with weather data and water quality data. The generation AI can also integrate the ecological survey data with other environmental data to conduct comprehensive ecosystem research. For example, with soil data and vegetation data. The generation AI can also integrate the ecological survey data with other environmental data to conduct comprehensive ecosystem research. For example, with weather data and water quality data. This allows the ecological survey data to be integrated with other environmental data to conduct comprehensive ecosystem research.

[0047] Generative AI can share ecological survey data with educational institutions and research institutions and use it for education and research. For example, generative AI can share ecological survey data with educational institutions and use it for education. For example, it can use it to study ecosystems in school classes. Generative AI can also share ecological survey data with research institutions and use it for research. For example, it can provide data for ecosystem research projects. Generative AI can also share ecological survey data with educational institutions and research institutions and use it for education and research. For example, it can use it for ecosystem research in university laboratories. This allows ecological survey data to be shared with educational institutions and research institutions and used for education and research.

[0048] The generative AI can make the ecological survey data public and share it with local residents and environmental protection organizations. For example, the generative AI can make the ecological survey data public and share it with local residents. For example, it can provide the data for local environmental protection activities. The generative AI can also make the ecological survey data public and share it with environmental protection organizations. For example, it can use the data to plan protection activities. The generative AI can also make the ecological survey data public and share it with local residents and environmental protection organizations. For example, it can provide the data for local environmental education programs. This allows the ecological survey data to be public and shared with local residents and environmental protection organizations.

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

[0050] The monitoring system is also equipped with a voice recognition unit that can analyze the calls and sounds of trapped creatures to identify their species. For example, the voice recognition unit can distinguish between bird calls and mammal calls. The voice recognition unit is also equipped with a voice database, which can match the call patterns to identify the species of creature. Furthermore, the voice recognition unit can analyze the calls in real time, detect abnormal calls, and issue an alarm. This allows the type of trapped creature to be quickly identified and appropriate action to be taken.

[0051] The monitoring system can also be equipped with a temperature sensor to measure the body temperature of trapped creatures. For example, the temperature sensor can measure body temperature without contact using infrared technology. The temperature sensor can also monitor body temperature fluctuations in real time, detect abnormal body temperatures, and issue an alert. Furthermore, the temperature sensor can analyze body temperature data to help evaluate the health status of trapped creatures. This allows for a detailed understanding of the health status of trapped creatures and allows for appropriate response.

[0052] The monitoring system is also equipped with a GPS function, allowing for accurate identification of trap placement locations. For example, the GPS function obtains trap location information in real time and displays it on a map. The GPS function also records the history of trap placement locations, allowing past data to be referenced. Furthermore, the GPS function provides information for optimizing trap placement locations and supports efficient capture activities. This allows for accurate identification of trap placement locations, enabling efficient capture activities.

[0053] The monitoring system can also be equipped with drones to monitor traps over a wide area. For example, a drone can be equipped with a high-resolution camera to monitor the entire trap installation area from the air. Infrared sensors can also be installed on the drone, allowing for monitoring of traps even at night or in poor visibility. Furthermore, drones can be equipped with GPS functionality to monitor trap installation locations accurately. This makes it possible to monitor traps over a wide area, supporting efficient capture activities.

[0054] The monitoring system can also be equipped with underwater cameras and sensors to capture aquatic organisms. For example, an underwater camera can be installed in the trap to monitor the capture status of aquatic organisms. The underwater sensor can also be used to detect the movements of the aquatic organisms and grasp the capture status in real time. Furthermore, the underwater camera and sensor can be linked to identify the type and size of the aquatic organisms. This allows for the capture of aquatic organisms and enables efficient capture activities.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The camera takes a picture of the inside of the trap. For example, the camera takes a high-resolution picture of the inside of the trap and collects video data. The camera also has a night vision function, allowing it to capture clear images even in the dark. Step 2: The sensor detects the movement of the living creature. For example, an infrared sensor detects the body temperature of the living creature and detects its movement. An ultrasonic sensor can also detect the movement of living creatures using sound waves. Furthermore, the sensor has a voice recognition function, and can analyze the calls and sounds of living creatures to identify their species. Step 3: The generation AI analyzes the data acquired by the camera and sensors. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the data and identify the type of creature and its situation. The generation AI may also use a multimodal generation AI to integrate and analyze multiple data. Furthermore, the generation AI may have an emotion estimation function and be able to analyze the emotional state of the trapped creature. Step 4: The notification unit notifies the recipient based on the results of the analysis by the generation AI. For example, the notification unit may send notifications via email or social media. The notification unit may also enable real-time responses via voice or video calls. This allows the monitoring system according to the embodiment to quickly identify the type and situation of the trapped creature and take appropriate action. For example, the notification unit may include detailed information about the health and emotional state of the trapped creature in the notification content. The notification unit may also expand the recipients of the notification to include local animal protection organizations and research institutions. Furthermore, the notification unit may use an emotion estimation function to automatically generate notification content according to the emotional state of the trapped creature.

