System

Abstract

A system is provided.SOLUTION: A system for wildlife monitoring, comprising: camera means for capturing wildlife movements; terminal means for compressing image data obtained from the camera means and transmitting the compressed image data to a server; server means for receiving and storing the image data transmitted from the terminal means; analysis means for analyzing the data received from the server with an AI algorithm and recognizing the type and movements of the wildlife; dashboard means for visually displaying the results of the analysis; and notification means for issuing a notification to a user upon an abnormality or a new discovery.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] The present invention relates to a system for efficiently monitoring wildlife, checking environmental health, and detecting illegal logging in order to protect the global environment and ecosystems. Such a system can improve the efficiency of environmental protection and automate monitoring activities. However, conventional systems do not collect and analyze data in real time, making it difficult to detect abnormalities early. Furthermore, algorithms for analyzing large amounts of data quickly and accurately are often not sophisticated enough, resulting in delays in detecting wildlife movements and illegal logging. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means: First, a system for monitoring wildlife includes a camera means for capturing the movements of the wildlife, a terminal means for compressing image data obtained from the camera means and transmitting it to a server, a server means for receiving and saving the image data transmitted from the terminal means, an analysis means for analyzing the data received by the server means using an AI algorithm to recognize the type and movements of the wildlife, a dashboard means for visually displaying the analysis results, and a notification means for issuing a notification to a user in the event of an abnormality or new discovery.

[0006] The environmental health check system also includes a sensor means for collecting environmental data such as temperature, humidity, and pH, a terminal means for transmitting the data collected from the sensor means to a server, a server means for receiving the data transmitted from the terminal means and storing it in a database, an analysis means for analyzing the data analyzed by the server means in real time to detect abnormalities, and a notification means for issuing a notification to a user when an abnormality is detected.

[0007] Furthermore, the illegal logging detection system includes drone means for monitoring the monitoring area, sensor means for collecting ground data, terminal means for transmitting data obtained from the drone means and sensor means to a server, server means for receiving and storing the data transmitted from the terminal means, analysis means for analyzing the data received by the server means with an AI algorithm to detect illegal logging, and notification means for issuing a notification to the user when illegal logging is suspected.

[0008] These methods enable the entire system to work together to collect and analyze data in real time, enabling rapid detection of abnormalities. Furthermore, by visualizing the analysis results on a dashboard, users can intuitively understand the results and take appropriate action.

[0009] A "wildlife monitoring system" is a system with a set of functions that captures, analyzes, and notifies users of wildlife movements.

[0010] "Camera means" means a photographic device for visually capturing wildlife movements.

[0011] The "terminal means" is a device for transmitting data obtained from the camera means or sensor means to the server.

[0012] The "server means" is a computer system that receives, stores, and analyzes data sent from the terminal means.

[0013] "Analysis means" refers to a mechanism for evaluating and analyzing data using an AI algorithm built into the server means.

[0014] The "dashboard means" is an interface for visually displaying the analysis results.

[0015] "Notification means" is a system that issues an alert to the user when an abnormality or new discovery occurs.

[0016] The "environmental health check system" is a system that collects and analyzes environmental data such as temperature, humidity, and pH, and notifies the user if any abnormalities are detected.

[0017] A "sensor means" is a device for collecting environmental data.

[0018] The "Illegal Logging Detection System" is a system that captures and analyzes illegal logging activities within a monitored area and notifies users.

[0019] "Drone means" refers to an unmanned aerial vehicle used to photograph the surveillance area from above. [Brief explanation of the drawings]

[0020] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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, a 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), and an APU (Accelerated Processing Unit).

[0024] 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.

[0025] 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.

[0026] 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), Bluetooth (registered trademark), etc.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0032] 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.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] The present invention provides a system for efficiently monitoring wildlife, checking environmental health, and detecting illegal logging. Each system is implemented as follows.

[0042] Wildlife Monitoring System

[0043] Program processing

[0044] 1. The device places cameras in specific forest areas, and when the cameras detect wildlife movement, they capture image data.

[0045] 2. The device compresses the captured image data and sends it to the server in real time.

[0046] 3. The server stores the received image data and analyzes it using AI algorithms, specifically using image recognition technology to identify the type and movement of wildlife.

[0047] 4. The server visualizes the analysis results in a dashboard, allowing users to easily check the results.

[0048] 5. If the server detects any new findings or abnormal behavior, it sends an alert to the device and notifies the user.

[0049] Specific examples

[0050] A user sets up a camera in a forest area. The camera captures the movements of large animals (e.g., bears) at night, and the device sends the image data to a server in real time. The server analyzes the image, identifies the presence of a bear, and displays that information on a dashboard. At the same time, an alert is sent to the user that a bear has appeared.

[0051] Environmental Health Check System

[0052] Program processing

[0053] 1. The device places sensors in forests, bodies of water, etc. The sensors collect environmental data such as temperature, humidity, and pH.

[0054] 2. The device sends the collected data to the server in real time.

[0055] 3. The server stores the received data and analyzes it using AI algorithms to detect abnormal values ​​(e.g., sudden fluctuations in pH).

[0056] 4. The server visualizes the analysis results in a dashboard, allowing users to easily check the results.

[0057] 5. If the server detects an abnormality, it sends that information to the terminal and notifies the user.

[0058] Specific examples

[0059] The device detects a sudden rise in pH value using sensors installed in the water area and sends the data to the server in real time. The server analyzes the data, determines whether there is a possibility of water pollution, and displays the results on a dashboard. At the same time, an alert is sent to the user, informing them that there is a possibility of water pollution.

[0060] Illegal logging detection system

[0061] Program processing

[0062] 1. The terminal deploys a drone and ground sensors in the monitoring area. The drone periodically flies over the monitoring area and captures image data.

[0063] 2. The device sends the captured data to the server. Ground sensors also collect and send data.

[0064] 3. The server stores the received data and analyzes it using AI algorithms to detect patterns of illegal logging.

[0065] 4. The server visualizes the analysis results in a dashboard, allowing users to easily check the results.

[0066] 5. If the server detects any suspicion of illegal logging, it sends the information to the terminal and notifies the user.

[0067] Specific examples

[0068] A user uses a drone to monitor a wide area of ​​forest. The drone captures abnormal logging activity in a certain area, and the device sends the data to a server in real time. The server analyzes the data, determines whether there is any suspicion of illegal logging, and displays the information on a dashboard. At the same time, an alert is sent to the user informing them of the possibility of illegal logging.

[0069] This process allows for efficient, real-time wildlife monitoring, environmental health checks, and illegal logging detection, allowing users to take immediate action.

[0070] The processing flow will be explained below.

[0071] Wildlife Monitoring System

[0072] Program processing

[0073] Step 1:

[0074] The device will set up a camera in the forest area, power it on, and check its operation. If there are no abnormalities, it will enter outdoor surveillance mode.

[0075] Step 2:

[0076] The camera detects motion and captures wildlife movements, triggering a capture and generating continuous image data.

[0077] Step 3:

[0078] The terminal compresses the captured image data, encrypts it using a data transfer protocol, and then transmits it to the server.

[0079] Step 4:

[0080] The server stores the image data received from the device, and simultaneously performs backup processing to ensure data integrity.

[0081] Step 5:

[0082] The server analyzes the received image data using AI algorithms. Image recognition technology is used to identify the type and movement of wildlife, for example, categorizing animals based on their shape, color, and size.

[0083] Step 6:

[0084] The server visualizes the analysis results on a dashboard, generating graphs and charts to help users intuitively understand the results.

[0085] Step 7:

[0086] If the server detects a new discovery or anomaly, it sends an alert to the device. The notification includes details of the analysis results and an image. The device then issues a notification to the user.

[0087] Environmental Health Check System

[0088] Program processing

[0089] Step 1:

[0090] The device will install environmental sensors in forests and water areas, and check that the sensors are working properly and can collect data properly.

[0091] Step 2:

[0092] Sensors collect environmental data (temperature, humidity, pH, etc.) at set intervals. As data is collected, it is saved in temporary storage.

[0093] Step 3:

[0094] The device periodically transmits data obtained from the sensors to the server, and the data is encrypted during transmission.

[0095] Step 4:

[0096] The server receives the data sent from the device and stores it in a database, also checking the integrity of the received data.

[0097] Step 5:

[0098] The server analyzes the received data in real time and uses AI algorithms to detect anomalies in the environmental data, for example, identifying abnormal fluctuations compared to established baseline values.

[0099] Step 6:

[0100] The server visualizes the analysis results on a dashboard, and if anomalies are detected, details are displayed in graphs and charts.

[0101] Step 7:

[0102] If the server detects an anomaly, it sends an alert to the device, which includes the specific anomaly value and location information. The device then issues a notification to the user.

[0103] Illegal logging detection system

[0104] Program processing

[0105] Step 1:

[0106] The terminal places the drone and ground sensors in the monitoring area, sets the drone's flight path, and checks the sensor's operation.

[0107] Step 2:

[0108] The drone flies according to a set schedule and captures image data of the monitored area, while ground sensors simultaneously collect data.

[0109] Step 3:

[0110] The device compresses the data obtained from the drone and sensors and transmits it to a server using a secure protocol.

[0111] Step 4:

[0112] The server receives the data sent from the device and stores it in a database. After receiving the data, it checks it to ensure its integrity.

[0113] Step 5:

[0114] The server analyzes the received data using AI algorithms. Image analysis and pattern recognition technology are used to detect possible illegal logging. For example, if a large amount of timber is being cut all at once, the system will detect any changes.

[0115] Step 6:

[0116] The server visualizes the analysis results on a dashboard, displaying them on graphs and maps to help users intuitively understand the results.

[0117] Step 7:

[0118] If the server detects any suspected illegal logging, it sends an alert to the device, which then issues a notification to the user, including specific location information and the results of image analysis.

[0119] Example 1

[0120] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0121] Current wildlife monitoring systems, environmental health check systems, and illegal logging detection systems often lack efficiency and accuracy in real-time data processing and anomaly detection. They also need to operate accurately at night and in bad weather. To address these issues, effective use of sensor technology and AI algorithms is essential.

[0122] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0123] In this invention, the server includes a means for analyzing received data using an AI algorithm to detect anomalies or new patterns, a dashboard means for visually displaying the analysis results, and a notification means for issuing a notification to the user when an anomaly or new pattern is detected, thereby enabling high-precision data analysis and anomaly detection in real time.

[0124] "Camera means" refers to a photographic device installed to capture the movements of wildlife, and is equipped with the ability to detect animal movements using infrared sensors even at night.

[0125] "Terminal means" is a device that compresses data obtained from sensors and cameras and transmits it to a server using a secure communication protocol.

[0126] "Server means" refers to a system that receives data sent from terminal means, stores it, and analyzes it using an AI algorithm.

[0127] The "analysis means" is a function that uses an AI algorithm to analyze data received by the server means, recognize the types and movements of wildlife, and detect environmental data and illegal logging.

[0128] The "dashboard means" is an interface that visually displays the analysis results so that the user can easily check them, and uses visualization tools to represent the results in graphs and charts.

[0129] "Notification methods" are functions that notify users when an abnormality or new discovery is detected, and provide information via email, SMS, push notifications, etc.

[0130] "Sensor means" refers to a device that collects environmental data such as temperature, humidity, pH, etc., and is used to obtain data with high accuracy.

[0131] A "drone vehicle" is an unmanned aerial vehicle that flies over a surveillance area, monitors the area using a high-resolution camera, and collects data.

[0132] A "generative AI model" is a learning model that uses AI algorithms to analyze specific data and perform pattern recognition and anomaly detection.

[0133] A "prompt sentence" is an input sentence that instructs a generative AI model to perform a specific analysis or recognition.

[0134] The present invention is an efficient and highly accurate system for wildlife monitoring, environmental health checks, and illegal logging detection. The detailed methods described below illustrate the preferred embodiments of the invention.

[0135] Wildlife Monitoring System

[0136] The device installs a high-resolution camera in a specific forest area. The camera has motion detection capabilities, and in particular, can detect animal movement even at night using an infrared sensor. When motion is detected, the camera captures image data. At the same time, the device compresses this image data using the JPEG compression algorithm and transmits it to a server in real time using a secure communication protocol (e.g., HTTPS).

[0137] The server stores the received image data in a database management system (e.g., MySQL®). The server then applies an AI algorithm (e.g., an image recognition model using TENSORFLOW®) to analyze the image data. This analysis identifies the type and movement of wildlife.

[0138] Finally, the analysis results are visually displayed through a dashboard. Visualization tools such as D3.js and Tableau are used for visualization, allowing users to easily check the analysis results. In addition, if abnormal behavior or new discoveries are detected, the server creates an alert and sends it to the terminal, which notifies the user.

[0139] Specific examples

[0140] A user sets up a camera in a forest area. The camera captures the movements of large animals (e.g., bears) at night, and the device sends the image data to a server in real time. The server analyzes the image, identifies the presence of a bear, and displays that information on a dashboard. At the same time, an alert is sent to the user that a bear has appeared.

[0141] Example prompts for generative AI models

[0142] Identify the type of wildlife captured by this camera.

[0143] Environmental Health Check System

[0144] The terminal places sensors in water bodies or forests. The sensors collect environmental data such as temperature, humidity, and pH. These sensors are typically built using Arduino or Raspberry Pi. The collected data is sent to a server in real time via the terminal. MQTT or HTTP is used as the communication protocol.

[0145] The server stores the received data in a database management system (e.g., PostgreSQL), and then applies AI algorithms to analyze the environmental data in real time to detect abnormalities, such as sudden pH fluctuations.

[0146] The analysis results are visually displayed on a dashboard, allowing users to easily check them. If an abnormality is detected, the server generates an alert and the device notifies the user.

[0147] Specific examples

[0148] The device detects a sudden rise in pH value using sensors installed in the water area and sends the data to the server in real time. The server analyzes the data, determines whether there is a possibility of water pollution, and displays the results on a dashboard. At the same time, the user is informed that there is a possibility of water pollution.

[0149] Example prompts for generative AI models

[0150] Detect unusual environmental fluctuations from this data.

[0151] Illegal logging detection system

[0152] The terminal places a drone and ground sensors in the monitoring area. The drone, equipped with a high-resolution camera, flies periodically to capture images of the monitoring area, while the ground sensors detect changes in the soil and vibrations from heavy machinery. Data obtained from the drone and sensors is sent to a server in real time via the terminal. A secure communication protocol (e.g., SSL / TLS) is used for transmission.

[0153] The server stores the received data in a database management system and uses AI algorithms (for example, analytical models using Keras) to analyze patterns of illegal logging. If the results are determined to be abnormal, an alert is generated.

[0154] The analysis results are displayed on a dashboard, where users can check them. If any abnormal monitoring activity is detected, the server sends an alert to the terminal, notifying the user.

[0155] Specific examples

[0156] A user uses a drone to monitor a wide area of ​​forest. The drone captures abnormal logging activity in a certain area, and the device sends the data to a server in real time. The server analyzes the data, determines whether there is any suspicion of illegal logging, and displays the information on a dashboard. At the same time, an alert is sent to the user informing them of the possibility of illegal logging.

[0157] Example prompts for generative AI models

[0158] Detect patterns of illegal logging in this image.

[0159] This process allows for efficient, real-time wildlife monitoring, environmental health checks, and illegal logging detection, allowing users to take immediate action.

[0160] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0161] Wildlife Monitoring System

[0162] Step 1: Camera installation and motion detection

[0163] The device installs a high-resolution camera in a forest area. Location information and installation angle are required as input. The device has a motion detection function and uses an infrared sensor to detect animal movements even at night. When movement is detected, the camera captures image data. The captured image data is obtained as output.

[0164] How it works: The camera uses a motion detection algorithm to detect movement and an infrared sensor to capture animals even at night.

[0165] Step 2: Compress and transmit image data

[0166] The device receives the captured image data as input and compresses it using the JPEG compression algorithm. As output, the compressed image data is obtained, which is then transmitted to the server in real time using a secure communication protocol (e.g., HTTPS).

[0167] Specific operation: The device applies a compression algorithm to reduce the data size and transmits it over a secure network.

[0168] Step 3: Saving and analyzing image data

[0169] The server receives the compressed image data as input and stores it using a database management system (e.g., MySQL). The stored image data is then analyzed using an AI algorithm (e.g., an image recognition model using TensorFlow). The analysis results (animal species and movements) are obtained as output.

[0170] Specific operation: The server stores the received data in a database and runs an image recognition model to perform analysis.

[0171] Step 4: Visualize the analysis results

[0172] The server receives the analysis results and displays them visually using a dashboard. The input required is the analysis results (animal species and movements). The output is visualized information in the form of graphs and charts.

[0173] Specific operation: The server uses a visualization tool (e.g., D3.js or Tableau) to generate an interface that displays the analysis results.

[0174] Step 5: Alert Notification

[0175] When the server detects a new discovery or abnormal behavior, it uses the alert generation system to generate an alert as input and sends it to the terminal. As output, it gets an alert notification, which the terminal sends to the user.

[0176] Specific operation: The server uses an anomaly detection algorithm to create an alert and notify the user via email or SMS.

[0177] Environmental Health Check System

[0178] Step 1: Sensor installation and data collection

[0179] The terminal installs sensors in water bodies or forests. The installation location and the type of sensor are required as inputs. The sensors collect environmental data such as temperature, humidity, pH, etc. The collected environmental data is obtained as output.

[0180] Specific operation: The sensor measures a specific environmental parameter and sends the data to the device.

[0181] Step 2: Sending data

[0182] The terminal receives collected environmental data as input and transmits it to the server in real time. The transmitted data is obtained as output. MQTT and HTTP are used as communication protocols.

[0183] Specific operation: The device formats the data and sends it using a secure communication protocol.

[0184] Step 3: Data storage and analysis

[0185] The server receives the received environmental data as input and stores it using a database management system (e.g., PostgreSQL). The stored data is then analyzed in real time using an AI algorithm. The analysis results (anomalous environmental data) are obtained as output.

[0186] Specific operation: The server stores the data in a database and performs analysis using an anomaly detection model.

[0187] Step 4: Visualize the analysis results

[0188] The server receives the analysis results and visually displays them using a dashboard. As input, the analysis results (anomalous environmental data) are required. As output, visualized information is obtained in the form of graphs and charts.

[0189] Specific operation: The server uses a visualization tool to generate an interface that displays the analysis results.

[0190] Step 5: Alert Notification

[0191] When the server detects an anomaly, it uses the alert generation system to generate an alert as input and sends it to the terminal. As output, an alert notification is obtained. The terminal then sends the notification to the user.

[0192] Specific operation: The server uses an anomaly detection algorithm to create an alert and notify the user via email or SMS.

[0193] Illegal logging detection system

[0194] Step 1: Drone and ground sensor placement

[0195] The terminal deploys drones and ground sensors in the monitoring area. The deployment location and drone flight plan are required as inputs. The drone monitors the area using a high-resolution camera, and the ground sensors detect changes in the soil and vibrations from heavy machinery. The output is the captured image data and sensor data.

[0196] How it works: The drone flies periodically, taking pictures of the area with its camera, and ground sensors collecting environmental data.

[0197] Step 2: Capturing and Sending Data

[0198] The terminal receives data from the drone and ground sensors as input and transmits it to the server in real time. The transmitted data is obtained as output. A secure communication protocol (e.g., SSL / TLS) is used for transmission.