[0057] (Example 2) The monitoring system according to an embodiment of the present invention collects data on trapped creatures, analyzes it using a generation AI, and takes appropriate action. This allows the monitoring system to quickly identify the type and circumstances of the trapped creature and take appropriate action.

[0058] A monitoring system according to an embodiment includes a camera, a sensor, a generation AI, and a notification unit. The camera captures images of the inside of the trap. For example, the camera captures high-resolution images of the inside of the trap and collects video data. The camera also has a nighttime imaging function, allowing it to capture clear images even in the dark. The sensor detects the movement of living creatures. For example, an infrared sensor detects the body temperature and movement of living creatures. An ultrasonic sensor can detect the movement of living creatures using sound waves. The sensor also has a voice recognition function and can analyze the calls and sounds of living creatures to identify their species. The generation AI analyzes the data acquired by the camera and the sensor. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM) to identify the type and situation of the living creature. The generation AI can also use a multimodal generation AI to integrate and analyze multiple data. The generation AI also has an emotion estimation function and can analyze the emotional state of the trapped creature. The notification unit notifies the destination based on the results of the analysis by the generation AI. For example, the notification unit notifies the destination based on the results of the analysis by the generation AI. The notification unit also enables real-time responses via voice calls or video calls. This allows the monitoring system according to the embodiment to quickly identify the type and situation of the trapped creature and take appropriate action. For example, the notification unit can include detailed information about the health and emotional state of the trapped creature in the alert. The notification unit can also expand the scope of alerts to include local animal protection organizations and research institutions. Furthermore, the notification unit can use an emotion estimation function to automatically generate alert content based on the emotional state of the trapped creature.

[0059] The camera is equipped with a night-time shooting function, allowing it to collect clear images even at night. For example, the camera may be equipped with an infrared night vision function to collect clear images even at night. This allows for detailed observation of the movements of trapped creatures at night. The camera also uses a highly sensitive low-light camera to collect clear images even in moonlight or starlight. This improves the accuracy of nighttime monitoring. Furthermore, the camera may be equipped with an automatic exposure adjustment function to collect images at appropriate brightness even at night. This makes the images easier to see at night. This allows for detailed observation of the movements of trapped creatures even at night.

[0060] The sensor is equipped with a voice recognition function and can identify the species of living creatures by analyzing their cries and sounds. For example, the sensor can be equipped with a voice recognition function and analyze the cries of living creatures to identify their species. For example, it can identify bird calls and mammal calls. The sensor can also use voice recognition technology to analyze the cries of trapped creatures in real time to identify their species. For example, it can identify the cries of wild boars and deer. Furthermore, the sensor can be equipped with a voice database and match the cries of the creatures to identify their species. For example, it can identify creatures with specific call patterns. This makes it possible to analyze the cries and sounds of living creatures and identify their species.

[0061] The generative AI is equipped with an emotion estimation function and can measure the stress level of trapped creatures and prompt immediate action if stress is high. For example, the generative AI analyzes camera footage and estimates the stress level from the creature's facial expressions and movements. For example, it analyzes the animal's eye movements and body tension. The generative AI also uses sensors to measure the creature's heart rate and breathing rate to estimate the stress level. For example, a high heart rate is determined to indicate high stress. Furthermore, the generative AI uses an emotion estimation algorithm to analyze the creature's behavioral patterns and measure the stress level. For example, if an abnormal behavioral pattern is observed, it is determined to indicate high stress. This makes it possible to measure the stress level of trapped creatures and prompt action can be taken.

[0062] The system is equipped with drones and can monitor traps over a wide area. For example, the system can equip the drone with a high-resolution camera and monitor traps over a wide area from the air. This allows for efficient monitoring of the entire trap installation area. The system also equips the drone with an infrared sensor, allowing for monitoring of traps even at night or in poor visibility. This allows for 24-hour monitoring. Furthermore, the system equips the drone with a GPS function, allowing for monitoring while accurately determining the trap installation locations. This allows for real-time location information of the traps. This allows for monitoring of traps over a wide area.

[0063] The system is equipped with an underwater camera and underwater sensors, and can also capture aquatic organisms. For example, the system installs an underwater camera in a trap to monitor the capture status of aquatic organisms, thereby making underwater capture activities more efficient. The system also uses underwater sensors to detect the movements of aquatic organisms and grasp the capture status in real time. For example, it detects the movements of fish and crabs. Furthermore, the system links the underwater camera and sensors to identify the type and size of the aquatic organisms. For example, it combines and analyzes camera footage and sensor information. This also makes it possible to capture aquatic organisms.

[0064] The generative AI is equipped with an emotion estimation function, allowing it to monitor the emotional state of trapped creatures in real time and respond appropriately. For example, the generative AI analyzes camera footage and monitors the emotional state of the creature in real time from its facial expressions and movements. For example, it analyzes the animal's eye movements and body tension. The generative AI also uses sensors to measure the creature's heart rate and breathing rate and monitors its emotional state in real time. For example, a high heart rate can be determined to indicate high stress. Furthermore, the generative AI uses an emotion estimation algorithm to analyze the creature's behavioral patterns and monitor its emotional state in real time. For example, if an abnormal behavioral pattern is observed, it can be determined to indicate high stress. This allows the emotional state of trapped creatures to be monitored in real time and appropriate responses to be taken.

[0065] Generative AI can perform a detailed analysis of the health of trapped creatures and detect signs of illness early on. Generative AI analyzes the health of trapped creatures, for example, from camera footage and sensor information. For example, it detects abnormalities in the skin or sluggish movement. Generative AI also analyzes the creature's body temperature and heart rate to detect abnormal values. For example, if the body temperature is too high, it will be judged to be a sign of fever. Generative AI can also analyze the creature's behavioral patterns to detect signs of illness early on. For example, it will detect abnormal behavior or loss of appetite. This allows for a detailed analysis of the health of trapped creatures and detect signs of illness early on.

[0066] The generation AI can learn the behavioral patterns of trapped creatures and use this information to help with the next capture. For example, the generation AI can learn the behavioral patterns of trapped creatures and use this information to help with the next capture. For example, it can analyze the behavior of creatures that are active at specific times of the day. The generation AI can also analyze the movement paths of creatures and optimize the placement of traps. For example, it can identify routes that creatures often take. Furthermore, the generation AI can learn the feeding patterns of creatures and optimize the placement of bait. For example, it can analyze the behavior of creatures that react to specific bait. In this way, the generation AI can learn the behavioral patterns of trapped creatures and use this information to help with the next capture.

[0067] The generative AI is equipped with an emotion estimation function, which allows it to generate summaries that capture the emotional nuances of the trapped creatures and reflect these emotional elements in the evaluation. For example, when summarizing, the generative AI uses the emotion estimation function to capture the emotional nuances of an answer. For example, it generates a summary based on an emotion score. The generative AI also uses the emotion estimation function to build a system that reflects the emotional elements of an answer in the evaluation. For example, it performs the evaluation based on the emotion score. Furthermore, the generative AI uses the emotion estimation function to develop an algorithm for generating summaries that capture the emotional nuances of an answer. For example, it generates a summary based on the emotion score and reflects this in the evaluation. In this way, by generating a summary that captures the emotional nuances, emotional elements can also be reflected in the evaluation.