[0199] What happens: The device formats the data and sends it over a secure network.

[0200] Step 3: Data storage and analysis

[0201] The server receives the received data as input and stores it using a database management system (e.g., MySQL). The stored data is then analyzed using an AI algorithm (e.g., an analytical model using Keras). The analysis results (patterns of illegal logging) are obtained as output.

[0202] Specific operation: The server stores the received data in a database and runs an analytical model to detect anomalies.

[0203] Step 4: Visualize the analysis results

[0204] The server receives the analysis results and displays them visually using a dashboard. The input required is the analysis results (patterns of illegal logging). The output is visualized information in the form of graphs and charts.

[0205] Specific operation: The server uses a visualization tool to generate an interface that displays the analysis results.

[0206] Step 5: Alert Notification

[0207] When the server detects suspected illegal logging, it uses the alert generation system to generate an alert as input and sends it to the terminal. The output is an alert notification, which the terminal then sends to the user.

[0208] Specific operation: The server uses an anomaly detection algorithm to create an alert and notify the user via email or SMS.

[0209] (Application example 1)

[0210] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0211] In modern society, effective wildlife monitoring, environmental health checks, and illegal logging detection are crucial. However, performing these monitoring and detection tasks efficiently and in real time poses technical challenges, particularly in the areas of wide-area monitoring and real-time data notification. Furthermore, existing systems are complex and require specialized knowledge, making them difficult for the general public to use. A system that can solve these problems and maintain a safe and healthy environment is needed.

[0212] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0213] In this invention, the server includes a photographing device, a transmitting device, a cloud server, an analysis device, a display device, a notification device, and a real-time notification device. This enables wide-area monitoring, real-time data analysis, and rapid notification to users. Furthermore, by using a generative AI model, a system is provided that can analyze environmental data and wildlife movements with high accuracy and quickly detect and notify abnormalities.

[0214] "Photography Device Means" refers to videography equipment for capturing wildlife and environmental activity.

[0215] The "transmission device means" is a device for compressing data obtained from the image capture device or sensor device and transmitting it to the cloud server.

[0216] "Cloud Server Means" refers to a remote computing resource for receiving, storing, and analyzing data transmitted from a Transmitter Means.

[0217] The "analysis means" is a means for analyzing data received by the cloud server means using a machine learning algorithm to recognize the type and movements of wildlife and detect illegal logging and environmental abnormalities.

[0218] The "display device means" is a device for visually displaying the results obtained by the analysis means.

[0219] "Notification device means" is a device for issuing a notification to the user in the event of an abnormality or new discovery.

[0220] The "real-time notification means" is a means for notifying the mobile terminal in real time of the data collected by the photographing device means and the transmitting device means.

[0221] A "sensor device means" is a device for collecting environmental data such as temperature, humidity, pH, etc.

[0222] "Unmanned Aerial Vehicle Means" means an unmanned aerial vehicle for periodically monitoring and collecting data from a surveillance area.

[0223] A "generative AI model" is an artificial intelligence model that is trained based on large datasets to perform specific tasks with high accuracy.

[0224] "Visualization means" refers to a means for visually displaying analysis results on the cloud.

[0225] This invention is a system for efficiently monitoring wildlife, checking environmental health, and detecting illegal logging. This system collects data using various imaging and sensor devices, transmits the data to a cloud server for analysis, and provides information to users in real time.

[0226] The server first receives image data obtained from the photographing device means and environmental data obtained from the sensor device means. Specific hardware examples include cameras for the photographing device means and temperature, humidity, and pH sensors for the sensor device means. These data are then transmitted to the cloud server via the transmitting device means. This transmission utilizes an internet connection.

[0227] The cloud server stores the received data and analyzes it using machine learning algorithms. Software such as TensorFlow and PyTorch is used for this analysis. The analysis results are visualized within the cloud server and provided to the user through the display device means. The results are displayed in a dashboard format so that the user can easily check them. In addition, if an abnormality or new discovery is detected, the notification device means issues an alert to the user through the real-time notification means.

[0228] As a practical example, a camera is installed in a forest area and captures the movement of wildlife (e.g., a bear) at night. The captured image data is sent to a cloud server in real time, where an AI algorithm analyzes the image and detects the presence of a bear. The results are displayed on a dashboard, and an alert is sent to the user that a "bear has appeared." This allows the user to respond quickly.

[0229] The system uses a generative AI model to analyze data with high accuracy and quickly detect anomalies. The generative AI model is trained using prompts such as:

[0230] "Please provide us with a dataset to identify animal species. Please provide us with an AI algorithm to develop a system that can monitor animal movements in real time using an environmentally friendly approach."

[0231] According to the embodiment of the present invention, users can efficiently and accurately monitor a wide area, check the environment, and detect illegal logging. This system will greatly contribute to sustainable environmental management and safety management.

[0232] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0233] Step 1:

[0234] A camera captures the movement. The user configures and positions the camera to cover a specific area. When the camera detects movement, such as wildlife or illegal logging, it captures the movement as image data.

[0235] (Input: Wildlife or target movement,raw data; Output: Captured image data)

[0236] Step 2:

[0237] The device compresses the captured image data and sends it to the cloud server. The device compresses the captured raw data for efficient transmission. The compressed data is then sent to the cloud server via Wi-Fi or mobile network.

[0238] (Input: captured image data; Output: compressed image data)

[0239] Step 3:

[0240] The cloud server stores the received image data and analyzes it using machine learning algorithms. The cloud server stores the data in a database and analyzes it using machine learning frameworks such as TensorFlow and PyTorch. The analysis is performed using a generative AI model to identify the type of animal and its movements from the data.

[0241] (Input: Compressed image data; Output: Recognition results of animal species and movements)

[0242] Step 4:

[0243] The server visualizes the analysis results on the cloud and provides them to the user through a display device. The analyzed data is provided to the user in the form of graphs and dashboards, which can be accessed from a web browser or a dedicated application.

[0244] (Input: Recognition results of animal species and movements; Output: Visualized analysis results on a dashboard)

[0245] Step 5:

[0246] If the server detects an anomaly or new discovery, it will issue a real-time notification to the user through a notification device. When the server detects an anomaly based on the analysis results, it will quickly send a notification to the user's smartphone or computer. The notification will be delivered as a text message or in-app notification.

[0247] (Input: Anomaly detection results; Output: Real-time notification)

[0248] Step 6:

[0249] Users receive notifications and access the system dashboard to view detailed data. Users can check alerts from their smartphones or computers, log in to the dashboard, and view footage and data analysis results.

[0250] (Input: Real-time notification; Output: User confirmation of detailed data)

[0251] These processing steps enable the system to efficiently monitor wildlife, check environmental health, and detect illegal logging in real time. The use of generative AI models enables highly accurate data analysis, prompting users to take immediate action.

[0252] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0253] The present invention provides a system that provides more advanced feedback by combining an emotion engine that recognizes user emotions with a system that efficiently monitors wildlife, checks environmental health, and detects illegal logging. Specific embodiments of the present invention are described below.

[0254] Wildlife Monitoring System

[0255] Program processing

[0256] 1. The device installs a camera in a forest area and performs an operation check. When the camera detects movement, it captures image data.

[0257] 2. The device compresses the captured image data and sends it to the server using an encryption protocol.

[0258] 3. The server stores the received data and analyzes it using AI algorithms, using image recognition technology to identify the type and movements of wildlife.

[0259] 4. The server visualizes the analysis results on a dashboard, displaying them in graphs and charts so that users can intuitively understand the results.

[0260] 5. If the server detects an abnormality or new discovery, it will send an alert to the device using a notification method.

[0261] 6. The device issues an alert to the user, and at the same time, the emotion engine analyzes the user's reaction, thereby monitoring the user's emotional state in real time.

[0262] 7. The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user reacts with surprise or joy, that information is collected.

[0263] 8. The server adjusts the content and format of notifications based on the emotional data, personalizing feedback based on the user’s emotional state.

[0264] Specific examples

[0265] A user sets up a camera in a forested area. The camera captures the movements of large animals (e.g., bears) at night, and the device sends the data in real time to a server. The server analyzes the images, identifies the presence of a bear, and displays that information on a dashboard. The server then sends an alert to the user that a "bear has appeared." The device analyzes the user's reaction using an emotion engine, and if the user is surprised, the emotion data is sent to the server, and the next notification will be more gentle.

[0266] Environmental Health Check System

[0267] Program processing

[0268] 1. The device installs sensors in forests and water areas and checks their operation. The sensors collect environmental data (temperature, humidity, pH, etc.).

[0269] 2. The device sends the collected data to the server in real time.

[0270] 3. The server receives and stores the data, then uses AI algorithms to analyze it and detect outliers.

[0271] 4. The server displays the analysis results on a dashboard and issues an alert if an abnormal value is detected.

[0272] 5. If the server detects an abnormality, it sends that information to the terminal and notifies the user.

[0273] 6. At the same time that the device issues an alert, the emotion engine analyzes the user's reaction, monitoring the user's emotional state in real time.

[0274] 7. The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user shows a nervous reaction, that information is collected.

[0275] 8. The server adjusts the content and format of notifications based on the emotional data, personalizing feedback based on the user’s emotional state.

[0276] Specific examples

[0277] The device installs a sensor in the water body and detects a sudden rise in pH level, sending the data to the server in real time. The server analyzes the data and determines that there is a possibility of water pollution. The server then sends an alert to the user stating that there is a possibility of water pollution. The device then analyzes the user's reaction using an emotion engine, and if the user becomes nervous, the emotion data is sent to the server, and the next notification will be more reassuring.

[0278] Illegal logging detection system

[0279] Program processing

[0280] 1. The device places drones and ground sensors in the surveillance area and sets the drone's flight path.

[0281] 2. Drones fly over the monitored area periodically and capture image data, and ground sensors collect data as well.

[0282] 3. The device compresses the captured data and sends it to the server using an encryption protocol.

[0283] 4. The server stores the received data and analyzes it using AI algorithms to detect patterns of illegal logging.

[0284] 5. The server visualizes the analysis results on a dashboard, allowing users to easily and intuitively understand the results.

[0285] 6. If the server detects suspicion of illegal logging, it will send an alert to the terminal using a notification method.

[0286] 7. The device issues an alert to the user, and at the same time, the emotion engine analyzes the user's reaction, monitoring the user's emotional state in real time.

[0287] 8. The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user reacts impatient or angry, that information is collected.

[0288] 9. The server adjusts the content and format of notifications based on the emotional data, personalizing feedback based on the user’s emotional state.

[0289] Specific examples

[0290] A user uses a drone to monitor a wide area of ​​forest. The drone captures abnormal logging activity in a certain area, and the device sends the data in real time to a server. The server analyzes the data and determines that illegal logging is suspected. The server then sends an alert to the user stating that "illegal logging is suspected." The device uses an emotion engine to analyze the user's reaction, and if the user becomes panicked, the emotional data is sent to the server, and the next notification will be delivered with content that allows the user to respond more calmly.

[0291] This process allows for efficient, real-time wildlife monitoring, environmental health checks, and illegal logging detection, while also providing personalized feedback based on the user's emotional state.

[0292] The processing flow will be explained below.

[0293] Combining emotion engines with wildlife monitoring systems

[0294] Program processing

[0295] Step 1:

[0296] The device installs a camera in the forest area. It turns on the camera and checks its operation. After confirming that it is working properly, it starts monitoring mode.

[0297] Step 2:

[0298] The camera detects motion and captures wildlife movements, triggering the generation of continuous image data.

[0299] Step 3:

[0300] The device compresses the captured image data and transmits it to a server in real time using an encryption protocol.

[0301] Step 4:

[0302] The server stores the received data and also performs backup processing to ensure data safety.

[0303] Step 5:

[0304] The server analyzes the stored image data with AI algorithms and uses image recognition technology to identify the type and movement of wildlife, for example by classifying animals based on their shape, color, and size.

[0305] Step 6:

[0306] The server visualizes the analysis results on a dashboard, generating and displaying graphs and charts that are easy for users to understand intuitively.

[0307] Step 7:

[0308] If the server detects any new findings or anomalies, it will use a notification mechanism to send an alert to the device, including details of the analysis results and an image.

[0309] Step 8:

[0310] The device issues an alert to the user. At the same time, the emotion engine analyzes the user's reaction and monitors the user's emotional state in real time after seeing the alert.

[0311] Step 9:

[0312] The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user shows a reaction of surprise or joy, that information is collected.

[0313] Step 10:

[0314] The server adjusts the content and format of notifications based on the emotional data, so the next notification will be personalized based on the user's emotional state.

[0315] Combining an environmental health check system with an emotion engine

[0316] Program processing

[0317] Step 1:

[0318] The device will install environmental sensors in forests and water areas, and check the operation of the sensors to ensure they are working properly.

[0319] Step 2:

[0320] The sensor collects environmental data (temperature, humidity, pH, etc.) at set intervals and stores the data in temporary storage.

[0321] Step 3:

[0322] The data collected by the terminal is sent to the server at regular intervals using an encryption protocol.

[0323] Step 4:

[0324] The server stores the received data, checks its integrity, and simultaneously backs up the data.

[0325] Step 5:

[0326] The server analyzes the received data in real time and uses AI algorithms to detect abnormalities in the environmental data (for example, sudden changes in pH).

[0327] Step 6:

[0328] The server visualizes the analysis results on a dashboard, and if anomalies are detected, details are displayed in graphs and charts.

[0329] Step 7:

[0330] If the server detects an abnormality, it will use a notification method to send an alert to the device, which will include the specific abnormal value and location information.

[0331] Step 8:

[0332] The device issues an alert to the user. At the same time, the emotion engine analyzes the user's reaction and monitors the user's emotional state in real time after seeing the alert.

[0333] Step 9:

[0334] The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user shows a nervous reaction, that information is collected.

[0335] Step 10:

[0336] The server adjusts the content and format of notifications based on the emotional data, so the next notification will be personalized based on the user's emotional state.

[0337] Combining an emotion engine with an illegal logging detection system

[0338] Program processing

[0339] Step 1:

[0340] The terminal places the drone and ground sensors in the monitoring area, sets the drone's flight path, and performs operational checks to confirm normal operation.

[0341] Step 2:

[0342] Drones fly according to a set schedule and capture image data of the monitored area, and ground sensors collect data as well.

[0343] Step 3:

[0344] The device compresses the captured data and sends it to the server using an encryption protocol.

[0345] Step 4:

[0346] The server saves the received data, checks the integrity of the data, and simultaneously backs up the received data.

[0347] Step 5:

[0348] The server analyzes the received data using AI algorithms to detect patterns of illegal logging. For example, if a large amount of timber is being cut all at once, it will detect this change.

[0349] Step 6:

[0350] The server visualizes the analysis results on a dashboard, displaying them in graphs and maps that are easy for users to understand intuitively.

[0351] Step 7:

[0352] If the server detects any suspicion of illegal logging, it will use a notification method to send an alert to the device, which will include specific location information and the results of image analysis.

[0353] Step 8:

[0354] The device issues an alert to the user. At the same time, the emotion engine analyzes the user's reaction and monitors the user's emotional state in real time after seeing the alert.

[0355] Step 9:

[0356] The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user shows impatience or anger, that information is collected.

[0357] Step 10:

[0358] The server adjusts the content and format of notifications based on the emotional data, so the next notification will be personalized based on the user's emotional state.

[0359] This process allows for efficient, real-time wildlife monitoring, environmental health checks, and illegal logging detection, while also providing personalized feedback based on the user's emotional state.

[0360] Example 2

[0361] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0362] While existing systems for wildlife monitoring, environmental health checks, and illegal logging detection are efficient at collecting and analyzing data, they lack the ability to personalize feedback based on the user's emotional state. As a result, the amount and quality of information users receive is constant, and they lack the flexibility to adapt to specific situations. Furthermore, when detecting anomalies or new discoveries, they lack the interactive support to help users respond calmly.

[0363] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a photographing means, a device means, a server means, an analysis means, a display means, a notification means, an emotion engine and a means for transmitting the data to the server, and a feedback means. This makes it possible to efficiently collect data, analyze it in real time, and adjust the content and format of notifications based on the user's emotions. By analyzing the user's reactions with the emotion engine and providing personalized feedback according to each situation, a system is realized that can provide the optimal response for each usage scenario.

[0364] "Capture means" refers to a device that captures wildlife and environmental movements and obtains image data.

[0365] "Device means" refers to a device for compressing data obtained from the imaging means or sensor means and transmitting it to a server using an encryption protocol.

[0366] "Server means" refers to a server that receives data transmitted from device means, decrypts and stores it, and further analyzes it.

[0367] "Analysis means" refers to the function of analyzing data received by the server means using an AI algorithm to recognize or detect the type and movement of wildlife, abnormalities in environmental data, and illegal logging.

[0368] "Display means" refers to an interface that visually displays the analysis results and shows them in graphs or charts so that the user can intuitively understand them.

[0369] "Notification means" refers to the function of sending an alert to the user via the terminal in the event of an abnormality or new discovery.

[0370] An "emotion engine" refers to an analysis device that analyzes a user's reaction and sends the results to a server.

[0371] "Feedback means" refers to a function that adjusts the content and format of notifications based on the user's emotional data, thereby personalizing feedback.

[0372] The present invention provides a system for efficiently monitoring wildlife, checking environmental health, and detecting illegal logging by combining it with an emotion engine that recognizes user emotions to provide more advanced feedback. Specific embodiments of the present invention are described below.

[0373] Basic system configuration

[0374] This system is mainly composed of the following means:

[0375] 1. Filming Method

[0376] The photographing means includes a camera device for capturing wildlife movements, such as a highly sensitive infrared camera or a camera with motion detection capabilities. The camera is installed in a designated forest area and captures image data when it detects movement.

[0377] 2. Equipment means

[0378] The device means compresses the image data obtained from the photographing means and the environmental data obtained from the sensor means, protects the data using the AES encryption protocol, and transmits it to the server. Data compression reduces the communication load and enables efficient data transmission.

[0379] 3. Server Means

[0380] The server means receives, decrypts, and stores data sent from the device means. The server is equipped with high-performance storage capable of processing large amounts of data and an AI algorithm (e.g., YOLO or ResNet) for data analysis.

[0381] 4. Analysis method

[0382] The analysis means uses AI algorithms to analyze data received and stored in the server means, and has the ability to recognize the types and movements of wildlife. Environmental data and illegal logging detection are also analyzed in the same way.

[0383] 5. Display means

[0384] The display means is a dashboard interface that visually represents the results of the analysis performed by the server. The dashboard visualizes the results in graphs and charts, allowing users to intuitively understand them.

[0385] 6. Means of notification

[0386] The notification function has the function of sending alerts to users if an abnormality or new discovery is detected based on the analysis results. Notifications are delivered to users via mobile app, email, or SMS.

[0387] 7. Emotion Engine

[0388] The emotion engine has the function of analyzing the user's reactions and sending the data to the server. For example, it analyzes the user's reactions such as surprise, joy, and tension, and collects the emotional data.

[0389] 8. Feedback channels

[0390] The feedback mechanism has a function that allows the server to adjust the content and format of the next notification based on data from the emotion engine, thereby personalizing the feedback, thereby providing the most appropriate information for the user.