[0068] The generation AI can perform DNA analysis of trapped organisms to identify their species. For example, the generation AI can perform DNA analysis of trapped organisms based on collected data to identify their species. For example, it can use DNA sequencing technology. The generation AI can also compare the results with a database of organisms' DNA to identify their species. For example, it can identify organisms with specific genetic patterns. Furthermore, the generation AI can identify the species based on the results of the organism's DNA analysis and register it in a database. For example, it can discover new species or confirm existing species. This allows the generation AI to perform DNA analysis of trapped organisms to identify their species.

[0069] The generation AI can analyze the cries and sounds of trapped creatures to identify their type and condition. For example, the generation AI can analyze the cries and sounds of trapped creatures to identify their type. For example, it can identify creatures with specific call patterns. The generation AI can also analyze the frequency of the cries and sounds of trapped creatures to identify their condition. For example, it can determine stress levels from changes in their cries. Furthermore, the generation AI can analyze the cries and sounds of trapped creatures in real time to identify their type and condition. For example, it can detect abnormal calls and issue an alert. This makes it possible to analyze the cries and sounds of trapped creatures to identify their type and condition.

[0070] The generation AI is equipped with an emotion estimation function, and can analyze the emotional state of trapped creatures and respond appropriately. The generation AI can, for example, analyze the emotional state of a trapped creature and respond appropriately. For example, if stress levels are high, it can prompt immediate action. The generation AI can also analyze the behavioral patterns of the creature, identify its emotional state, and respond appropriately. For example, it can issue a warning if abnormal behavior is observed. Furthermore, the generation AI uses the emotion estimation function to analyze the emotional state of a trapped creature in real time and respond appropriately. For example, it can suggest environmental improvements to reduce stress. This makes it possible to analyze the emotional state of trapped creatures and respond appropriately.

[0071] The notification unit can include detailed information about the health and emotional state of the trapped creature in the alert content. For example, the notification unit may include a detailed description of the creature's health condition in the alert content. For example, the notification may say, "The trapped creature is showing signs of fever. Please take immediate action." The notification unit may also include the creature's emotional state in the alert content. For example, the notification may say, "The trapped creature is in a state of high stress. Please take immediate action." The notification unit may also include a detailed description of the creature's health and emotional state in the alert content to encourage appropriate action. For example, the notification may say, "The trapped creature is in good health, but caution is required." This allows the detailed information about the health and emotional state of the trapped creature to be included in the alert content.

[0072] The notification unit can also expand the recipients of the report to local animal protection organizations and research institutions. For example, the notification unit can expand the recipients of the report to local animal protection organizations and share information about trapped organisms. For example, the notification unit can send a message saying, "The trapped organism needs to be protected." The notification unit can also expand the recipients of the report to research institutions and provide data about trapped organisms. For example, the notification unit can send a message saying, "Please use the data about trapped organisms for research." The notification unit can also expand the recipients of the report to animal protection organizations and research institutions and share information about trapped organisms. For example, the notification unit can send a message saying, "The trapped organism needs to be protected and researched." This allows the recipients of the report to be expanded to local animal protection organizations and research institutions.

[0073] The notification unit can use the emotion estimation function to automatically generate alert content according to the emotional state of the trapped creature. The notification unit, for example, uses the emotion estimation function to automatically generate alert content according to the emotional state of the trapped creature. For example, the notification unit may send a message saying, "The trapped creature is in a state of high stress. Immediate action is required." The notification unit also uses the emotion estimation function to analyze the emotional state of the creature and automatically generate appropriate alert content. For example, the notification unit may send a message saying, "The trapped creature is in a healthy state, but caution is required." The notification unit also uses the emotion estimation function to automatically generate alert content based on the emotional state of the trapped creature and prompt an appropriate response. For example, the notification unit may send a message saying, "The trapped creature is in an unstable state. Immediate action is required." In this way, alert content according to the emotional state of the trapped creature can be automatically generated.

[0074] The notification unit can add voice calls or video calls to the alerting means, enabling a real-time response. The notification unit, for example, adds voice calls to the alerting means, enabling a real-time response. For example, the status of a trapped creature is reported by voice. The notification unit also adds video calls to the alerting means, enabling a real-time response. For example, a video of the trapped creature is shared via video call. The notification unit also adds voice calls or video calls to the alerting means, encouraging a real-time response. For example, a quick response is provided via voice calls or video calls in an emergency. This allows voice calls or video calls to be added to the alerting means, enabling a real-time response.