[0391] Specific examples

[0392] For example, if a user installs a camera in a forest area and the camera captures the movement of a large animal (e.g., a bear) at night, the image data is sent in real time to a server by a device means. The server analyzes the data, identifies the presence of a bear, and displays the information on a dashboard. Based on the analysis results, the server sends an alert to the user through a notification means that a "bear has appeared." If the device analyzes the user's reaction with an emotion engine, and the user is surprised, the emotion data is sent to the server, and the next notification will be provided in a gentler tone.

[0393] Prompt Sentence Examples

[0394] "Create a program that describes a system in which a user-installed camera detects movement in a forested area and sends the image data to a server. The server then analyzes the data to identify the type of wildlife and notify the user."

[0395] As can be seen, the present invention can enhance wildlife monitoring, environmental health checks, illegal logging detection, and provide personalized feedback based on the user's emotional state.

[0396] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0397] Step 1:

[0398] The user installs the imaging device (camera) in a forest area and checks its operation. The user fixes the camera in an area specified by the user and checks that the motion detection function is working properly. The input for the imaging device includes the setting parameters for detecting motion and camera position information. The output is image data generated when motion is detected.

[0399] Step 2:

[0400] Image data is captured when the imaging means detects motion. For example, a camera may trigger a shutter in response to wildlife movement. The input is the detection result of a motion detection sensor, and the output is the captured image data.

[0401] Step 3:

[0402] The device compresses the image data obtained from the image capture means and sends it to the server using an encryption protocol. Specifically, the image data is compressed into a format such as JPEG and encrypted using an encryption method such as AES256. The input is the captured image data, and the output is compressed and encrypted data.

[0403] Step 4:

[0404] The server receives the data sent from the terminal, decrypts it, and stores it. Specifically, the server receives the encrypted data and decrypts it using an AES256 key. It then stores it in a database. The input is encrypted image data, and the output is the original decrypted image data, which is then stored in the database.

[0405] Step 5:

[0406] The server then analyzes the decoded data using AI algorithms. For example, image recognition techniques such as YOLO or ResNet are used to identify the wildlife species and their movements. The input is the decoded image data, and the output is information about the wildlife species and their movements.

[0407] Step 6:

[0408] The server displays the analysis results on a dashboard. It converts the data into graphs and charts and visualizes them so that users can easily understand them. The input is the analysis result data, and the output is visualized graphs and charts displayed on the dashboard screen.

[0409] Step 7:

[0410] If the server detects an abnormality or a new discovery, it will send an alert to the device using a notification method. Specifically, it will send an alert message using email, SMS, etc. The input is the analysis result, and the output is an alert message that is generated and sent to the device.

[0411] Step 8:

[0412] The device issues an alert to the user and analyzes the user's reaction using an emotion engine. For example, the alert can be displayed through a mobile app or a dedicated device, and the emotion engine can analyze the user's facial expression and tone of voice. The input is an alert message from the server, and the output is the user's emotional data.

[0413] Step 9:

[0414] The emotion engine analyzes the user's emotion data and sends it to the server. Specifically, the user's facial expressions and tone of voice are sent to the server as numerical data. The input is the user's emotion data, and the output is the emotion data sent to the server.

[0415] Step 10:

[0416] The server adjusts the content and format of notifications based on the emotion data. For example, if the user is surprised, the next notification will be sent in a softer tone. The input is the emotion data sent by the emotion engine, and the output is the adjusted content and format of the notification.

[0417] Through these steps, the wildlife monitoring system can efficiently collect and analyze data and provide personalized feedback based on the user's emotional state.

[0418] (Application example 2)

[0419] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0420] While existing wildlife monitoring systems, environmental health check systems, and illegal logging detection systems are capable of advanced monitoring and data analysis, they lack the functionality to provide feedback based on the user's emotions, resulting in a limited user experience. In particular, if alert notifications cause excessive tension or anxiety in the user, this can hinder subsequent response.

[0421] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes emotion engine means for analyzing the user's emotion data, feedback means for transmitting the emotion data analyzed by the emotion engine means to the server and for personalizing the notification content, and notification means for issuing a notification to the user when an abnormality or new discovery is detected. This enables the alert notification to be personalized according to the user's emotional state, helping the user to respond appropriately and quickly.

[0422] "Wildlife" means any organism other than humans that inhabits the natural environment, especially animals.

[0423] "Camera means" refers to a device used to photograph wildlife and the environment and obtain image data thereof.

[0424] "Terminal means" refers to a device that compresses and transmits data obtained from camera means or sensor means.

[0425] "Server means" refers to a device or group of devices that receives and stores data sent from terminal means and performs the necessary processing.

[0426] "Analysis means" refers to a device or group of devices that analyzes data received by the server means using an AI algorithm and extracts and recognizes specific information.

[0427] The "dashboard means" is a device or interface for visually displaying the analysis results obtained by the analysis means.

[0428] "Notification means" refers to a device or function that issues an appropriate notification to the user in the event of an abnormality or new discovery.

[0429] The "emotion engine means" is a device or algorithm that analyzes the user's emotional data and personalizes the notification content based on that data.

[0430] The "feedback means" is a device or function for providing the user with optimal notification content using emotion data analyzed by the emotion engine means.

[0431] "Sensor means" refers to a device for collecting environmental data such as temperature, humidity, pH value, etc.

[0432] A "drone vehicle" is a flying device that periodically flies over a surveillance area from the air and captures image data.

[0433] This invention relates to an advanced environmental monitoring system for analyzing user emotions and optimizing the feedback. Specifically, by integrating an emotion engine into systems for wildlife monitoring, environmental health checks, and illegal logging detection, the system provides feedback according to the user's emotions.

[0434] 1. Wildlife Monitoring System

[0435] Hardware Configuration

[0436] In this system, multiple cameras are used to detect wildlife movement. When a camera detects movement, it captures image data and transmits it to a terminal device. The terminal device compresses and encrypts the data and transmits it to a server device.

[0437] Software Configuration

[0438] The server stores the received data and analyzes it using AI algorithms. The AI ​​uses image recognition technology to identify wildlife species and movements. The results are displayed visually on a dashboard in an intuitive format for users.

[0439] If an abnormality or new discovery is detected, the notification means issues a notification to the user. At the same time, the emotion engine analyzes the user's reaction and sends the emotional data to the server. Based on this data, the server personalizes the notification content and adjusts the next feedback.

[0440] Specific examples

[0441] A user sets up a camera in a forested area. The camera captures bear movements at night and sends the image data to a server. The server identifies the bear's presence and displays it on a dashboard. When an alert is sent to the user that a bear has appeared, the emotion engine analyzes the user's reaction to surprise. As a result, the next notification will be presented in a more toned-down format.

[0442] 2. Environmental Health Check System

[0443] Hardware Configuration

[0444] In this system, multiple sensors are used to collect environmental data such as temperature, humidity, pH, etc. The sensor means transmits the collected data in real time to the terminal means, and the terminal means transmits the data to the server means.

[0445] Software Configuration

[0446] The server means stores the received data and analyzes it using an AI algorithm. If an abnormality is detected, the notification means issues a notification to the user. The emotion engine analyzes the user's reaction and sends the emotion data to the server. The server uses this data to personalize the notification content.

[0447] Specific examples

[0448] A sensor installed by the user in a body of water detects a sudden rise in pH and sends the data to a server in real time. The server analyzes the data and determines that there is a possibility of water pollution. An alert is sent to the user stating "Possible water pollution" and an emotion engine analyzes the user's tense reaction. As a result, the next notification is presented in a reassuring format.

[0449] 3. Illegal Logging Detection System

[0450] Hardware Configuration

[0451] In this system, a drone is used to monitor a monitoring area from the air. Sensor means is also used to collect data on the ground. Data obtained from the drone and sensor means is transmitted to server means via terminal means.

[0452] Software Configuration

[0453] The server means stores the received data and uses an AI algorithm to detect illegal logging. If an abnormality is detected, the notification means issues a notification to the user. The emotion engine analyzes the user's reaction and sends the emotional data to the server. The server uses this data to personalize the notification content.

[0454] Specific examples

[0455] A user uses a drone to monitor a wide area of ​​forest. The drone captures abnormal logging activity in a certain area and sends the data to a server in real time. The server analyzes the data and detects possible illegal logging. An alert is sent to the user stating "Possible illegal logging" and an emotion engine analyzes the user's panicked reaction. As a result, the next notification is presented in a format that encourages a calm response.

[0456] Prompt Sentence Examples

[0457] "How can I combine sentiment analysis with notifications from my home security system to provide personalized feedback based on the user's response?"

[0458] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0459] Program processing flow

[0460] Step 1: The camera detects motion and captures an image

[0461] Input: The camera detects motion within the monitored area.

[0462] Data processing: Capture images using movement as a trigger.

[0463] Output: The captured image data is sent to the terminal means.

[0464] Specific operation: When the camera means detects movement, it automatically releases the shutter and saves the image at that time in memory.

[0465] Step 2: The device compresses the image data and sends it to the server

[0466] Input: Image data captured from a camera means.

[0467] Data processing: Compression and encryption of image data.

[0468] Output: Send the compressed image data to the server means.

[0469] Specific operation: The terminal means compresses the image data from the camera to reduce its size, and transmits the data to the server means using an encryption protocol.

[0470] Step 3: The server stores the received data and analyzes it with AI

[0471] Input: Compressed and encrypted image data sent from the terminal means.

[0472] Data processing: decryption and data storage.

[0473] Output: Extract the analysis results and send them to a dashboard.

[0474] What it does: The server decrypts the compressed data and stores it in a database. It uses AI algorithms to perform image recognition and identify wildlife species and movements.

[0475] Step 4: Display the analysis results on the dashboard

[0476] Input: Recognition results from AI analysis.

[0477] Data processing: Converting data into a visual format.

[0478] Output: Displayed on the dashboard.

[0479] Specific operation: The server means converts the analysis results into graphs or charts and displays them on the user interface.

[0480] Step 5: Issue a user notification when an error occurs

[0481] Input: Data whose analysis results indicate an anomaly.

[0482] Data processing: Generation of notification content.

[0483] Output: Sends a notification to the user.

[0484] Specific operation: If the notification means detects an abnormality, it will issue an alert to the user based on pre-defined rules.

[0485] Step 6: Analyze user reactions with the emotion engine

[0486] Input: Real-time user responses (e.g., data from a smartphone camera or sensors).

[0487] Data processing: Data analysis using sentiment analysis algorithms.

[0488] Output: Generate emotion data and send it to the server.

[0489] Specific operation: The emotion engine means performs facial and voice recognition on the user to identify emotions such as surprise or anxiety.

[0490] Step 7: The server analyzes the emotion data and adjusts the notification content.

[0491] Input: Emotion data sent from the emotion engine means.

[0492] Data processing: Feedback adjustment based on emotion data.

[0493] Output: Adjustment result for next notification content.

[0494] Specific operation: The server analyzes the emotional data and adjusts the next notification content, such as softening it, depending on the user's emotional state.

[0495] Step 8: Sending feedback to the server

[0496] Input: Reconciliation result by server means.

[0497] Data processing: generating the final feedback content.

[0498] Output: The revised notification is sent to the user.

[0499] Specific operation: The server means generates tailored notification content and sends feedback that gives the user peace of mind.

[0500] 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.

[0501] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0502] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0503] [Second embodiment]

[0504] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0505] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0506] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0507] 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.

[0508] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0509] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0510] 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.

[0511] 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.

[0512] 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 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.

[0513] 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.

[0514] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0515] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0516] The present invention provides a system for efficiently monitoring wildlife, checking environmental health, and detecting illegal logging. Each system is implemented as follows.

[0517] Wildlife Monitoring System

[0518] Program processing

[0519] 1. The device places cameras in specific forest areas, and when the cameras detect wildlife movement, they capture image data.

[0520] 2. The device compresses the captured image data and sends it to the server in real time.

[0521] 3. The server stores the received image data and analyzes it using AI algorithms, specifically using image recognition technology to identify the type and movement of wildlife.

[0522] 4. The server visualizes the analysis results in a dashboard, allowing users to easily check the results.

[0523] 5. If the server detects any new findings or abnormal behavior, it sends an alert to the device and notifies the user.

[0524] Specific examples

[0525] A user sets up a camera in a forest area. The camera captures the movements of large animals (e.g., bears) at night, and the device sends the image data to a server in real time. The server analyzes the image, identifies the presence of a bear, and displays that information on a dashboard. At the same time, an alert is sent to the user that a bear has appeared.

[0526] Environmental Health Check System

[0527] Program processing

[0528] 1. The device places sensors in forests, bodies of water, etc. The sensors collect environmental data such as temperature, humidity, and pH.

[0529] 2. The device sends the collected data to the server in real time.

[0530] 3. The server stores the received data and analyzes it using AI algorithms to detect abnormal values ​​(e.g., sudden fluctuations in pH).

[0531] 4. The server visualizes the analysis results in a dashboard, allowing users to easily check the results.

[0532] 5. If the server detects an abnormality, it sends that information to the terminal and notifies the user.

[0533] Specific examples

[0534] The device detects a sudden rise in pH value using sensors installed in the water area and sends the data to the server in real time. The server analyzes the data, determines whether there is a possibility of water pollution, and displays the results on a dashboard. At the same time, an alert is sent to the user, informing them that there is a possibility of water pollution.

[0535] Illegal logging detection system

[0536] Program processing

[0537] 1. The terminal deploys a drone and ground sensors in the monitoring area. The drone periodically flies over the monitoring area and captures image data.

[0538] 2. The device sends the captured data to the server. Ground sensors also collect and send data.

[0539] 3. The server stores the received data and analyzes it using AI algorithms to detect patterns of illegal logging.

[0540] 4. The server visualizes the analysis results in a dashboard, allowing users to easily check the results.

[0541] 5. If the server detects any suspicion of illegal logging, it sends the information to the terminal and notifies the user.

[0542] Specific examples

[0543] A user uses a drone to monitor a wide area of ​​forest. The drone captures abnormal logging activity in a certain area, and the device sends the data to a server in real time. The server analyzes the data, determines whether there is any suspicion of illegal logging, and displays the information on a dashboard. At the same time, an alert is sent to the user informing them of the possibility of illegal logging.

[0544] This process allows for efficient, real-time wildlife monitoring, environmental health checks, and illegal logging detection, allowing users to take immediate action.

[0545] The processing flow will be explained below.

[0546] Wildlife Monitoring System

[0547] Program processing

[0548] Step 1:

[0549] The device will set up a camera in the forest area, power it on, and check its operation. If there are no abnormalities, it will enter outdoor surveillance mode.

[0550] Step 2:

[0551] The camera detects motion and captures wildlife movements, triggering a capture and generating continuous image data.

[0552] Step 3:

[0553] The terminal compresses the captured image data, encrypts it using a data transfer protocol, and then transmits it to the server.

[0554] Step 4:

[0555] The server stores the image data received from the device, and simultaneously performs backup processing to ensure data integrity.

[0556] Step 5:

[0557] The server analyzes the received image data using AI algorithms. Image recognition technology is used to identify the type and movement of wildlife, for example, categorizing animals based on their shape, color, and size.

[0558] Step 6:

[0559] The server visualizes the analysis results on a dashboard, generating graphs and charts to help users intuitively understand the results.

[0560] Step 7:

[0561] If the server detects a new discovery or anomaly, it sends an alert to the device. The notification includes details of the analysis results and an image. The device then issues a notification to the user.

[0562] Environmental Health Check System

[0563] Program processing

[0564] Step 1:

[0565] The device will install environmental sensors in forests and water areas, and check that the sensors are working properly and can collect data properly.

[0566] Step 2:

[0567] Sensors collect environmental data (temperature, humidity, pH, etc.) at set intervals. As data is collected, it is saved in temporary storage.

[0568] Step 3:

[0569] The device periodically transmits data obtained from the sensors to the server, and the data is encrypted during transmission.

[0570] Step 4:

[0571] The server receives the data sent from the device and stores it in a database, also checking the integrity of the received data.

[0572] Step 5:

[0573] The server analyzes the received data in real time and uses AI algorithms to detect anomalies in the environmental data, for example, identifying abnormal fluctuations compared to established baseline values.

[0574] Step 6:

[0575] The server visualizes the analysis results on a dashboard, and if anomalies are detected, details are displayed in graphs and charts.

[0576] Step 7:

[0577] If the server detects an anomaly, it sends an alert to the device, which includes the specific anomaly value and location information. The device then issues a notification to the user.

[0578] Illegal logging detection system

[0579] Program processing

[0580] Step 1:

[0581] The terminal places the drone and ground sensors in the monitoring area, sets the drone's flight path, and checks the sensor's operation.

[0582] Step 2:

[0583] The drone flies according to a set schedule and captures image data of the monitored area, while ground sensors simultaneously collect data.

[0584] Step 3:

[0585] The device compresses the data obtained from the drone and sensors and transmits it to a server using a secure protocol.

[0586] Step 4:

[0587] The server receives the data sent from the device and stores it in a database. After receiving the data, it checks it to ensure its integrity.

[0588] Step 5:

[0589] The server analyzes the received data using AI algorithms. Image analysis and pattern recognition technology are used to detect possible illegal logging. For example, if a large amount of timber is being cut all at once, the system will detect any changes.

[0590] Step 6:

[0591] The server visualizes the analysis results on a dashboard, displaying them on graphs and maps to help users intuitively understand the results.

[0592] Step 7:

[0593] If the server detects any suspected illegal logging, it sends an alert to the device, which then issues a notification to the user, including specific location information and the results of image analysis.

[0594] Example 1

[0595] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0596] Current wildlife monitoring systems, environmental health check systems, and illegal logging detection systems often lack efficiency and accuracy in real-time data processing and anomaly detection. They also need to operate accurately at night and in bad weather. To address these issues, effective use of sensor technology and AI algorithms is essential.

[0597] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0598] In this invention, the server includes a means for analyzing received data using an AI algorithm to detect anomalies or new patterns, a dashboard means for visually displaying the analysis results, and a notification means for issuing a notification to the user when an anomaly or new pattern is detected, thereby enabling high-precision data analysis and anomaly detection in real time.

[0599] "Camera means" refers to a photographic device installed to capture the movements of wildlife, and is equipped with the ability to detect animal movements using infrared sensors even at night.

[0600] "Terminal means" is a device that compresses data obtained from sensors and cameras and transmits it to a server using a secure communication protocol.

[0601] "Server means" refers to a system that receives data sent from terminal means, stores it, and analyzes it using an AI algorithm.

[0602] The "analysis means" is a function that uses an AI algorithm to analyze data received by the server means, recognize the types and movements of wildlife, and detect environmental data and illegal logging.

[0603] The "dashboard means" is an interface that visually displays the analysis results so that the user can easily check them, and uses visualization tools to represent the results in graphs and charts.

[0604] "Notification methods" are functions that notify users when an abnormality or new discovery is detected, and provide information via email, SMS, push notifications, etc.

[0605] "Sensor means" refers to a device that collects environmental data such as temperature, humidity, pH, etc., and is used to obtain data with high accuracy.

[0606] A "drone vehicle" is an unmanned aerial vehicle that flies over a surveillance area, monitors the area using a high-resolution camera, and collects data.