[0075] The notification unit can include detailed map information of the trap's installation location and the capture status in the content of the alert. For example, the notification unit includes detailed map information of the trap's installation location in the content of the alert. For example, the notification unit may notify, "Please refer to the map below for the trap's installation location." The notification unit also includes detailed map information of the capture status in the content of the alert. For example, the notification unit may notify, "Please refer to the map below for the capture status of the trapped creature." The notification unit also includes detailed map information of the trap's installation location and the capture status in the content of the alert to prompt appropriate action. For example, the notification unit may notify, "Please refer to the map below for the trap's installation location and the capture status." This allows the notification unit to include detailed map information of the trap's installation location and the capture status in the content of the alert.

[0076] The notification unit can use the emotion estimation function to automatically generate alert content based on the emotional state of the trapped creature and encourage an appropriate response. The notification unit can, for example, use the emotion estimation function to automatically generate alert content based on the emotional state of the trapped creature and encourage an appropriate response. For example, the notification unit can send a message saying, "The trapped creature is in a state of high stress. Immediate action is required." The notification unit can also use the emotion estimation function to analyze the emotional state of the creature and automatically generate appropriate alert content. For example, the notification unit can send a message saying, "The trapped creature is in a healthy state, but caution is required." The notification unit can also use the emotion estimation function to automatically generate alert content based on the emotional state of the trapped creature and encourage an appropriate response. For example, the notification unit can send a message saying, "The trapped creature is in an unstable state. Immediate action is required." This allows the notification unit to automatically generate alert content based on the emotional state of the trapped creature and encourage an appropriate response.

[0077] The system can build a hunter network and add a real-time information sharing function, enabling a rapid response. For example, the system can add a real-time information sharing function to the hunter network, enabling a rapid response. For example, information about trapped creatures can be shared in real time. The system can also add a real-time chat function to information sharing within the network, encouraging a rapid response. For example, information can be shared via chat in an emergency. The system can also add a real-time information sharing function to the hunter network, enabling a rapid response. For example, the location of traps and the status of captures can be shared in real time. In this way, by building a hunter network and adding a real-time information sharing function, a rapid response is possible.

[0078] The system can build a hunter network and add audio and video messages to information sharing within the network, thereby facilitating communication. The system, for example, adds audio messages to information sharing within the network, thereby facilitating communication. For example, the status of a trapped creature is reported by audio. The system can also add video messages to information sharing within the network, thereby facilitating communication. For example, footage of a trapped creature is shared by video message. The system can also add audio and video messages to information sharing within the network, thereby facilitating communication. For example, information can be quickly shared by audio and video messages in an emergency. In this way, a hunter network can be built, and adding audio and video messages to information sharing within the network can facilitate communication.

[0079] The system can build a hunter network and use the emotion estimation function to analyze the emotional state of hunters and suggest measures to reduce stress. For example, the system can use the emotion estimation function to analyze the emotional state of hunters and suggest measures to reduce stress. For example, if stress is high, the system can suggest relaxation methods. The system can also use the emotion estimation function to analyze the emotional state of hunters in real time and suggest specific measures to reduce stress. For example, the system can suggest appropriate breaks and ways to refresh. Furthermore, the system can use the emotion estimation function to analyze the emotional state of hunters and suggest environmental improvements to reduce stress. For example, the system can suggest adjustments to the work environment or strengthening the support system. In this way, the system can build a hunter network and use the emotion estimation function to analyze the emotional state of hunters and suggest measures to reduce stress.

[0080] The system can build a hunter network, expand it into an international network, and enable global information sharing. For example, the system can expand the hunter network into an international network and enable global information sharing. For example, information can be shared with hunters in different countries. The system can also build an international hunter network and promote global information sharing. For example, international hunting events and research results can be shared. Furthermore, the system can expand the hunter network internationally and enable global information sharing. For example, hunters in different regions can collaborate and share information. This can expand the hunter network into an international network and enable global information sharing.