[0607] A "generative AI model" is a learning model that uses AI algorithms to analyze specific data and perform pattern recognition and anomaly detection.

[0608] A "prompt sentence" is an input sentence that instructs a generative AI model to perform a specific analysis or recognition.

[0609] The present invention is an efficient and highly accurate system for wildlife monitoring, environmental health checks, and illegal logging detection. The detailed methods described below illustrate the preferred embodiments of the invention.

[0610] Wildlife Monitoring System

[0611] The device installs a high-resolution camera in a specific forest area. The camera has motion detection capabilities, and in particular, can detect animal movement even at night using an infrared sensor. When motion is detected, the camera captures image data. At the same time, the device compresses this image data using the JPEG compression algorithm and transmits it to a server in real time using a secure communication protocol (e.g., HTTPS).

[0612] The server stores the received image data in a database management system (e.g., MySQL), then applies an AI algorithm (e.g., an image recognition model using TensorFlow) to analyze the image data. This analysis identifies the type and movement of wildlife.

[0613] Finally, the analysis results are visually displayed through a dashboard. Visualization tools such as D3.js and Tableau are used for visualization, allowing users to easily check the analysis results. In addition, if abnormal behavior or new discoveries are detected, the server creates an alert and sends it to the terminal, which notifies the user.

[0614] Specific examples

[0615] A user sets up a camera in a forest area. The camera captures the movements of large animals (e.g., bears) at night, and the device sends the image data to a server in real time. The server analyzes the image, identifies the presence of a bear, and displays that information on a dashboard. At the same time, an alert is sent to the user that a bear has appeared.

[0616] Example prompts for generative AI models

[0617] Identify the type of wildlife captured by this camera.

[0618] Environmental Health Check System

[0619] The terminal places sensors in water bodies or forests. The sensors collect environmental data such as temperature, humidity, and pH. These sensors are typically built using Arduino or Raspberry Pi. The collected data is sent to a server in real time via the terminal. MQTT or HTTP is used as the communication protocol.

[0620] The server stores the received data in a database management system (e.g., PostgreSQL), and then applies AI algorithms to analyze the environmental data in real time to detect abnormalities, such as sudden pH fluctuations.

[0621] The analysis results are visually displayed on a dashboard, allowing users to easily check them. If an abnormality is detected, the server generates an alert and the device notifies the user.

[0622] Specific examples

[0623] The device detects a sudden rise in pH value using sensors installed in the water area and sends the data to the server in real time. The server analyzes the data, determines whether there is a possibility of water pollution, and displays the results on a dashboard. At the same time, the user is informed that there is a possibility of water pollution.

[0624] Example prompts for generative AI models

[0625] Detect unusual environmental fluctuations from this data.

[0626] Illegal logging detection system

[0627] The terminal places a drone and ground sensors in the monitoring area. The drone, equipped with a high-resolution camera, flies periodically to capture images of the monitoring area, while the ground sensors detect changes in the soil and vibrations from heavy machinery. Data obtained from the drone and sensors is sent to a server in real time via the terminal. A secure communication protocol (e.g., SSL / TLS) is used for transmission.

[0628] The server stores the received data in a database management system and uses AI algorithms (for example, analytical models using Keras) to analyze patterns of illegal logging. If the results are determined to be abnormal, an alert is generated.

[0629] The analysis results are displayed on a dashboard, where users can check them. If any abnormal monitoring activity is detected, the server sends an alert to the terminal, notifying the user.

[0630] Specific examples

[0631] A user uses a drone to monitor a wide area of ​​forest. The drone captures abnormal logging activity in a certain area, and the device sends the data to a server in real time. The server analyzes the data, determines whether there is any suspicion of illegal logging, and displays the information on a dashboard. At the same time, an alert is sent to the user informing them of the possibility of illegal logging.

[0632] Example prompts for generative AI models

[0633] Detect patterns of illegal logging in this image.

[0634] This process allows for efficient, real-time wildlife monitoring, environmental health checks, and illegal logging detection, allowing users to take immediate action.

[0635] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0636] Wildlife Monitoring System

[0637] Step 1: Camera installation and motion detection

[0638] The device installs a high-resolution camera in a forest area. Location information and installation angle are required as input. The device has a motion detection function and uses an infrared sensor to detect animal movements even at night. When movement is detected, the camera captures image data. The captured image data is obtained as output.

[0639] How it works: The camera uses a motion detection algorithm to detect movement and an infrared sensor to capture animals even at night.

[0640] Step 2: Compress and transmit image data

[0641] The device receives the captured image data as input and compresses it using the JPEG compression algorithm. As output, the compressed image data is obtained, which is then transmitted to the server in real time using a secure communication protocol (e.g., HTTPS).

[0642] Specific operation: The device applies a compression algorithm to reduce the data size and transmits it over a secure network.

[0643] Step 3: Saving and analyzing image data

[0644] The server receives the compressed image data as input and stores it using a database management system (e.g., MySQL). The stored image data is then analyzed using an AI algorithm (e.g., an image recognition model using TensorFlow). The analysis results (animal species and movements) are obtained as output.

[0645] Specific operation: The server stores the received data in a database and runs an image recognition model to perform analysis.

[0646] Step 4: Visualize the analysis results

[0647] The server receives the analysis results and displays them visually using a dashboard. The input required is the analysis results (animal species and movements). The output is visualized information in the form of graphs and charts.

[0648] Specific operation: The server uses a visualization tool (e.g., D3.js or Tableau) to generate an interface that displays the analysis results.

[0649] Step 5: Alert Notification

[0650] When the server detects a new discovery or abnormal behavior, it uses the alert generation system to generate an alert as input and sends it to the terminal. As output, it gets an alert notification, which the terminal sends to the user.

[0651] Specific operation: The server uses an anomaly detection algorithm to create an alert and notify the user via email or SMS.

[0652] Environmental Health Check System

[0653] Step 1: Sensor installation and data collection

[0654] The terminal installs sensors in water bodies or forests. The installation location and the type of sensor are required as inputs. The sensors collect environmental data such as temperature, humidity, pH, etc. The collected environmental data is obtained as output.

[0655] Specific operation: The sensor measures a specific environmental parameter and sends the data to the device.

[0656] Step 2: Sending data

[0657] The terminal receives collected environmental data as input and transmits it to the server in real time. The transmitted data is obtained as output. MQTT and HTTP are used as communication protocols.

[0658] Specific operation: The device formats the data and sends it using a secure communication protocol.

[0659] Step 3: Data storage and analysis

[0660] The server receives the received environmental data as input and stores it using a database management system (e.g., PostgreSQL). The stored data is then analyzed in real time using an AI algorithm. The analysis results (anomalous environmental data) are obtained as output.

[0661] Specific operation: The server stores the data in a database and performs analysis using an anomaly detection model.

[0662] Step 4: Visualize the analysis results

[0663] The server receives the analysis results and visually displays them using a dashboard. As input, the analysis results (anomalous environmental data) are required. As output, visualized information is obtained in the form of graphs and charts.

[0664] Specific operation: The server uses a visualization tool to generate an interface that displays the analysis results.

[0665] Step 5: Alert Notification

[0666] When the server detects an anomaly, it uses the alert generation system to generate an alert as input and sends it to the terminal. As output, an alert notification is obtained. The terminal then sends the notification to the user.

[0667] Specific operation: The server uses an anomaly detection algorithm to create an alert and notify the user via email or SMS.

[0668] Illegal logging detection system

[0669] Step 1: Drone and ground sensor placement

[0670] The terminal deploys drones and ground sensors in the monitoring area. The deployment location and drone flight plan are required as inputs. The drone monitors the area using a high-resolution camera, and the ground sensors detect changes in the soil and vibrations from heavy machinery. The output is the captured image data and sensor data.

[0671] How it works: The drone flies periodically, taking pictures of the area with its camera, and ground sensors collecting environmental data.

[0672] Step 2: Capturing and Sending Data

[0673] The terminal receives data from the drone and ground sensors as input and transmits it to the server in real time. The transmitted data is obtained as output. A secure communication protocol (e.g., SSL / TLS) is used for transmission.

[0674] What happens: The device formats the data and sends it over a secure network.

[0675] Step 3: Data storage and analysis

[0676] The server receives the received data as input and stores it using a database management system (e.g., MySQL). The stored data is then analyzed using an AI algorithm (e.g., an analytical model using Keras). The analysis results (patterns of illegal logging) are obtained as output.

[0677] Specific operation: The server stores the received data in a database and runs an analytical model to detect anomalies.

[0678] Step 4: Visualize the analysis results

[0679] The server receives the analysis results and displays them visually using a dashboard. The input required is the analysis results (patterns of illegal logging). The output is visualized information in the form of graphs and charts.

[0680] Specific operation: The server uses a visualization tool to generate an interface that displays the analysis results.

[0681] Step 5: Alert Notification

[0682] When the server detects suspected illegal logging, it uses the alert generation system to generate an alert as input and sends it to the terminal. The output is an alert notification, which the terminal then sends to the user.

[0683] Specific operation: The server uses an anomaly detection algorithm to create an alert and notify the user via email or SMS.

[0684] (Application example 1)

[0685] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0686] In modern society, effective wildlife monitoring, environmental health checks, and illegal logging detection are crucial. However, performing these monitoring and detection tasks efficiently and in real time poses technical challenges, particularly in the areas of wide-area monitoring and real-time data notification. Furthermore, existing systems are complex and require specialized knowledge, making them difficult for the general public to use. A system that can solve these problems and maintain a safe and healthy environment is needed.

[0687] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0688] In this invention, the server includes a photographing device, a transmitting device, a cloud server, an analysis device, a display device, a notification device, and a real-time notification device. This enables wide-area monitoring, real-time data analysis, and rapid notification to users. Furthermore, by using a generative AI model, a system is provided that can analyze environmental data and wildlife movements with high accuracy and quickly detect and notify abnormalities.

[0689] "Photography Device Means" refers to videography equipment for capturing wildlife and environmental activity.

[0690] The "transmission device means" is a device for compressing data obtained from the image capture device or sensor device and transmitting it to the cloud server.

[0691] "Cloud Server Means" refers to a remote computing resource for receiving, storing, and analyzing data transmitted from a Transmitter Means.

[0692] The "analysis means" is a means for analyzing data received by the cloud server means using a machine learning algorithm to recognize the type and movements of wildlife and detect illegal logging and environmental abnormalities.

[0693] The "display device means" is a device for visually displaying the results obtained by the analysis means.

[0694] "Notification device means" is a device for issuing a notification to the user in the event of an abnormality or new discovery.

[0695] The "real-time notification means" is a means for notifying the mobile terminal in real time of the data collected by the photographing device means and the transmitting device means.

[0696] A "sensor device means" is a device for collecting environmental data such as temperature, humidity, pH, etc.

[0697] "Unmanned Aerial Vehicle Means" means an unmanned aerial vehicle for periodically monitoring and collecting data from a surveillance area.

[0698] A "generative AI model" is an artificial intelligence model that is trained based on large datasets to perform specific tasks with high accuracy.

[0699] "Visualization means" refers to a means for visually displaying analysis results on the cloud.

[0700] This invention is a system for efficiently monitoring wildlife, checking environmental health, and detecting illegal logging. This system collects data using various imaging and sensor devices, transmits the data to a cloud server for analysis, and provides information to users in real time.

[0701] The server first receives image data obtained from the photographing device means and environmental data obtained from the sensor device means. Specific hardware examples include cameras for the photographing device means and temperature, humidity, and pH sensors for the sensor device means. These data are then transmitted to the cloud server via the transmitting device means. This transmission utilizes an internet connection.

[0702] The cloud server stores the received data and analyzes it using machine learning algorithms. Software such as TensorFlow and PyTorch is used for this analysis. The analysis results are visualized within the cloud server and provided to the user through the display device means. The results are displayed in a dashboard format so that the user can easily check them. In addition, if an abnormality or new discovery is detected, the notification device means issues an alert to the user through the real-time notification means.

[0703] As a practical example, a camera is installed in a forest area and captures the movement of wildlife (e.g., a bear) at night. The captured image data is sent to a cloud server in real time, where an AI algorithm analyzes the image and detects the presence of a bear. The results are displayed on a dashboard, and an alert is sent to the user that a "bear has appeared." This allows the user to respond quickly.

[0704] The system uses a generative AI model to analyze data with high accuracy and quickly detect anomalies. The generative AI model is trained using prompts such as:

[0705] "Please provide us with a dataset to identify animal species. Please provide us with an AI algorithm to develop a system that can monitor animal movements in real time using an environmentally friendly approach."

[0706] According to the embodiment of the present invention, users can efficiently and accurately monitor a wide area, check the environment, and detect illegal logging. This system will greatly contribute to sustainable environmental management and safety management.

[0707] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0708] Step 1:

[0709] A camera captures the movement. The user configures and positions the camera to cover a specific area. When the camera detects movement, such as wildlife or illegal logging, it captures the movement as image data.

[0710] (Input: Wildlife or target movement,raw data; Output: Captured image data)

[0711] Step 2:

[0712] The device compresses the captured image data and sends it to the cloud server. The device compresses the captured raw data for efficient transmission. The compressed data is then sent to the cloud server via Wi-Fi or mobile network.

[0713] (Input: captured image data; Output: compressed image data)

[0714] Step 3:

[0715] The cloud server stores the received image data and analyzes it using machine learning algorithms. The cloud server stores the data in a database and analyzes it using machine learning frameworks such as TensorFlow and PyTorch. The analysis is performed using a generative AI model to identify the type of animal and its movements from the data.

[0716] (Input: Compressed image data; Output: Recognition results of animal species and movements)

[0717] Step 4:

[0718] The server visualizes the analysis results on the cloud and provides them to the user through a display device. The analyzed data is provided to the user in the form of graphs and dashboards, which can be accessed from a web browser or a dedicated application.

[0719] (Input: Recognition results of animal species and movements; Output: Visualized analysis results on a dashboard)

[0720] Step 5:

[0721] If the server detects an anomaly or new discovery, it will issue a real-time notification to the user through a notification device. When the server detects an anomaly based on the analysis results, it will quickly send a notification to the user's smartphone or computer. The notification will be delivered as a text message or in-app notification.

[0722] (Input: Anomaly detection results; Output: Real-time notification)

[0723] Step 6:

[0724] Users receive notifications and access the system dashboard to view detailed data. Users can check alerts from their smartphones or computers, log in to the dashboard, and view footage and data analysis results.

[0725] (Input: Real-time notification; Output: User confirmation of detailed data)

[0726] These processing steps enable the system to efficiently monitor wildlife, check environmental health, and detect illegal logging in real time. The use of generative AI models enables highly accurate data analysis, prompting users to take immediate action.

[0727] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0728] The present invention provides a system that provides more advanced feedback by combining an emotion engine that recognizes user emotions with a system that efficiently monitors wildlife, checks environmental health, and detects illegal logging. Specific embodiments of the present invention are described below.

[0729] Wildlife Monitoring System

[0730] Program processing

[0731] 1. The device installs a camera in a forest area and performs an operation check. When the camera detects movement, it captures image data.

[0732] 2. The device compresses the captured image data and sends it to the server using an encryption protocol.

[0733] 3. The server stores the received data and analyzes it using AI algorithms, using image recognition technology to identify the type and movements of wildlife.

[0734] 4. The server visualizes the analysis results on a dashboard, displaying them in graphs and charts so that users can intuitively understand the results.

[0735] 5. If the server detects an abnormality or new discovery, it will send an alert to the device using a notification method.

[0736] 6. The device issues an alert to the user, and at the same time, the emotion engine analyzes the user's reaction, thereby monitoring the user's emotional state in real time.

[0737] 7. The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user reacts with surprise or joy, that information is collected.

[0738] 8. The server adjusts the content and format of notifications based on the emotional data, personalizing feedback based on the user’s emotional state.

[0739] Specific examples

[0740] A user sets up a camera in a forested area. The camera captures the movements of large animals (e.g., bears) at night, and the device sends the data in real time to a server. The server analyzes the images, identifies the presence of a bear, and displays that information on a dashboard. The server then sends an alert to the user that a "bear has appeared." The device analyzes the user's reaction using an emotion engine, and if the user is surprised, the emotion data is sent to the server, and the next notification will be more gentle.

[0741] Environmental Health Check System

[0742] Program processing

[0743] 1. The device installs sensors in forests and water areas and checks their operation. The sensors collect environmental data (temperature, humidity, pH, etc.).

[0744] 2. The device sends the collected data to the server in real time.

[0745] 3. The server receives and stores the data, then uses AI algorithms to analyze it and detect outliers.

[0746] 4. The server displays the analysis results on a dashboard and issues an alert if an abnormal value is detected.

[0747] 5. If the server detects an abnormality, it sends that information to the terminal and notifies the user.

[0748] 6. At the same time that the device issues an alert, the emotion engine analyzes the user's reaction, monitoring the user's emotional state in real time.

[0749] 7. The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user shows a nervous reaction, that information is collected.

[0750] 8. The server adjusts the content and format of notifications based on the emotional data, personalizing feedback based on the user’s emotional state.

[0751] Specific examples

[0752] The device installs a sensor in the water body and detects a sudden rise in pH level, sending the data to the server in real time. The server analyzes the data and determines that there is a possibility of water pollution. The server then sends an alert to the user stating that there is a possibility of water pollution. The device then analyzes the user's reaction using an emotion engine, and if the user becomes nervous, the emotion data is sent to the server, and the next notification will be more reassuring.

[0753] Illegal logging detection system

[0754] Program processing

[0755] 1. The device places drones and ground sensors in the surveillance area and sets the drone's flight path.

[0756] 2. Drones fly over the monitored area periodically and capture image data, and ground sensors collect data as well.

[0757] 3. The device compresses the captured data and sends it to the server using an encryption protocol.

[0758] 4. The server stores the received data and analyzes it using AI algorithms to detect patterns of illegal logging.

[0759] 5. The server visualizes the analysis results on a dashboard, allowing users to easily and intuitively understand the results.

[0760] 6. If the server detects suspicion of illegal logging, it will send an alert to the terminal using a notification method.

[0761] 7. The device issues an alert to the user, and at the same time, the emotion engine analyzes the user's reaction, monitoring the user's emotional state in real time.

[0762] 8. The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user reacts impatient or angry, that information is collected.

[0763] 9. The server adjusts the content and format of notifications based on the emotional data, personalizing feedback based on the user’s emotional state.

[0764] Specific examples

[0765] A user uses a drone to monitor a wide area of ​​forest. The drone captures abnormal logging activity in a certain area, and the device sends the data in real time to a server. The server analyzes the data and determines that illegal logging is suspected. The server then sends an alert to the user stating that "illegal logging is suspected." The device uses an emotion engine to analyze the user's reaction, and if the user becomes panicked, the emotional data is sent to the server, and the next notification will be delivered with content that allows the user to respond more calmly.

[0766] This process allows for efficient, real-time wildlife monitoring, environmental health checks, and illegal logging detection, while also providing personalized feedback based on the user's emotional state.

[0767] The processing flow will be explained below.

[0768] Combining emotion engines with wildlife monitoring systems

[0769] Program processing

[0770] Step 1:

[0771] The device installs a camera in the forest area. It turns on the camera and checks its operation. After confirming that it is working properly, it starts monitoring mode.

[0772] Step 2:

[0773] The camera detects motion and captures wildlife movements, triggering the generation of continuous image data.

[0774] Step 3:

[0775] The device compresses the captured image data and transmits it to a server in real time using an encryption protocol.

[0776] Step 4:

[0777] The server stores the received data and also performs backup processing to ensure data safety.

[0778] Step 5:

[0779] The server analyzes the stored image data with AI algorithms and uses image recognition technology to identify the type and movement of wildlife, for example by classifying animals based on their shape, color, and size.

[0780] Step 6:

[0781] The server visualizes the analysis results on a dashboard, generating and displaying graphs and charts that are easy for users to understand intuitively.

[0782] Step 7:

[0783] If the server detects any new findings or anomalies, it will use a notification mechanism to send an alert to the device, including details of the analysis results and an image.

[0784] Step 8:

[0785] The device issues an alert to the user. At the same time, the emotion engine analyzes the user's reaction and monitors the user's emotional state in real time after seeing the alert.

[0786] Step 9:

[0787] The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user shows a reaction of surprise or joy, that information is collected.

[0788] Step 10:

[0789] The server adjusts the content and format of notifications based on the emotional data, so the next notification will be personalized based on the user's emotional state.

[0790] Combining an environmental health check system with an emotion engine

[0791] Program processing

[0792] Step 1:

[0793] The device will install environmental sensors in forests and water areas, and check the operation of the sensors to ensure they are working properly.

[0794] Step 2:

[0795] The sensor collects environmental data (temperature, humidity, pH, etc.) at set intervals and stores the data in temporary storage.

[0796] Step 3:

[0797] The data collected by the terminal is sent to the server at regular intervals using an encryption protocol.

[0798] Step 4:

[0799] The server stores the received data, checks its integrity, and simultaneously backs up the data.

[0800] Step 5:

[0801] The server analyzes the received data in real time and uses AI algorithms to detect abnormalities in the environmental data (for example, sudden changes in pH).

[0802] Step 6:

[0803] The server visualizes the analysis results on a dashboard, and if anomalies are detected, details are displayed in graphs and charts.

[0804] Step 7:

[0805] If the server detects an abnormality, it will use a notification method to send an alert to the device, which will include the specific abnormal value and location information.

[0806] Step 8:

[0807] The device issues an alert to the user. At the same time, the emotion engine analyzes the user's reaction and monitors the user's emotional state in real time after seeing the alert.

[0808] Step 9:

[0809] The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user shows a nervous reaction, that information is collected.

[0810] Step 10:

[0811] The server adjusts the content and format of notifications based on the emotional data, so the next notification will be personalized based on the user's emotional state.

[0812] Combining an emotion engine with an illegal logging detection system

[0813] Program processing

[0814] Step 1:

[0815] The terminal places the drone and ground sensors in the monitoring area, sets the drone's flight path, and performs operational checks to confirm normal operation.

[0816] Step 2:

[0817] Drones fly according to a set schedule and capture image data of the monitored area, and ground sensors collect data as well.

[0818] Step 3:

[0819] The device compresses the captured data and sends it to the server using an encryption protocol.

[0820] Step 4:

[0821] The server saves the received data, checks the integrity of the data, and simultaneously backs up the received data.

[0822] Step 5:

[0823] The server analyzes the received data using AI algorithms to detect patterns of illegal logging. For example, if a large amount of timber is being cut all at once, it will detect this change.

[0824] Step 6:

[0825] The server visualizes the analysis results on a dashboard, displaying them in graphs and maps that are easy for users to understand intuitively.

[0826] Step 7:

[0827] If the server detects any suspicion of illegal logging, it will use a notification method to send an alert to the device, which will include specific location information and the results of image analysis.

[0828] Step 8:

[0829] The device issues an alert to the user. At the same time, the emotion engine analyzes the user's reaction and monitors the user's emotional state in real time after seeing the alert.

[0830] Step 9:

[0831] The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user shows impatience or anger, that information is collected.

[0832] Step 10:

[0833] The server adjusts the content and format of notifications based on the emotional data, so the next notification will be personalized based on the user's emotional state.

[0834] This process allows for efficient, real-time wildlife monitoring, environmental health checks, and illegal logging detection, while also providing personalized feedback based on the user's emotional state.

[0835] Example 2

[0836] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0837] While existing systems for wildlife monitoring, environmental health checks, and illegal logging detection are efficient at collecting and analyzing data, they lack the ability to personalize feedback based on the user's emotional state. As a result, the amount and quality of information users receive is constant, and they lack the flexibility to adapt to specific situations. Furthermore, when detecting anomalies or new discoveries, they lack the interactive support to help users respond calmly.

[0838] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a photographing means, a device means, a server means, an analysis means, a display means, a notification means, an emotion engine and a means for transmitting the data to the server, and a feedback means. This makes it possible to efficiently collect data, analyze it in real time, and adjust the content and format of notifications based on the user's emotions. By analyzing the user's reactions with the emotion engine and providing personalized feedback according to each situation, a system is realized that can provide the optimal response for each usage scenario.

[0839] "Capture means" refers to a device that captures wildlife and environmental movements and obtains image data.

[0840] "Device means" refers to a device for compressing data obtained from the imaging means or sensor means and transmitting it to a server using an encryption protocol.

[0841] "Server means" refers to a server that receives data transmitted from device means, decrypts and stores it, and further analyzes it.

[0842] "Analysis means" refers to the function of analyzing data received by the server means using an AI algorithm to recognize or detect the type and movement of wildlife, abnormalities in environmental data, and illegal logging.

[0843] "Display means" refers to an interface that visually displays the analysis results and shows them in graphs or charts so that the user can intuitively understand them.

[0844] "Notification means" refers to the function of sending an alert to the user via the terminal in the event of an abnormality or new discovery.

[0845] An "emotion engine" refers to an analysis device that analyzes a user's reaction and sends the results to a server.

[0846] "Feedback means" refers to a function that adjusts the content and format of notifications based on the user's emotional data, thereby personalizing feedback.

[0847] The present invention provides a system for efficiently monitoring wildlife, checking environmental health, and detecting illegal logging by combining it with an emotion engine that recognizes user emotions to provide more advanced feedback. Specific embodiments of the present invention are described below.

[0848] Basic system configuration

[0849] This system is mainly composed of the following means:

[0850] 1. Filming Method

[0851] The photographing means includes a camera device for capturing wildlife movements, such as a highly sensitive infrared camera or a camera with motion detection capabilities. The camera is installed in a designated forest area and captures image data when it detects movement.

[0852] 2. Equipment means

[0853] The device means compresses the image data obtained from the photographing means and the environmental data obtained from the sensor means, protects the data using the AES encryption protocol, and transmits it to the server. Data compression reduces the communication load and enables efficient data transmission.

[0854] 3. Server Means

[0855] The server means receives, decrypts, and stores data sent from the device means. The server is equipped with high-performance storage capable of processing large amounts of data and an AI algorithm (e.g., YOLO or ResNet) for data analysis.

[0856] 4. Analysis method

[0857] The analysis means uses AI algorithms to analyze data received and stored in the server means, and has the ability to recognize the types and movements of wildlife. Environmental data and illegal logging detection are also analyzed in the same way.

[0858] 5. Display means

[0859] The display means is a dashboard interface that visually represents the results of the analysis performed by the server. The dashboard visualizes the results in graphs and charts, allowing users to intuitively understand them.

[0860] 6. Means of notification

[0861] The notification function has the function of sending alerts to users if an abnormality or new discovery is detected based on the analysis results. Notifications are delivered to users via mobile app, email, or SMS.

[0862] 7. Emotion Engine

[0863] The emotion engine has the function of analyzing the user's reactions and sending the data to the server. For example, it analyzes the user's reactions such as surprise, joy, and tension, and collects the emotional data.

[0864] 8. Feedback channels

[0865] The feedback mechanism has a function that allows the server to adjust the content and format of the next notification based on data from the emotion engine, thereby personalizing the feedback, thereby providing the most appropriate information for the user.

[0866] Specific examples

[0867] For example, if a user installs a camera in a forest area and the camera captures the movement of a large animal (e.g., a bear) at night, the image data is sent in real time to a server by a device means. The server analyzes the data, identifies the presence of a bear, and displays the information on a dashboard. Based on the analysis results, the server sends an alert to the user through a notification means that a "bear has appeared." If the device analyzes the user's reaction with an emotion engine, and the user is surprised, the emotion data is sent to the server, and the next notification will be provided in a gentler tone.

[0868] Prompt Sentence Examples

[0869] "Create a program that describes a system in which a user-installed camera detects movement in a forested area and sends the image data to a server. The server then analyzes the data to identify the type of wildlife and notify the user."

[0870] As can be seen, the present invention can enhance wildlife monitoring, environmental health checks, illegal logging detection, and provide personalized feedback based on the user's emotional state.

[0871] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0872] Step 1:

[0873] The user installs the imaging device (camera) in a forest area and checks its operation. The user fixes the camera in an area specified by the user and checks that the motion detection function is working properly. The input for the imaging device includes the setting parameters for detecting motion and camera position information. The output is image data generated when motion is detected.

[0874] Step 2:

[0875] Image data is captured when the imaging means detects motion. For example, a camera may trigger a shutter in response to wildlife movement. The input is the detection result of a motion detection sensor, and the output is the captured image data.

[0876] Step 3:

[0877] The device compresses the image data obtained from the image capture means and sends it to the server using an encryption protocol. Specifically, the image data is compressed into a format such as JPEG and encrypted using an encryption method such as AES256. The input is the captured image data, and the output is compressed and encrypted data.

[0878] Step 4:

[0879] The server receives the data sent from the terminal, decrypts it, and stores it. Specifically, the server receives the encrypted data and decrypts it using an AES256 key. It then stores it in a database. The input is encrypted image data, and the output is the original decrypted image data, which is then stored in the database.

[0880] Step 5:

[0881] The server then analyzes the decoded data using AI algorithms. For example, image recognition techniques such as YOLO or ResNet are used to identify the wildlife species and their movements. The input is the decoded image data, and the output is information about the wildlife species and their movements.

[0882] Step 6:

[0883] The server displays the analysis results on a dashboard. It converts the data into graphs and charts and visualizes them so that users can easily understand them. The input is the analysis result data, and the output is visualized graphs and charts displayed on the dashboard screen.

[0884] Step 7:

[0885] If the server detects an abnormality or a new discovery, it will send an alert to the device using a notification method. Specifically, it will send an alert message using email, SMS, etc. The input is the analysis result, and the output is an alert message that is generated and sent to the device.

[0886] Step 8:

[0887] The device issues an alert to the user and analyzes the user's reaction using an emotion engine. For example, the alert can be displayed through a mobile app or a dedicated device, and the emotion engine can analyze the user's facial expression and tone of voice. The input is an alert message from the server, and the output is the user's emotional data.

[0888] Step 9:

[0889] The emotion engine analyzes the user's emotion data and sends it to the server. Specifically, the user's facial expressions and tone of voice are sent to the server as numerical data. The input is the user's emotion data, and the output is the emotion data sent to the server.

[0890] Step 10:

[0891] The server adjusts the content and format of notifications based on the emotion data. For example, if the user is surprised, the next notification will be sent in a softer tone. The input is the emotion data sent by the emotion engine, and the output is the adjusted content and format of the notification.

[0892] Through these steps, the wildlife monitoring system can efficiently collect and analyze data and provide personalized feedback based on the user's emotional state.

[0893] (Application example 2)

[0894] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0895] While existing wildlife monitoring systems, environmental health check systems, and illegal logging detection systems are capable of advanced monitoring and data analysis, they lack the functionality to provide feedback based on the user's emotions, resulting in a limited user experience. In particular, if alert notifications cause excessive tension or anxiety in the user, this can hinder subsequent response.

[0896] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes emotion engine means for analyzing the user's emotion data, feedback means for transmitting the emotion data analyzed by the emotion engine means to the server and for personalizing the notification content, and notification means for issuing a notification to the user when an abnormality or new discovery is detected. This enables the alert notification to be personalized according to the user's emotional state, helping the user to respond appropriately and quickly.

[0897] "Wildlife" means any organism other than humans that inhabits the natural environment, especially animals.

[0898] "Camera means" refers to a device used to photograph wildlife and the environment and obtain image data thereof.

[0899] "Terminal means" refers to a device that compresses and transmits data obtained from camera means or sensor means.

[0900] "Server means" refers to a device or group of devices that receives and stores data sent from terminal means and performs the necessary processing.

[0901] "Analysis means" refers to a device or group of devices that analyzes data received by the server means using an AI algorithm and extracts and recognizes specific information.

[0902] The "dashboard means" is a device or interface for visually displaying the analysis results obtained by the analysis means.

[0903] "Notification means" refers to a device or function that issues an appropriate notification to the user in the event of an abnormality or new discovery.

[0904] The "emotion engine means" is a device or algorithm that analyzes the user's emotional data and personalizes the notification content based on that data.

[0905] The "feedback means" is a device or function for providing the user with optimal notification content using emotion data analyzed by the emotion engine means.

[0906] "Sensor means" refers to a device for collecting environmental data such as temperature, humidity, pH value, etc.

[0907] A "drone vehicle" is a flying device that periodically flies over a surveillance area from the air and captures image data.

[0908] This invention relates to an advanced environmental monitoring system for analyzing user emotions and optimizing the feedback. Specifically, by integrating an emotion engine into systems for wildlife monitoring, environmental health checks, and illegal logging detection, the system provides feedback according to the user's emotions.

[0909] 1. Wildlife Monitoring System

[0910] Hardware Configuration

[0911] In this system, multiple cameras are used to detect wildlife movement. When a camera detects movement, it captures image data and transmits it to a terminal device. The terminal device compresses and encrypts the data and transmits it to a server device.

[0912] Software Configuration

[0913] The server stores the received data and analyzes it using AI algorithms. The AI ​​uses image recognition technology to identify wildlife species and movements. The results are displayed visually on a dashboard in an intuitive format for users.

[0914] If an abnormality or new discovery is detected, the notification means issues a notification to the user. At the same time, the emotion engine analyzes the user's reaction and sends the emotional data to the server. Based on this data, the server personalizes the notification content and adjusts the next feedback.

[0915] Specific examples

[0916] A user sets up a camera in a forested area. The camera captures bear movements at night and sends the image data to a server. The server identifies the bear's presence and displays it on a dashboard. When an alert is sent to the user that a bear has appeared, the emotion engine analyzes the user's reaction to surprise. As a result, the next notification will be presented in a more toned-down format.

[0917] 2. Environmental Health Check System

[0918] Hardware Configuration

[0919] In this system, multiple sensors are used to collect environmental data such as temperature, humidity, pH, etc. The sensor means transmits the collected data in real time to the terminal means, and the terminal means transmits the data to the server means.

[0920] Software Configuration

[0921] The server means stores the received data and analyzes it using an AI algorithm. If an abnormality is detected, the notification means issues a notification to the user. The emotion engine analyzes the user's reaction and sends the emotion data to the server. The server uses this data to personalize the notification content.

[0922] Specific examples

[0923] A sensor installed by the user in a body of water detects a sudden rise in pH and sends the data to a server in real time. The server analyzes the data and determines that there is a possibility of water pollution. An alert is sent to the user stating "Possible water pollution" and an emotion engine analyzes the user's tense reaction. As a result, the next notification is presented in a reassuring format.

[0924] 3. Illegal Logging Detection System

[0925] Hardware Configuration

[0926] In this system, a drone is used to monitor a monitoring area from the air. Sensor means is also used to collect data on the ground. Data obtained from the drone and sensor means is transmitted to server means via terminal means.

[0927] Software Configuration

[0928] The server means stores the received data and uses an AI algorithm to detect illegal logging. If an abnormality is detected, the notification means issues a notification to the user. The emotion engine analyzes the user's reaction and sends the emotional data to the server. The server uses this data to personalize the notification content.

[0929] Specific examples

[0930] A user uses a drone to monitor a wide area of ​​forest. The drone captures abnormal logging activity in a certain area and sends the data to a server in real time. The server analyzes the data and detects possible illegal logging. An alert is sent to the user stating "Possible illegal logging" and an emotion engine analyzes the user's panicked reaction. As a result, the next notification is presented in a format that encourages a calm response.

[0931] Prompt Sentence Examples

[0932] "How can I combine sentiment analysis with notifications from my home security system to provide personalized feedback based on the user's response?"

[0933] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0934] Program processing flow

[0935] Step 1: The camera detects motion and captures an image

[0936] Input: The camera detects motion within the monitored area.

[0937] Data processing: Capture images using movement as a trigger.

[0938] Output: The captured image data is sent to the terminal means.

[0939] Specific operation: When the camera means detects movement, it automatically releases the shutter and saves the image at that time in memory.

[0940] Step 2: The device compresses the image data and sends it to the server

[0941] Input: Image data captured from a camera means.

[0942] Data processing: Compression and encryption of image data.

[0943] Output: Send the compressed image data to the server means.

[0944] Specific operation: The terminal means compresses the image data from the camera to reduce its size, and transmits the data to the server means using an encryption protocol.

[0945] Step 3: The server stores the received data and analyzes it with AI

[0946] Input: Compressed and encrypted image data sent from the terminal means.

[0947] Data processing: decryption and data storage.

[0948] Output: Extract the analysis results and send them to a dashboard.

[0949] What it does: The server decrypts the compressed data and stores it in a database. It uses AI algorithms to perform image recognition and identify wildlife species and movements.

[0950] Step 4: Display the analysis results on the dashboard

[0951] Input: Recognition results from AI analysis.

[0952] Data processing: Converting data into a visual format.

[0953] Output: Displayed on the dashboard.

[0954] Specific operation: The server means converts the analysis results into graphs or charts and displays them on the user interface.

[0955] Step 5: Issue a user notification when an error occurs

[0956] Input: Data whose analysis results indicate an anomaly.

[0957] Data processing: Generation of notification content.

[0958] Output: Sends a notification to the user.

[0959] Specific operation: If the notification means detects an abnormality, it will issue an alert to the user based on pre-defined rules.

[0960] Step 6: Analyze user reactions with the emotion engine

[0961] Input: Real-time user responses (e.g., data from a smartphone camera or sensors).

[0962] Data processing: Data analysis using sentiment analysis algorithms.

[0963] Output: Generate emotion data and send it to the server.

[0964] Specific operation: The emotion engine means performs facial and voice recognition on the user to identify emotions such as surprise or anxiety.

[0965] Step 7: The server analyzes the emotion data and adjusts the notification content.

[0966] Input: Emotion data sent from the emotion engine means.

[0967] Data processing: Feedback adjustment based on emotion data.

[0968] Output: Adjustment result for next notification content.

[0969] Specific operation: The server analyzes the emotional data and adjusts the next notification content, such as softening it, depending on the user's emotional state.

[0970] Step 8: Sending feedback to the server

[0971] Input: Reconciliation result by server means.

[0972] Data processing: generating the final feedback content.

[0973] Output: The revised notification is sent to the user.

[0974] Specific operation: The server means generates tailored notification content and sends feedback that gives the user peace of mind.

[0975] 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.

[0976] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0977] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0978] [Third embodiment]

[0979] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0980] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0981] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0982] 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.

[0983] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0984] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0985] 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.

[0986] 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.

[0987] 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 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.