[0081] The system builds a hunter network and adds an AI-based automatic translation function to information sharing within the network, thereby facilitating communication between different languages. For example, the system adds an AI-based automatic translation function to information sharing within the network, thereby facilitating communication between different languages. For example, automatic translation from English to Japanese is performed. The system also introduces an AI-based automatic translation function into the network to promote information sharing between different languages. For example, automatic translation from Spanish to French is performed. The system also adds an AI-based automatic translation function to information sharing within the network, thereby facilitating communication between different languages. For example, automatic translation from Chinese to English is performed. In this way, the system builds a hunter network and adds an AI-based automatic translation function to information sharing within the network, thereby facilitating communication between different languages.

[0082] The system builds a hunter network and uses an emotion estimation function to analyze the emotional state of hunters and provide appropriate support. For example, the system uses the emotion estimation function to analyze the emotional state of hunters and provide appropriate support. For example, if stress is high, the system may suggest relaxation methods. The system also uses the emotion estimation function to analyze the emotional state of hunters in real time and provide appropriate support. For example, the system may suggest appropriate breaks or ways to refresh. The system also uses the emotion estimation function to analyze the emotional state of hunters and provide appropriate support. For example, the system may suggest adjustments to the work environment or strengthening of the support system. In this way, the system builds a hunter network and uses the emotion estimation function to analyze the emotional state of hunters and provide appropriate support.

[0083] Generative AI can analyze ecological survey data in detail and predict changes in the ecosystem. For example, generative AI analyzes ecological survey data using AI and predicts changes in the ecosystem. For example, it analyzes animal migration patterns and breeding status. Generative AI also uses AI to predict the impact of environmental changes from ecological survey data. For example, it analyzes the impact of climate change on animal ecology. Generative AI also analyzes ecological survey data in detail using AI and predicts changes in the ecosystem. For example, it predicts the increase or decrease of specific animal species. This makes it possible to analyze ecological survey data in detail and predict changes in the ecosystem.

[0084] The generation AI can integrate the ecological survey data with other environmental data to conduct comprehensive ecosystem research. The generation AI can, for example, integrate the ecological survey data with other environmental data to conduct comprehensive ecosystem research. For example, with weather data and water quality data. The generation AI can also integrate the ecological survey data with other environmental data to conduct comprehensive ecosystem research. For example, with soil data and vegetation data. The generation AI can also integrate the ecological survey data with other environmental data to conduct comprehensive ecosystem research. For example, with weather data and water quality data. This allows the ecological survey data to be integrated with other environmental data to conduct comprehensive ecosystem research.

[0085] The generative AI can use the emotion estimation function to analyze the emotional state of captured organisms and assess the health of the ecosystem. For example, the generative AI can use the emotion estimation function to analyze the emotional state of captured organisms and assess the health of the ecosystem. For example, high stress levels can indicate problems with the ecosystem. The generative AI can also use the emotion estimation function to analyze the emotional state of captured organisms in real time and assess the health of the ecosystem. For example, it can issue an alert if an abnormal emotional state is observed. The generative AI can also use the emotion estimation function to analyze the emotional state of captured organisms and assess the health of the ecosystem. For example, it can suggest environmental improvements to reduce stress. This makes it possible to use the emotion estimation function to analyze the emotional state of captured organisms and assess the health of the ecosystem.

[0086] Generative AI can share ecological survey data with educational institutions and research institutions and use it for education and research. For example, generative AI can share ecological survey data with educational institutions and use it for education. For example, it can use it to study ecosystems in school classes. Generative AI can also share ecological survey data with research institutions and use it for research. For example, it can provide data for ecosystem research projects. Generative AI can also share ecological survey data with educational institutions and research institutions and use it for education and research. For example, it can use it for ecosystem research in university laboratories. This allows ecological survey data to be shared with educational institutions and research institutions and used for education and research.

[0087] The generative AI can make the ecological survey data public and share it with local residents and environmental protection organizations. For example, the generative AI can make the ecological survey data public and share it with local residents. For example, it can provide the data for local environmental protection activities. The generative AI can also make the ecological survey data public and share it with environmental protection organizations. For example, it can use the data to plan protection activities. The generative AI can also make the ecological survey data public and share it with local residents and environmental protection organizations. For example, it can provide the data for local environmental education programs. This allows the ecological survey data to be public and shared with local residents and environmental protection organizations.

[0088] The generative AI can use the emotion estimation function to analyze the emotional state of captured organisms, evaluate the health of the ecosystem, and propose appropriate conservation measures. For example, the generative AI can use the emotion estimation function to analyze the emotional state of captured organisms and evaluate the health of the ecosystem. For example, high stress levels can indicate ecosystem problems. The generative AI can also use the emotion estimation function to analyze the emotional state of captured organisms in real time and evaluate the health of the ecosystem. For example, it can issue an alert if an abnormal emotional state is observed. The generative AI can also use the emotion estimation function to analyze the emotional state of captured organisms, evaluate the health of the ecosystem, and propose appropriate conservation measures. For example, it can propose environmental improvements to reduce stress. This makes it possible to use the emotion estimation function to analyze the emotional state of captured organisms, evaluate the health of the ecosystem, and propose appropriate conservation measures.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The monitoring system is also equipped with a voice recognition unit that can analyze the calls and sounds of trapped creatures to identify their species. For example, the voice recognition unit can distinguish between bird calls and mammal calls. The voice recognition unit is also equipped with a voice database, which can match the call patterns to identify the species of creature. Furthermore, the voice recognition unit can analyze the calls in real time, detect abnormal calls, and issue an alarm. This allows the type of trapped creature to be quickly identified and appropriate action to be taken.

[0091] The monitoring system can also be equipped with a temperature sensor to measure the body temperature of trapped creatures. For example, the temperature sensor can measure body temperature without contact using infrared technology. The temperature sensor can also monitor body temperature fluctuations in real time, detect abnormal body temperatures, and issue an alert. Furthermore, the temperature sensor can analyze body temperature data to help evaluate the health status of trapped creatures. This allows for a detailed understanding of the health status of trapped creatures and allows for appropriate response.

[0092] The monitoring system is also equipped with a GPS function, allowing for accurate identification of trap placement locations. For example, the GPS function obtains trap location information in real time and displays it on a map. The GPS function also records the history of trap placement locations, allowing past data to be referenced. Furthermore, the GPS function provides information for optimizing trap placement locations and supports efficient capture activities. This allows for accurate identification of trap placement locations, enabling efficient capture activities.

[0093] The monitoring system can also be equipped with drones to monitor traps over a wide area. For example, a drone can be equipped with a high-resolution camera to monitor the entire trap installation area from the air. Infrared sensors can also be installed on the drone, allowing for monitoring of traps even at night or in poor visibility. Furthermore, drones can be equipped with GPS functionality to monitor trap installation locations accurately. This makes it possible to monitor traps over a wide area, supporting efficient capture activities.

[0094] The monitoring system can also be equipped with underwater cameras and sensors to capture aquatic organisms. For example, an underwater camera can be installed in the trap to monitor the capture status of aquatic organisms. The underwater sensor can also be used to detect the movements of the aquatic organisms and grasp the capture status in real time. Furthermore, the underwater camera and sensor can be linked to identify the type and size of the aquatic organisms. This allows for the capture of aquatic organisms and enables efficient capture activities.

[0095] The monitoring system is also equipped with an emotion estimation function, which allows it to monitor the emotional state of trapped creatures in real time and take appropriate measures. For example, it can analyze camera footage and monitor the emotional state in real time from the creature's facial expressions and movements. It can also use sensors to measure the creature's heart rate and respiratory rate and monitor the emotional state in real time. Furthermore, it can use an emotion estimation algorithm to analyze the creature's behavioral patterns and monitor the emotional state in real time. This allows it to grasp the emotional state of trapped creatures in real time and take appropriate measures.

[0096] The monitoring system is also equipped with an emotion estimation function, which can measure the stress level of trapped creatures and prompt immediate action if the stress level is high. For example, it can analyze camera footage to estimate the stress level from the creature's facial expressions and movements. It can also use sensors to measure the creature's heart rate and breathing rate to estimate the stress level. Furthermore, it can use an emotion estimation algorithm to analyze the creature's behavioral patterns and measure the stress level. This makes it possible to measure the stress level of trapped creatures and prompt action.