[0988] 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.

[0989] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0990] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0991] The present invention provides a system for efficiently monitoring wildlife, checking environmental health, and detecting illegal logging. Each system is implemented as follows.

[0992] Wildlife Monitoring System

[0993] Program processing

[0994] 1. The device places cameras in specific forest areas, and when the cameras detect wildlife movement, they capture image data.

[0995] 2. The device compresses the captured image data and sends it to the server in real time.

[0996] 3. The server stores the received image data and analyzes it using AI algorithms, specifically using image recognition technology to identify the type and movement of wildlife.

[0997] 4. The server visualizes the analysis results in a dashboard, allowing users to easily check the results.

[0998] 5. If the server detects any new findings or abnormal behavior, it sends an alert to the device and notifies the user.

[0999] Specific examples

[1000] A user sets up a camera in a forest area. The camera captures the movements of large animals (e.g., bears) at night, and the device sends the image data to a server in real time. The server analyzes the image, identifies the presence of a bear, and displays that information on a dashboard. At the same time, an alert is sent to the user that a bear has appeared.

[1001] Environmental Health Check System

[1002] Program processing

[1003] 1. The device places sensors in forests, bodies of water, etc. The sensors collect environmental data such as temperature, humidity, and pH.

[1004] 2. The device sends the collected data to the server in real time.

[1005] 3. The server stores the received data and analyzes it using AI algorithms to detect abnormal values ​​(e.g., sudden fluctuations in pH).

[1006] 4. The server visualizes the analysis results in a dashboard, allowing users to easily check the results.

[1007] 5. If the server detects an abnormality, it sends that information to the terminal and notifies the user.

[1008] Specific examples

[1009] The device detects a sudden rise in pH value using sensors installed in the water area and sends the data to the server in real time. The server analyzes the data, determines whether there is a possibility of water pollution, and displays the results on a dashboard. At the same time, an alert is sent to the user, informing them that there is a possibility of water pollution.

[1010] Illegal logging detection system

[1011] Program processing

[1012] 1. The terminal deploys a drone and ground sensors in the monitoring area. The drone periodically flies over the monitoring area and captures image data.

[1013] 2. The device sends the captured data to the server. Ground sensors also collect and send data.

[1014] 3. The server stores the received data and analyzes it using AI algorithms to detect patterns of illegal logging.

[1015] 4. The server visualizes the analysis results in a dashboard, allowing users to easily check the results.

[1016] 5. If the server detects any suspicion of illegal logging, it sends the information to the terminal and notifies the user.

[1017] Specific examples

[1018] A user uses a drone to monitor a wide area of ​​forest. The drone captures abnormal logging activity in a certain area, and the device sends the data to a server in real time. The server analyzes the data, determines whether there is any suspicion of illegal logging, and displays the information on a dashboard. At the same time, an alert is sent to the user informing them of the possibility of illegal logging.

[1019] This process allows for efficient, real-time wildlife monitoring, environmental health checks, and illegal logging detection, allowing users to take immediate action.

[1020] The processing flow will be explained below.

[1021] Wildlife Monitoring System

[1022] Program processing

[1023] Step 1:

[1024] The device will set up a camera in the forest area, power it on, and check its operation. If there are no abnormalities, it will enter outdoor surveillance mode.

[1025] Step 2:

[1026] The camera detects motion and captures wildlife movements, triggering a capture and generating continuous image data.

[1027] Step 3:

[1028] The terminal compresses the captured image data, encrypts it using a data transfer protocol, and then transmits it to the server.

[1029] Step 4:

[1030] The server stores the image data received from the device, and simultaneously performs backup processing to ensure data integrity.

[1031] Step 5:

[1032] The server analyzes the received image data using AI algorithms. Image recognition technology is used to identify the type and movement of wildlife, for example, categorizing animals based on their shape, color, and size.

[1033] Step 6:

[1034] The server visualizes the analysis results on a dashboard, generating graphs and charts to help users intuitively understand the results.

[1035] Step 7:

[1036] If the server detects a new discovery or anomaly, it sends an alert to the device. The notification includes details of the analysis results and an image. The device then issues a notification to the user.

[1037] Environmental Health Check System

[1038] Program processing

[1039] Step 1:

[1040] The device will install environmental sensors in forests and water areas, and check that the sensors are working properly and can collect data properly.

[1041] Step 2:

[1042] Sensors collect environmental data (temperature, humidity, pH, etc.) at set intervals. As data is collected, it is saved in temporary storage.

[1043] Step 3:

[1044] The device periodically transmits data obtained from the sensors to the server, and the data is encrypted during transmission.

[1045] Step 4:

[1046] The server receives the data sent from the device and stores it in a database, also checking the integrity of the received data.

[1047] Step 5:

[1048] The server analyzes the received data in real time and uses AI algorithms to detect anomalies in the environmental data, for example, identifying abnormal fluctuations compared to established baseline values.

[1049] Step 6:

[1050] The server visualizes the analysis results on a dashboard, and if anomalies are detected, details are displayed in graphs and charts.

[1051] Step 7:

[1052] If the server detects an anomaly, it sends an alert to the device, which includes the specific anomaly value and location information. The device then issues a notification to the user.

[1053] Illegal logging detection system

[1054] Program processing

[1055] Step 1:

[1056] The terminal places the drone and ground sensors in the monitoring area, sets the drone's flight path, and checks the sensor's operation.

[1057] Step 2:

[1058] The drone flies according to a set schedule and captures image data of the monitored area, while ground sensors simultaneously collect data.

[1059] Step 3:

[1060] The device compresses the data obtained from the drone and sensors and transmits it to a server using a secure protocol.

[1061] Step 4:

[1062] The server receives the data sent from the device and stores it in a database. After receiving the data, it checks it to ensure its integrity.

[1063] Step 5:

[1064] The server analyzes the received data using AI algorithms. Image analysis and pattern recognition technology are used to detect possible illegal logging. For example, if a large amount of timber is being cut all at once, the system will detect any changes.

[1065] Step 6:

[1066] The server visualizes the analysis results on a dashboard, displaying them on graphs and maps to help users intuitively understand the results.

[1067] Step 7:

[1068] If the server detects any suspected illegal logging, it sends an alert to the device, which then issues a notification to the user, including specific location information and the results of image analysis.

[1069] Example 1

[1070] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1071] Current wildlife monitoring systems, environmental health check systems, and illegal logging detection systems often lack efficiency and accuracy in real-time data processing and anomaly detection. They also need to operate accurately at night and in bad weather. To address these issues, effective use of sensor technology and AI algorithms is essential.

[1072] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1073] In this invention, the server includes a means for analyzing received data using an AI algorithm to detect anomalies or new patterns, a dashboard means for visually displaying the analysis results, and a notification means for issuing a notification to the user when an anomaly or new pattern is detected, thereby enabling high-precision data analysis and anomaly detection in real time.

[1074] "Camera means" refers to a photographic device installed to capture the movements of wildlife, and is equipped with the ability to detect animal movements using infrared sensors even at night.

[1075] "Terminal means" is a device that compresses data obtained from sensors and cameras and transmits it to a server using a secure communication protocol.

[1076] "Server means" refers to a system that receives data sent from terminal means, stores it, and analyzes it using an AI algorithm.

[1077] The "analysis means" is a function that uses an AI algorithm to analyze data received by the server means, recognize the types and movements of wildlife, and detect environmental data and illegal logging.

[1078] The "dashboard means" is an interface that visually displays the analysis results so that the user can easily check them, and uses visualization tools to represent the results in graphs and charts.

[1079] "Notification methods" are functions that notify users when an abnormality or new discovery is detected, and provide information via email, SMS, push notifications, etc.

[1080] "Sensor means" refers to a device that collects environmental data such as temperature, humidity, pH, etc., and is used to obtain data with high accuracy.

[1081] A "drone vehicle" is an unmanned aerial vehicle that flies over a surveillance area, monitors the area using a high-resolution camera, and collects data.

[1082] A "generative AI model" is a learning model that uses AI algorithms to analyze specific data and perform pattern recognition and anomaly detection.

[1083] A "prompt sentence" is an input sentence that instructs a generative AI model to perform a specific analysis or recognition.

[1084] The present invention is an efficient and highly accurate system for wildlife monitoring, environmental health checks, and illegal logging detection. The detailed methods described below illustrate the preferred embodiments of the invention.

[1085] Wildlife Monitoring System

[1086] The device installs a high-resolution camera in a specific forest area. The camera has motion detection capabilities, and in particular, can detect animal movement even at night using an infrared sensor. When motion is detected, the camera captures image data. At the same time, the device compresses this image data using the JPEG compression algorithm and transmits it to a server in real time using a secure communication protocol (e.g., HTTPS).

[1087] The server stores the received image data in a database management system (e.g., MySQL), then applies an AI algorithm (e.g., an image recognition model using TensorFlow) to analyze the image data. This analysis identifies the type and movement of wildlife.

[1088] Finally, the analysis results are visually displayed through a dashboard. Visualization tools such as D3.js and Tableau are used for visualization, allowing users to easily check the analysis results. In addition, if abnormal behavior or new discoveries are detected, the server creates an alert and sends it to the terminal, which notifies the user.

[1089] Specific examples

[1090] A user sets up a camera in a forest area. The camera captures the movements of large animals (e.g., bears) at night, and the device sends the image data to a server in real time. The server analyzes the image, identifies the presence of a bear, and displays that information on a dashboard. At the same time, an alert is sent to the user that a bear has appeared.

[1091] Example prompts for generative AI models

[1092] Identify the type of wildlife captured by this camera.

[1093] Environmental Health Check System

[1094] The terminal places sensors in water bodies or forests. The sensors collect environmental data such as temperature, humidity, and pH. These sensors are typically built using Arduino or Raspberry Pi. The collected data is sent to a server in real time via the terminal. MQTT or HTTP is used as the communication protocol.

[1095] The server stores the received data in a database management system (e.g., PostgreSQL), and then applies AI algorithms to analyze the environmental data in real time to detect abnormalities, such as sudden pH fluctuations.

[1096] The analysis results are visually displayed on a dashboard, allowing users to easily check them. If an abnormality is detected, the server generates an alert and the device notifies the user.

[1097] Specific examples

[1098] The device detects a sudden rise in pH value using sensors installed in the water area and sends the data to the server in real time. The server analyzes the data, determines whether there is a possibility of water pollution, and displays the results on a dashboard. At the same time, the user is informed that there is a possibility of water pollution.

[1099] Example prompts for generative AI models

[1100] Detect unusual environmental fluctuations from this data.

[1101] Illegal logging detection system

[1102] The terminal places a drone and ground sensors in the monitoring area. The drone, equipped with a high-resolution camera, flies periodically to capture images of the monitoring area, while the ground sensors detect changes in the soil and vibrations from heavy machinery. Data obtained from the drone and sensors is sent to a server in real time via the terminal. A secure communication protocol (e.g., SSL / TLS) is used for transmission.

[1103] The server stores the received data in a database management system and uses AI algorithms (for example, analytical models using Keras) to analyze patterns of illegal logging. If the results are determined to be abnormal, an alert is generated.

[1104] The analysis results are displayed on a dashboard, where users can check them. If any abnormal monitoring activity is detected, the server sends an alert to the terminal, notifying the user.

[1105] Specific examples

[1106] A user uses a drone to monitor a wide area of ​​forest. The drone captures abnormal logging activity in a certain area, and the device sends the data to a server in real time. The server analyzes the data, determines whether there is any suspicion of illegal logging, and displays the information on a dashboard. At the same time, an alert is sent to the user informing them of the possibility of illegal logging.

[1107] Example prompts for generative AI models

[1108] Detect patterns of illegal logging in this image.

[1109] This process allows for efficient, real-time wildlife monitoring, environmental health checks, and illegal logging detection, allowing users to take immediate action.

[1110] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1111] Wildlife Monitoring System

[1112] Step 1: Camera installation and motion detection

[1113] The device installs a high-resolution camera in a forest area. Location information and installation angle are required as input. The device has a motion detection function and uses an infrared sensor to detect animal movements even at night. When movement is detected, the camera captures image data. The captured image data is obtained as output.

[1114] How it works: The camera uses a motion detection algorithm to detect movement and an infrared sensor to capture animals even at night.

[1115] Step 2: Compress and transmit image data

[1116] The device receives the captured image data as input and compresses it using the JPEG compression algorithm. As output, the compressed image data is obtained, which is then transmitted to the server in real time using a secure communication protocol (e.g., HTTPS).

[1117] Specific operation: The device applies a compression algorithm to reduce the data size and transmits it over a secure network.

[1118] Step 3: Saving and analyzing image data

[1119] The server receives the compressed image data as input and stores it using a database management system (e.g., MySQL). The stored image data is then analyzed using an AI algorithm (e.g., an image recognition model using TensorFlow). The analysis results (animal species and movements) are obtained as output.

[1120] Specific operation: The server stores the received data in a database and runs an image recognition model to perform analysis.

[1121] Step 4: Visualize the analysis results

[1122] The server receives the analysis results and displays them visually using a dashboard. The input required is the analysis results (animal species and movements). The output is visualized information in the form of graphs and charts.

[1123] Specific operation: The server uses a visualization tool (e.g., D3.js or Tableau) to generate an interface that displays the analysis results.

[1124] Step 5: Alert Notification

[1125] When the server detects a new discovery or abnormal behavior, it uses the alert generation system to generate an alert as input and sends it to the terminal. As output, it gets an alert notification, which the terminal sends to the user.

[1126] Specific operation: The server uses an anomaly detection algorithm to create an alert and notify the user via email or SMS.

[1127] Environmental Health Check System

[1128] Step 1: Sensor installation and data collection

[1129] The terminal installs sensors in water bodies or forests. The installation location and the type of sensor are required as inputs. The sensors collect environmental data such as temperature, humidity, pH, etc. The collected environmental data is obtained as output.

[1130] Specific operation: The sensor measures a specific environmental parameter and sends the data to the device.

[1131] Step 2: Sending data

[1132] The terminal receives collected environmental data as input and transmits it to the server in real time. The transmitted data is obtained as output. MQTT and HTTP are used as communication protocols.

[1133] Specific operation: The device formats the data and sends it using a secure communication protocol.

[1134] Step 3: Data storage and analysis

[1135] The server receives the received environmental data as input and stores it using a database management system (e.g., PostgreSQL). The stored data is then analyzed in real time using an AI algorithm. The analysis results (anomalous environmental data) are obtained as output.

[1136] Specific operation: The server stores the data in a database and performs analysis using an anomaly detection model.

[1137] Step 4: Visualize the analysis results

[1138] The server receives the analysis results and visually displays them using a dashboard. As input, the analysis results (anomalous environmental data) are required. As output, visualized information is obtained in the form of graphs and charts.

[1139] Specific operation: The server uses a visualization tool to generate an interface that displays the analysis results.

[1140] Step 5: Alert Notification

[1141] When the server detects an anomaly, it uses the alert generation system to generate an alert as input and sends it to the terminal. As output, an alert notification is obtained. The terminal then sends the notification to the user.

[1142] Specific operation: The server uses an anomaly detection algorithm to create an alert and notify the user via email or SMS.

[1143] Illegal logging detection system

[1144] Step 1: Drone and ground sensor placement

[1145] The terminal deploys drones and ground sensors in the monitoring area. The deployment location and drone flight plan are required as inputs. The drone monitors the area using a high-resolution camera, and the ground sensors detect changes in the soil and vibrations from heavy machinery. The output is the captured image data and sensor data.

[1146] How it works: The drone flies periodically, taking pictures of the area with its camera, and ground sensors collecting environmental data.

[1147] Step 2: Capturing and Sending Data

[1148] The terminal receives data from the drone and ground sensors as input and transmits it to the server in real time. The transmitted data is obtained as output. A secure communication protocol (e.g., SSL / TLS) is used for transmission.

[1149] What happens: The device formats the data and sends it over a secure network.

[1150] Step 3: Data storage and analysis

[1151] The server receives the received data as input and stores it using a database management system (e.g., MySQL). The stored data is then analyzed using an AI algorithm (e.g., an analytical model using Keras). The analysis results (patterns of illegal logging) are obtained as output.

[1152] Specific operation: The server stores the received data in a database and runs an analytical model to detect anomalies.

[1153] Step 4: Visualize the analysis results

[1154] The server receives the analysis results and displays them visually using a dashboard. The input required is the analysis results (patterns of illegal logging). The output is visualized information in the form of graphs and charts.

[1155] Specific operation: The server uses a visualization tool to generate an interface that displays the analysis results.

[1156] Step 5: Alert Notification

[1157] When the server detects suspected illegal logging, it uses the alert generation system to generate an alert as input and sends it to the terminal. The output is an alert notification, which the terminal then sends to the user.

[1158] Specific operation: The server uses an anomaly detection algorithm to create an alert and notify the user via email or SMS.

[1159] (Application example 1)

[1160] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1161] In modern society, effective wildlife monitoring, environmental health checks, and illegal logging detection are crucial. However, performing these monitoring and detection tasks efficiently and in real time poses technical challenges, particularly in the areas of wide-area monitoring and real-time data notification. Furthermore, existing systems are complex and require specialized knowledge, making them difficult for the general public to use. A system that can solve these problems and maintain a safe and healthy environment is needed.

[1162] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1163] In this invention, the server includes a photographing device, a transmitting device, a cloud server, an analysis device, a display device, a notification device, and a real-time notification device. This enables wide-area monitoring, real-time data analysis, and rapid notification to users. Furthermore, by using a generative AI model, a system is provided that can analyze environmental data and wildlife movements with high accuracy and quickly detect and notify abnormalities.

[1164] "Photography Device Means" refers to videography equipment for capturing wildlife and environmental activity.

[1165] The "transmission device means" is a device for compressing data obtained from the image capture device or sensor device and transmitting it to the cloud server.

[1166] "Cloud Server Means" refers to a remote computing resource for receiving, storing, and analyzing data transmitted from a Transmitter Means.

[1167] The "analysis means" is a means for analyzing data received by the cloud server means using a machine learning algorithm to recognize the type and movements of wildlife and detect illegal logging and environmental abnormalities.

[1168] The "display device means" is a device for visually displaying the results obtained by the analysis means.

[1169] "Notification device means" is a device for issuing a notification to the user in the event of an abnormality or new discovery.

[1170] The "real-time notification means" is a means for notifying the mobile terminal in real time of the data collected by the photographing device means and the transmitting device means.

[1171] A "sensor device means" is a device for collecting environmental data such as temperature, humidity, pH, etc.

[1172] "Unmanned Aerial Vehicle Means" means an unmanned aerial vehicle for periodically monitoring and collecting data from a surveillance area.

[1173] A "generative AI model" is an artificial intelligence model that is trained based on large datasets to perform specific tasks with high accuracy.

[1174] "Visualization means" refers to a means for visually displaying analysis results on the cloud.

[1175] This invention is a system for efficiently monitoring wildlife, checking environmental health, and detecting illegal logging. This system collects data using various imaging and sensor devices, transmits the data to a cloud server for analysis, and provides information to users in real time.

[1176] The server first receives image data obtained from the photographing device means and environmental data obtained from the sensor device means. Specific hardware examples include cameras for the photographing device means and temperature, humidity, and pH sensors for the sensor device means. These data are then transmitted to the cloud server via the transmitting device means. This transmission utilizes an internet connection.