[0097] The monitor system is further equipped with an emotion estimation function, which can generate summaries that capture the emotional nuances of trapped creatures and reflect these emotional elements in the evaluation. For example, when summarizing, the emotion estimation function can be used to capture the emotional nuances of answers. In addition, the emotion estimation function can be used to build a system that reflects the emotional elements of answers in the evaluation. Furthermore, the emotion estimation function can be used to develop an algorithm for generating summaries that capture the emotional nuances of answers. In this way, by generating summaries that capture the emotional nuances, emotional elements can also be reflected in the evaluation.

[0098] The monitoring system is also equipped with an emotion estimation function, which can analyze the emotional state of trapped creatures and take appropriate measures. For example, by analyzing the emotional state of a creature and urging immediate action if stress levels are high, the system can also analyze the creature's behavioral patterns, identify its emotional state, and take appropriate action. Furthermore, the emotion estimation function can be used to analyze the emotional state of a creature in real time and suggest environmental improvements to reduce stress. This makes it possible to analyze the emotional state of trapped creatures and take appropriate action.

[0099] The monitoring system further includes an emotion estimation function that can analyze the emotional state of trapped organisms, assess the health of the ecosystem, and suggest appropriate conservation measures. For example, the emotion estimation function can be used to analyze the emotional state of captured organisms, and if stress levels are high, it can suggest ecosystem problems. The emotion estimation function can also be used to analyze the emotional state of captured organisms in real time and issue an alert if an abnormal emotional state is observed. The emotion estimation function can also be used to analyze the emotional state of captured organisms and suggest environmental improvements to reduce stress. In this way, the emotion estimation function can be used to analyze the emotional state of captured organisms, assess the health of the ecosystem, and suggest appropriate conservation measures.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The camera takes a picture of the inside of the trap. For example, the camera takes a high-resolution picture of the inside of the trap and collects video data. The camera also has a night vision function, allowing it to capture clear images even in the dark. Step 2: The sensor detects the movement of the living creature. For example, an infrared sensor detects the body temperature of the living creature and detects its movement. An ultrasonic sensor can also detect the movement of living creatures using sound waves. Furthermore, the sensor has a voice recognition function, and can analyze the calls and sounds of living creatures to identify their species. Step 3: The generation AI analyzes the data acquired by the camera and sensors. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the data and identify the type of creature and its situation. The generation AI may also use a multimodal generation AI to integrate and analyze multiple data. Furthermore, the generation AI may have an emotion estimation function and be able to analyze the emotional state of the trapped creature. Step 4: The notification unit notifies the recipient based on the results of the analysis by the generation AI. For example, the notification unit may send notifications via email or social media. The notification unit may also enable real-time responses via voice or video calls. This allows the monitoring system according to the embodiment to quickly identify the type and situation of the trapped creature and take appropriate action. For example, the notification unit may include detailed information about the health and emotional state of the trapped creature in the notification content. The notification unit may also expand the recipients of the notification to include local animal protection organizations and research institutions. Furthermore, the notification unit may use an emotion estimation function to automatically generate notification content according to the emotional state of the trapped creature.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

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

[0136] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] 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.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0149] 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.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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]

[0169] 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 camera to film the inside of the trap, A sensor that detects the movement of a living organism; A generation AI that analyzes data acquired by the camera and the sensor; A notification unit that notifies a notification destination based on the analysis result by the generation AI. A system characterized by:

2. The camera is Equipped with night-time shooting function, it collects clear images even at night 2. The system of claim 1.

3. The sensor Equipped with a voice recognition function, it analyzes the cries and sounds of living creatures to identify their species.

2. The system of claim 1.

4. The generated AI is Equipped with an emotion estimation function, it measures the stress level of trapped creatures and prompts immediate action if stress levels are high.

2. The system of claim 1.

5. The system comprises: Equipped with drones, The drone is Monitor traps over a wide area 2. The system of claim 1.

Images