[1177] The cloud server stores the received data and analyzes it using machine learning algorithms. Software such as TensorFlow and PyTorch is used for this analysis. The analysis results are visualized within the cloud server and provided to the user through the display device means. The results are displayed in a dashboard format so that the user can easily check them. In addition, if an abnormality or new discovery is detected, the notification device means issues an alert to the user through the real-time notification means.

[1178] As a practical example, a camera is installed in a forest area and captures the movement of wildlife (e.g., a bear) at night. The captured image data is sent to a cloud server in real time, where an AI algorithm analyzes the image and detects the presence of a bear. The results are displayed on a dashboard, and an alert is sent to the user that a "bear has appeared." This allows the user to respond quickly.

[1179] The system uses a generative AI model to analyze data with high accuracy and quickly detect anomalies. The generative AI model is trained using prompts such as:

[1180] "Please provide us with a dataset to identify animal species. Please provide us with an AI algorithm to develop a system that can monitor animal movements in real time using an environmentally friendly approach."

[1181] According to the embodiment of the present invention, users can efficiently and accurately monitor a wide area, check the environment, and detect illegal logging. This system will greatly contribute to sustainable environmental management and safety management.

[1182] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1183] Step 1:

[1184] A camera captures the movement. The user configures and positions the camera to cover a specific area. When the camera detects movement, such as wildlife or illegal logging, it captures the movement as image data.

[1185] (Input: Wildlife or target movement,raw data; Output: Captured image data)

[1186] Step 2:

[1187] The device compresses the captured image data and sends it to the cloud server. The device compresses the captured raw data for efficient transmission. The compressed data is then sent to the cloud server via Wi-Fi or mobile network.

[1188] (Input: captured image data; Output: compressed image data)

[1189] Step 3:

[1190] The cloud server stores the received image data and analyzes it using machine learning algorithms. The cloud server stores the data in a database and analyzes it using machine learning frameworks such as TensorFlow and PyTorch. The analysis is performed using a generative AI model to identify the type of animal and its movements from the data.

[1191] (Input: Compressed image data; Output: Recognition results of animal species and movements)

[1192] Step 4:

[1193] The server visualizes the analysis results on the cloud and provides them to the user through a display device. The analyzed data is provided to the user in the form of graphs and dashboards, which can be accessed from a web browser or a dedicated application.

[1194] (Input: Recognition results of animal species and movements; Output: Visualized analysis results on a dashboard)

[1195] Step 5:

[1196] If the server detects an anomaly or new discovery, it will issue a real-time notification to the user through a notification device. When the server detects an anomaly based on the analysis results, it will quickly send a notification to the user's smartphone or computer. The notification will be delivered as a text message or in-app notification.

[1197] (Input: Anomaly detection results; Output: Real-time notification)

[1198] Step 6:

[1199] Users receive notifications and access the system dashboard to view detailed data. Users can check alerts from their smartphones or computers, log in to the dashboard, and view footage and data analysis results.

[1200] (Input: Real-time notification; Output: User confirmation of detailed data)

[1201] These processing steps enable the system to efficiently monitor wildlife, check environmental health, and detect illegal logging in real time. The use of generative AI models enables highly accurate data analysis, prompting users to take immediate action.

[1202] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1203] The present invention provides a system that provides more advanced feedback by combining an emotion engine that recognizes user emotions with a system that efficiently monitors wildlife, checks environmental health, and detects illegal logging. Specific embodiments of the present invention are described below.

[1204] Wildlife Monitoring System

[1205] Program processing

[1206] 1. The device installs a camera in a forest area and performs an operation check. When the camera detects movement, it captures image data.

[1207] 2. The device compresses the captured image data and sends it to the server using an encryption protocol.

[1208] 3. The server stores the received data and analyzes it using AI algorithms, using image recognition technology to identify the type and movements of wildlife.

[1209] 4. The server visualizes the analysis results on a dashboard, displaying them in graphs and charts so that users can intuitively understand the results.

[1210] 5. If the server detects an abnormality or new discovery, it will send an alert to the device using a notification method.

[1211] 6. The device issues an alert to the user, and at the same time, the emotion engine analyzes the user's reaction, thereby monitoring the user's emotional state in real time.

[1212] 7. The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user reacts with surprise or joy, that information is collected.

[1213] 8. The server adjusts the content and format of notifications based on the emotional data, personalizing feedback based on the user’s emotional state.

[1214] Specific examples

[1215] A user sets up a camera in a forested area. The camera captures the movements of large animals (e.g., bears) at night, and the device sends the data in real time to a server. The server analyzes the images, identifies the presence of a bear, and displays that information on a dashboard. The server then sends an alert to the user that a "bear has appeared." The device analyzes the user's reaction using an emotion engine, and if the user is surprised, the emotion data is sent to the server, and the next notification will be more gentle.

[1216] Environmental Health Check System

[1217] Program processing

[1218] 1. The device installs sensors in forests and water areas and checks their operation. The sensors collect environmental data (temperature, humidity, pH, etc.).

[1219] 2. The device sends the collected data to the server in real time.

[1220] 3. The server receives and stores the data, then uses AI algorithms to analyze it and detect outliers.

[1221] 4. The server displays the analysis results on a dashboard and issues an alert if an abnormal value is detected.

[1222] 5. If the server detects an abnormality, it sends that information to the terminal and notifies the user.

[1223] 6. At the same time that the device issues an alert, the emotion engine analyzes the user's reaction, monitoring the user's emotional state in real time.

[1224] 7. The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user shows a nervous reaction, that information is collected.

[1225] 8. The server adjusts the content and format of notifications based on the emotional data, personalizing feedback based on the user’s emotional state.

[1226] Specific examples

[1227] The device installs a sensor in the water body and detects a sudden rise in pH level, sending the data to the server in real time. The server analyzes the data and determines that there is a possibility of water pollution. The server then sends an alert to the user stating that there is a possibility of water pollution. The device then analyzes the user's reaction using an emotion engine, and if the user becomes nervous, the emotion data is sent to the server, and the next notification will be more reassuring.

[1228] Illegal logging detection system

[1229] Program processing

[1230] 1. The device places drones and ground sensors in the surveillance area and sets the drone's flight path.

[1231] 2. Drones fly over the monitored area periodically and capture image data, and ground sensors collect data as well.

[1232] 3. The device compresses the captured data and sends it to the server using an encryption protocol.

[1233] 4. The server stores the received data and analyzes it using AI algorithms to detect patterns of illegal logging.

[1234] 5. The server visualizes the analysis results on a dashboard, allowing users to easily and intuitively understand the results.

[1235] 6. If the server detects suspicion of illegal logging, it will send an alert to the terminal using a notification method.

[1236] 7. The device issues an alert to the user, and at the same time, the emotion engine analyzes the user's reaction, monitoring the user's emotional state in real time.

[1237] 8. The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user reacts impatient or angry, that information is collected.

[1238] 9. The server adjusts the content and format of notifications based on the emotional data, personalizing feedback based on the user’s emotional state.

[1239] Specific examples

[1240] A user uses a drone to monitor a wide area of ​​forest. The drone captures abnormal logging activity in a certain area, and the device sends the data in real time to a server. The server analyzes the data and determines that illegal logging is suspected. The server then sends an alert to the user stating that "illegal logging is suspected." The device uses an emotion engine to analyze the user's reaction, and if the user becomes panicked, the emotional data is sent to the server, and the next notification will be delivered with content that allows the user to respond more calmly.

[1241] This process allows for efficient, real-time wildlife monitoring, environmental health checks, and illegal logging detection, while also providing personalized feedback based on the user's emotional state.

[1242] The processing flow will be explained below.

[1243] Combining emotion engines with wildlife monitoring systems

[1244] Program processing

[1245] Step 1:

[1246] The device installs a camera in the forest area. It turns on the camera and checks its operation. After confirming that it is working properly, it starts monitoring mode.

[1247] Step 2:

[1248] The camera detects motion and captures wildlife movements, triggering the generation of continuous image data.

[1249] Step 3:

[1250] The device compresses the captured image data and transmits it to a server in real time using an encryption protocol.

[1251] Step 4:

[1252] The server stores the received data and also performs backup processing to ensure data safety.

[1253] Step 5:

[1254] The server analyzes the stored image data with AI algorithms and uses image recognition technology to identify the type and movement of wildlife, for example by classifying animals based on their shape, color, and size.

[1255] Step 6:

[1256] The server visualizes the analysis results on a dashboard, generating and displaying graphs and charts that are easy for users to understand intuitively.

[1257] Step 7:

[1258] If the server detects any new findings or anomalies, it will use a notification mechanism to send an alert to the device, including details of the analysis results and an image.

[1259] Step 8:

[1260] The device issues an alert to the user. At the same time, the emotion engine analyzes the user's reaction and monitors the user's emotional state in real time after seeing the alert.

[1261] Step 9:

[1262] The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user shows a reaction of surprise or joy, that information is collected.

[1263] Step 10:

[1264] The server adjusts the content and format of notifications based on the emotional data, so the next notification will be personalized based on the user's emotional state.

[1265] Combining an environmental health check system with an emotion engine

[1266] Program processing

[1267] Step 1:

[1268] The device will install environmental sensors in forests and water areas, and check the operation of the sensors to ensure they are working properly.

[1269] Step 2:

[1270] The sensor collects environmental data (temperature, humidity, pH, etc.) at set intervals and stores the data in temporary storage.

[1271] Step 3:

[1272] The data collected by the terminal is sent to the server at regular intervals using an encryption protocol.

[1273] Step 4:

[1274] The server stores the received data, checks its integrity, and simultaneously backs up the data.

[1275] Step 5:

[1276] The server analyzes the received data in real time and uses AI algorithms to detect abnormalities in the environmental data (for example, sudden changes in pH).

[1277] Step 6:

[1278] The server visualizes the analysis results on a dashboard, and if anomalies are detected, details are displayed in graphs and charts.

[1279] Step 7:

[1280] If the server detects an abnormality, it will use a notification method to send an alert to the device, which will include the specific abnormal value and location information.

[1281] Step 8:

[1282] The device issues an alert to the user. At the same time, the emotion engine analyzes the user's reaction and monitors the user's emotional state in real time after seeing the alert.

[1283] Step 9:

[1284] The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user shows a nervous reaction, that information is collected.

[1285] Step 10:

[1286] The server adjusts the content and format of notifications based on the emotional data, so the next notification will be personalized based on the user's emotional state.

[1287] Combining an emotion engine with an illegal logging detection system

[1288] Program processing

[1289] Step 1:

[1290] The terminal places the drone and ground sensors in the monitoring area, sets the drone's flight path, and performs operational checks to confirm normal operation.

[1291] Step 2:

[1292] Drones fly according to a set schedule and capture image data of the monitored area, and ground sensors collect data as well.

[1293] Step 3:

[1294] The device compresses the captured data and sends it to the server using an encryption protocol.

[1295] Step 4:

[1296] The server saves the received data, checks the integrity of the data, and simultaneously backs up the received data.

[1297] Step 5:

[1298] The server analyzes the received data using AI algorithms to detect patterns of illegal logging. For example, if a large amount of timber is being cut all at once, it will detect this change.

[1299] Step 6:

[1300] The server visualizes the analysis results on a dashboard, displaying them in graphs and maps that are easy for users to understand intuitively.

[1301] Step 7:

[1302] If the server detects any suspicion of illegal logging, it will use a notification method to send an alert to the device, which will include specific location information and the results of image analysis.

[1303] Step 8:

[1304] The device issues an alert to the user. At the same time, the emotion engine analyzes the user's reaction and monitors the user's emotional state in real time after seeing the alert.

[1305] Step 9:

[1306] The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user shows impatience or anger, that information is collected.

[1307] Step 10:

[1308] The server adjusts the content and format of notifications based on the emotional data, so the next notification will be personalized based on the user's emotional state.

[1309] This process allows for efficient, real-time wildlife monitoring, environmental health checks, and illegal logging detection, while also providing personalized feedback based on the user's emotional state.

[1310] Example 2

[1311] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1312] While existing systems for wildlife monitoring, environmental health checks, and illegal logging detection are efficient at collecting and analyzing data, they lack the ability to personalize feedback based on the user's emotional state. As a result, the amount and quality of information users receive is constant, and they lack the flexibility to adapt to specific situations. Furthermore, when detecting anomalies or new discoveries, they lack the interactive support to help users respond calmly.

[1313] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a photographing means, a device means, a server means, an analysis means, a display means, a notification means, an emotion engine and a means for transmitting the data to the server, and a feedback means. This makes it possible to efficiently collect data, analyze it in real time, and adjust the content and format of notifications based on the user's emotions. By analyzing the user's reactions with the emotion engine and providing personalized feedback according to each situation, a system is realized that can provide the optimal response for each usage scenario.

[1314] "Capture means" refers to a device that captures wildlife and environmental movements and obtains image data.

[1315] "Device means" refers to a device for compressing data obtained from the imaging means or sensor means and transmitting it to a server using an encryption protocol.

[1316] "Server means" refers to a server that receives data transmitted from device means, decrypts and stores it, and further analyzes it.

[1317] "Analysis means" refers to the function of analyzing data received by the server means using an AI algorithm to recognize or detect the type and movement of wildlife, abnormalities in environmental data, and illegal logging.

[1318] "Display means" refers to an interface that visually displays the analysis results and shows them in graphs or charts so that the user can intuitively understand them.

[1319] "Notification means" refers to the function of sending an alert to the user via the terminal in the event of an abnormality or new discovery.

[1320] An "emotion engine" refers to an analysis device that analyzes a user's reaction and sends the results to a server.

[1321] "Feedback means" refers to a function that adjusts the content and format of notifications based on the user's emotional data, thereby personalizing feedback.

[1322] The present invention provides a system for efficiently monitoring wildlife, checking environmental health, and detecting illegal logging by combining it with an emotion engine that recognizes user emotions to provide more advanced feedback. Specific embodiments of the present invention are described below.

[1323] Basic system configuration

[1324] This system is mainly composed of the following means:

[1325] 1. Filming Method

[1326] The photographing means includes a camera device for capturing wildlife movements, such as a highly sensitive infrared camera or a camera with motion detection capabilities. The camera is installed in a designated forest area and captures image data when it detects movement.

[1327] 2. Equipment means

[1328] The device means compresses the image data obtained from the photographing means and the environmental data obtained from the sensor means, protects the data using the AES encryption protocol, and transmits it to the server. Data compression reduces the communication load and enables efficient data transmission.

[1329] 3. Server Means

[1330] The server means receives, decrypts, and stores data sent from the device means. The server is equipped with high-performance storage capable of processing large amounts of data and an AI algorithm (e.g., YOLO or ResNet) for data analysis.

[1331] 4. Analysis method

[1332] The analysis means uses AI algorithms to analyze data received and stored in the server means, and has the ability to recognize the types and movements of wildlife. Environmental data and illegal logging detection are also analyzed in the same way.

[1333] 5. Display means

[1334] The display means is a dashboard interface that visually represents the results of the analysis performed by the server. The dashboard visualizes the results in graphs and charts, allowing users to intuitively understand them.

[1335] 6. Means of notification

[1336] The notification function has the function of sending alerts to users if an abnormality or new discovery is detected based on the analysis results. Notifications are delivered to users via mobile app, email, or SMS.

[1337] 7. Emotion Engine

[1338] The emotion engine has the function of analyzing the user's reactions and sending the data to the server. For example, it analyzes the user's reactions such as surprise, joy, and tension, and collects the emotional data.

[1339] 8. Feedback channels

[1340] The feedback mechanism has a function that allows the server to adjust the content and format of the next notification based on data from the emotion engine, thereby personalizing the feedback, thereby providing the most appropriate information for the user.

[1341] Specific examples

[1342] For example, if a user installs a camera in a forest area and the camera captures the movement of a large animal (e.g., a bear) at night, the image data is sent in real time to a server by a device means. The server analyzes the data, identifies the presence of a bear, and displays the information on a dashboard. Based on the analysis results, the server sends an alert to the user through a notification means that a "bear has appeared." If the device analyzes the user's reaction with an emotion engine, and the user is surprised, the emotion data is sent to the server, and the next notification will be provided in a gentler tone.

[1343] Prompt Sentence Examples

[1344] "Create a program that describes a system in which a user-installed camera detects movement in a forested area and sends the image data to a server. The server then analyzes the data to identify the type of wildlife and notify the user."

[1345] As can be seen, the present invention can enhance wildlife monitoring, environmental health checks, illegal logging detection, and provide personalized feedback based on the user's emotional state.

[1346] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1347] Step 1:

[1348] The user installs the imaging device (camera) in a forest area and checks its operation. The user fixes the camera in an area specified by the user and checks that the motion detection function is working properly. The input for the imaging device includes the setting parameters for detecting motion and camera position information. The output is image data generated when motion is detected.

[1349] Step 2:

[1350] Image data is captured when the imaging means detects motion. For example, a camera may trigger a shutter in response to wildlife movement. The input is the detection result of a motion detection sensor, and the output is the captured image data.

[1351] Step 3:

[1352] The device compresses the image data obtained from the image capture means and sends it to the server using an encryption protocol. Specifically, the image data is compressed into a format such as JPEG and encrypted using an encryption method such as AES256. The input is the captured image data, and the output is compressed and encrypted data.

[1353] Step 4:

[1354] The server receives the data sent from the terminal, decrypts it, and stores it. Specifically, the server receives the encrypted data and decrypts it using an AES256 key. It then stores it in a database. The input is encrypted image data, and the output is the original decrypted image data, which is then stored in the database.

[1355] Step 5:

[1356] The server then analyzes the decoded data using AI algorithms. For example, image recognition techniques such as YOLO or ResNet are used to identify the wildlife species and their movements. The input is the decoded image data, and the output is information about the wildlife species and their movements.

[1357] Step 6:

[1358] The server displays the analysis results on a dashboard. It converts the data into graphs and charts and visualizes them so that users can easily understand them. The input is the analysis result data, and the output is visualized graphs and charts displayed on the dashboard screen.

[1359] Step 7:

[1360] If the server detects an abnormality or a new discovery, it will send an alert to the device using a notification method. Specifically, it will send an alert message using email, SMS, etc. The input is the analysis result, and the output is an alert message that is generated and sent to the device.

[1361] Step 8:

[1362] The device issues an alert to the user and analyzes the user's reaction using an emotion engine. For example, the alert can be displayed through a mobile app or a dedicated device, and the emotion engine can analyze the user's facial expression and tone of voice. The input is an alert message from the server, and the output is the user's emotional data.

[1363] Step 9:

[1364] The emotion engine analyzes the user's emotion data and sends it to the server. Specifically, the user's facial expressions and tone of voice are sent to the server as numerical data. The input is the user's emotion data, and the output is the emotion data sent to the server.

[1365] Step 10:

[1366] The server adjusts the content and format of notifications based on the emotion data. For example, if the user is surprised, the next notification will be sent in a softer tone. The input is the emotion data sent by the emotion engine, and the output is the adjusted content and format of the notification.

[1367] Through these steps, the wildlife monitoring system can efficiently collect and analyze data and provide personalized feedback based on the user's emotional state.

[1368] (Application example 2)

[1369] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1370] While existing wildlife monitoring systems, environmental health check systems, and illegal logging detection systems are capable of advanced monitoring and data analysis, they lack the functionality to provide feedback based on the user's emotions, resulting in a limited user experience. In particular, if alert notifications cause excessive tension or anxiety in the user, this can hinder subsequent response.

[1371] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes emotion engine means for analyzing the user's emotion data, feedback means for transmitting the emotion data analyzed by the emotion engine means to the server and for personalizing the notification content, and notification means for issuing a notification to the user when an abnormality or new discovery is detected. This enables the alert notification to be personalized according to the user's emotional state, helping the user to respond appropriately and quickly.

[1372] "Wildlife" means any organism other than humans that inhabits the natural environment, especially animals.

[1373] "Camera means" refers to a device used to photograph wildlife and the environment and obtain image data thereof.

[1374] "Terminal means" refers to a device that compresses and transmits data obtained from camera means or sensor means.

[1375] "Server means" refers to a device or group of devices that receives and stores data sent from terminal means and performs the necessary processing.

[1376] "Analysis means" refers to a device or group of devices that analyzes data received by the server means using an AI algorithm and extracts and recognizes specific information.

[1377] The "dashboard means" is a device or interface for visually displaying the analysis results obtained by the analysis means.

[1378] "Notification means" refers to a device or function that issues an appropriate notification to the user in the event of an abnormality or new discovery.

[1379] The "emotion engine means" is a device or algorithm that analyzes the user's emotional data and personalizes the notification content based on that data.

[1380] The "feedback means" is a device or function for providing the user with optimal notification content using emotion data analyzed by the emotion engine means.

[1381] "Sensor means" refers to a device for collecting environmental data such as temperature, humidity, pH value, etc.

[1382] A "drone vehicle" is a flying device that periodically flies over a surveillance area from the air and captures image data.

[1383] This invention relates to an advanced environmental monitoring system for analyzing user emotions and optimizing the feedback. Specifically, by integrating an emotion engine into systems for wildlife monitoring, environmental health checks, and illegal logging detection, the system provides feedback according to the user's emotions.

[1384] 1. Wildlife Monitoring System

[1385] Hardware Configuration

[1386] In this system, multiple cameras are used to detect wildlife movement. When a camera detects movement, it captures image data and transmits it to a terminal device. The terminal device compresses and encrypts the data and transmits it to a server device.

[1387] Software Configuration

[1388] The server stores the received data and analyzes it using AI algorithms. The AI ​​uses image recognition technology to identify wildlife species and movements. The results are displayed visually on a dashboard in an intuitive format for users.

[1389] If an abnormality or new discovery is detected, the notification means issues a notification to the user. At the same time, the emotion engine analyzes the user's reaction and sends the emotional data to the server. Based on this data, the server personalizes the notification content and adjusts the next feedback.

[1390] Specific examples

[1391] A user sets up a camera in a forested area. The camera captures bear movements at night and sends the image data to a server. The server identifies the bear's presence and displays it on a dashboard. When an alert is sent to the user that a bear has appeared, the emotion engine analyzes the user's reaction to surprise. As a result, the next notification will be presented in a more toned-down format.

[1392] 2. Environmental Health Check System

[1393] Hardware Configuration

[1394] In this system, multiple sensors are used to collect environmental data such as temperature, humidity, pH, etc. The sensor means transmits the collected data in real time to the terminal means, and the terminal means transmits the data to the server means.

[1395] Software Configuration

[1396] The server means stores the received data and analyzes it using an AI algorithm. If an abnormality is detected, the notification means issues a notification to the user. The emotion engine analyzes the user's reaction and sends the emotion data to the server. The server uses this data to personalize the notification content.

[1397] Specific examples

[1398] A sensor installed by the user in a body of water detects a sudden rise in pH and sends the data to a server in real time. The server analyzes the data and determines that there is a possibility of water pollution. An alert is sent to the user stating "Possible water pollution" and an emotion engine analyzes the user's tense reaction. As a result, the next notification is presented in a reassuring format.

[1399] 3. Illegal Logging Detection System

[1400] Hardware Configuration

[1401] In this system, a drone is used to monitor a monitoring area from the air. Sensor means is also used to collect data on the ground. Data obtained from the drone and sensor means is transmitted to server means via terminal means.

[1402] Software Configuration

[1403] The server means stores the received data and uses an AI algorithm to detect illegal logging. If an abnormality is detected, the notification means issues a notification to the user. The emotion engine analyzes the user's reaction and sends the emotional data to the server. The server uses this data to personalize the notification content.

[1404] Specific examples

[1405] A user uses a drone to monitor a wide area of ​​forest. The drone captures abnormal logging activity in a certain area and sends the data to a server in real time. The server analyzes the data and detects possible illegal logging. An alert is sent to the user stating "Possible illegal logging" and an emotion engine analyzes the user's panicked reaction. As a result, the next notification is presented in a format that encourages a calm response.

[1406] Prompt Sentence Examples

[1407] "How can I combine sentiment analysis with notifications from my home security system to provide personalized feedback based on the user's response?"

[1408] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1409] Program processing flow

[1410] Step 1: The camera detects motion and captures an image

[1411] Input: The camera detects motion within the monitored area.

[1412] Data processing: Capture images using movement as a trigger.

[1413] Output: The captured image data is sent to the terminal means.

[1414] Specific operation: When the camera means detects movement, it automatically releases the shutter and saves the image at that time in memory.

[1415] Step 2: The device compresses the image data and sends it to the server

[1416] Input: Image data captured from a camera means.

[1417] Data processing: Compression and encryption of image data.

[1418] Output: Send the compressed image data to the server means.

[1419] Specific operation: The terminal means compresses the image data from the camera to reduce its size, and transmits the data to the server means using an encryption protocol.

[1420] Step 3: The server stores the received data and analyzes it with AI

[1421] Input: Compressed and encrypted image data sent from the terminal means.

[1422] Data processing: decryption and data storage.

[1423] Output: Extract the analysis results and send them to a dashboard.

[1424] What it does: The server decrypts the compressed data and stores it in a database. It uses AI algorithms to perform image recognition and identify wildlife species and movements.

[1425] Step 4: Display the analysis results on the dashboard

[1426] Input: Recognition results from AI analysis.

[1427] Data processing: Converting data into a visual format.

[1428] Output: Displayed on the dashboard.

[1429] Specific operation: The server means converts the analysis results into graphs or charts and displays them on the user interface.

[1430] Step 5: Issue a user notification when an error occurs

[1431] Input: Data whose analysis results indicate an anomaly.

[1432] Data processing: Generation of notification content.

[1433] Output: Sends a notification to the user.

[1434] Specific operation: If the notification means detects an abnormality, it will issue an alert to the user based on pre-defined rules.

[1435] Step 6: Analyze user reactions with the emotion engine

[1436] Input: Real-time user responses (e.g., data from a smartphone camera or sensors).

[1437] Data processing: Data analysis using sentiment analysis algorithms.

[1438] Output: Generate emotion data and send it to the server.

[1439] Specific operation: The emotion engine means performs facial and voice recognition on the user to identify emotions such as surprise or anxiety.

[1440] Step 7: The server analyzes the emotion data and adjusts the notification content.

[1441] Input: Emotion data sent from the emotion engine means.

[1442] Data processing: Feedback adjustment based on emotion data.

[1443] Output: Adjustment result for next notification content.

[1444] Specific operation: The server analyzes the emotional data and adjusts the next notification content, such as softening it, depending on the user's emotional state.

[1445] Step 8: Sending feedback to the server

[1446] Input: Reconciliation result by server means.

[1447] Data processing: generating the final feedback content.

[1448] Output: The revised notification is sent to the user.

[1449] Specific operation: The server means generates tailored notification content and sends feedback that gives the user peace of mind.

[1450] 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.

[1451] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1452] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1453] [Fourth embodiment]

[1454] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1455] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1456] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1457] 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.

[1458] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1459] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1460] 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.

[1461] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1462] 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.

[1463] 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 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.

[1464] 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.

[1465] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1466] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1467] The present invention provides a system for efficiently monitoring wildlife, checking environmental health, and detecting illegal logging. Each system is implemented as follows.

[1468] Wildlife Monitoring System

[1469] Program processing

[1470] 1. The device places cameras in specific forest areas, and when the cameras detect wildlife movement, they capture image data.

[1471] 2. The device compresses the captured image data and sends it to the server in real time.

[1472] 3. The server stores the received image data and analyzes it using AI algorithms, specifically using image recognition technology to identify the type and movement of wildlife.

[1473] 4. The server visualizes the analysis results in a dashboard, allowing users to easily check the results.

[1474] 5. If the server detects any new findings or abnormal behavior, it sends an alert to the device and notifies the user.

[1475] Specific examples

[1476] A user sets up a camera in a forest area. The camera captures the movements of large animals (e.g., bears) at night, and the device sends the image data to a server in real time. The server analyzes the image, identifies the presence of a bear, and displays that information on a dashboard. At the same time, an alert is sent to the user that a bear has appeared.

[1477] Environmental Health Check System

[1478] Program processing

[1479] 1. The device places sensors in forests, bodies of water, etc. The sensors collect environmental data such as temperature, humidity, and pH.

[1480] 2. The device sends the collected data to the server in real time.

[1481] 3. The server stores the received data and analyzes it using AI algorithms to detect abnormal values ​​(e.g., sudden fluctuations in pH).

[1482] 4. The server visualizes the analysis results in a dashboard, allowing users to easily check the results.

[1483] 5. If the server detects an abnormality, it sends that information to the terminal and notifies the user.

[1484] Specific examples

[1485] The device detects a sudden rise in pH value using sensors installed in the water area and sends the data to the server in real time. The server analyzes the data, determines whether there is a possibility of water pollution, and displays the results on a dashboard. At the same time, an alert is sent to the user, informing them that there is a possibility of water pollution.

[1486] Illegal logging detection system

[1487] Program processing

[1488] 1. The terminal deploys a drone and ground sensors in the monitoring area. The drone periodically flies over the monitoring area and captures image data.

[1489] 2. The device sends the captured data to the server. Ground sensors also collect and send data.

[1490] 3. The server stores the received data and analyzes it using AI algorithms to detect patterns of illegal logging.

[1491] 4. The server visualizes the analysis results in a dashboard, allowing users to easily check the results.

[1492] 5. If the server detects any suspicion of illegal logging, it sends the information to the terminal and notifies the user.

[1493] Specific examples

[1494] A user uses a drone to monitor a wide area of ​​forest. The drone captures abnormal logging activity in a certain area, and the device sends the data to a server in real time. The server analyzes the data, determines whether there is any suspicion of illegal logging, and displays the information on a dashboard. At the same time, an alert is sent to the user informing them of the possibility of illegal logging.

[1495] This process allows for efficient, real-time wildlife monitoring, environmental health checks, and illegal logging detection, allowing users to take immediate action.

[1496] The processing flow will be explained below.

[1497] Wildlife Monitoring System

[1498] Program processing

[1499] Step 1:

[1500] The device will set up a camera in the forest area, power it on, and check its operation. If there are no abnormalities, it will enter outdoor surveillance mode.

[1501] Step 2:

[1502] The camera detects motion and captures wildlife movements, triggering a capture and generating continuous image data.

[1503] Step 3:

[1504] The terminal compresses the captured image data, encrypts it using a data transfer protocol, and then transmits it to the server.

[1505] Step 4:

[1506] The server stores the image data received from the device, and simultaneously performs backup processing to ensure data integrity.

[1507] Step 5:

[1508] The server analyzes the received image data using AI algorithms. Image recognition technology is used to identify the type and movement of wildlife, for example, categorizing animals based on their shape, color, and size.

[1509] Step 6:

[1510] The server visualizes the analysis results on a dashboard, generating graphs and charts to help users intuitively understand the results.

[1511] Step 7:

[1512] If the server detects a new discovery or anomaly, it sends an alert to the device. The notification includes details of the analysis results and an image. The device then issues a notification to the user.

[1513] Environmental Health Check System

[1514] Program processing

[1515] Step 1:

[1516] The device will install environmental sensors in forests and water areas, and check that the sensors are working properly and can collect data properly.

[1517] Step 2:

[1518] Sensors collect environmental data (temperature, humidity, pH, etc.) at set intervals. As data is collected, it is saved in temporary storage.

[1519] Step 3:

[1520] The device periodically transmits data obtained from the sensors to the server, and the data is encrypted during transmission.

[1521] Step 4:

[1522] The server receives the data sent from the device and stores it in a database, also checking the integrity of the received data.

[1523] Step 5:

[1524] The server analyzes the received data in real time and uses AI algorithms to detect anomalies in the environmental data, for example, identifying abnormal fluctuations compared to established baseline values.

[1525] Step 6:

[1526] The server visualizes the analysis results on a dashboard, and if anomalies are detected, details are displayed in graphs and charts.

[1527] Step 7:

[1528] If the server detects an anomaly, it sends an alert to the device, which includes the specific anomaly value and location information. The device then issues a notification to the user.

[1529] Illegal logging detection system

[1530] Program processing

[1531] Step 1:

[1532] The terminal places the drone and ground sensors in the monitoring area, sets the drone's flight path, and checks the sensor's operation.

[1533] Step 2:

[1534] The drone flies according to a set schedule and captures image data of the monitored area, while ground sensors simultaneously collect data.

[1535] Step 3:

[1536] The device compresses the data obtained from the drone and sensors and transmits it to a server using a secure protocol.

[1537] Step 4:

[1538] The server receives the data sent from the device and stores it in a database. After receiving the data, it checks it to ensure its integrity.

[1539] Step 5:

[1540] The server analyzes the received data using AI algorithms. Image analysis and pattern recognition technology are used to detect possible illegal logging. For example, if a large amount of timber is being cut all at once, the system will detect any changes.

[1541] Step 6:

[1542] The server visualizes the analysis results on a dashboard, displaying them on graphs and maps to help users intuitively understand the results.

[1543] Step 7:

[1544] If the server detects any suspected illegal logging, it sends an alert to the device, which then issues a notification to the user, including specific location information and the results of image analysis.

[1545] Example 1

[1546] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1547] Current wildlife monitoring systems, environmental health check systems, and illegal logging detection systems often lack efficiency and accuracy in real-time data processing and anomaly detection. They also need to operate accurately at night and in bad weather. To address these issues, effective use of sensor technology and AI algorithms is essential.

[1548] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1549] In this invention, the server includes a means for analyzing received data using an AI algorithm to detect anomalies or new patterns, a dashboard means for visually displaying the analysis results, and a notification means for issuing a notification to the user when an anomaly or new pattern is detected, thereby enabling high-precision data analysis and anomaly detection in real time.

[1550] "Camera means" refers to a photographic device installed to capture the movements of wildlife, and is equipped with the ability to detect animal movements using infrared sensors even at night.

[1551] "Terminal means" is a device that compresses data obtained from sensors and cameras and transmits it to a server using a secure communication protocol.

[1552] "Server means" refers to a system that receives data sent from terminal means, stores it, and analyzes it using an AI algorithm.

[1553] The "analysis means" is a function that uses an AI algorithm to analyze data received by the server means, recognize the types and movements of wildlife, and detect environmental data and illegal logging.

[1554] The "dashboard means" is an interface that visually displays the analysis results so that the user can easily check them, and uses visualization tools to represent the results in graphs and charts.

[1555] "Notification methods" are functions that notify users when an abnormality or new discovery is detected, and provide information via email, SMS, push notifications, etc.

[1556] "Sensor means" refers to a device that collects environmental data such as temperature, humidity, pH, etc., and is used to obtain data with high accuracy.

[1557] A "drone vehicle" is an unmanned aerial vehicle that flies over a surveillance area, monitors the area using a high-resolution camera, and collects data.

[1558] A "generative AI model" is a learning model that uses AI algorithms to analyze specific data and perform pattern recognition and anomaly detection.

[1559] A "prompt sentence" is an input sentence that instructs a generative AI model to perform a specific analysis or recognition.

[1560] The present invention is an efficient and highly accurate system for wildlife monitoring, environmental health checks, and illegal logging detection. The detailed methods described below illustrate the preferred embodiments of the invention.

[1561] Wildlife Monitoring System

[1562] The device installs a high-resolution camera in a specific forest area. The camera has motion detection capabilities, and in particular, can detect animal movement even at night using an infrared sensor. When motion is detected, the camera captures image data. At the same time, the device compresses this image data using the JPEG compression algorithm and transmits it to a server in real time using a secure communication protocol (e.g., HTTPS).

[1563] The server stores the received image data in a database management system (e.g., MySQL), then applies an AI algorithm (e.g., an image recognition model using TensorFlow) to analyze the image data. This analysis identifies the type and movement of wildlife.

[1564] Finally, the analysis results are visually displayed through a dashboard. Visualization tools such as D3.js and Tableau are used for visualization, allowing users to easily check the analysis results. In addition, if abnormal behavior or new discoveries are detected, the server creates an alert and sends it to the terminal, which notifies the user.

[1565] Specific examples

[1566] A user sets up a camera in a forest area. The camera captures the movements of large animals (e.g., bears) at night, and the device sends the image data to a server in real time. The server analyzes the image, identifies the presence of a bear, and displays that information on a dashboard. At the same time, an alert is sent to the user that a bear has appeared.

[1567] Example prompts for generative AI models

[1568] Identify the type of wildlife captured by this camera.

[1569] Environmental Health Check System

[1570] The terminal places sensors in water bodies or forests. The sensors collect environmental data such as temperature, humidity, and pH. These sensors are typically built using Arduino or Raspberry Pi. The collected data is sent to a server in real time via the terminal. MQTT or HTTP is used as the communication protocol.

[1571] The server stores the received data in a database management system (e.g., PostgreSQL), and then applies AI algorithms to analyze the environmental data in real time to detect abnormalities, such as sudden pH fluctuations.

[1572] The analysis results are visually displayed on a dashboard, allowing users to easily check them. If an abnormality is detected, the server generates an alert and the device notifies the user.

[1573] Specific examples

[1574] The device detects a sudden rise in pH value using sensors installed in the water area and sends the data to the server in real time. The server analyzes the data, determines whether there is a possibility of water pollution, and displays the results on a dashboard. At the same time, the user is informed that there is a possibility of water pollution.

[1575] Example prompts for generative AI models

[1576] Detect unusual environmental fluctuations from this data.

[1577] Illegal logging detection system

[1578] The terminal places a drone and ground sensors in the monitoring area. The drone, equipped with a high-resolution camera, flies periodically to capture images of the monitoring area, while the ground sensors detect changes in the soil and vibrations from heavy machinery. Data obtained from the drone and sensors is sent to a server in real time via the terminal. A secure communication protocol (e.g., SSL / TLS) is used for transmission.

[1579] The server stores the received data in a database management system and uses AI algorithms (for example, analytical models using Keras) to analyze patterns of illegal logging. If the results are determined to be abnormal, an alert is generated.

[1580] The analysis results are displayed on a dashboard, where users can check them. If any abnormal monitoring activity is detected, the server sends an alert to the terminal, notifying the user.

[1581] Specific examples

[1582] A user uses a drone to monitor a wide area of ​​forest. The drone captures abnormal logging activity in a certain area, and the device sends the data to a server in real time. The server analyzes the data, determines whether there is any suspicion of illegal logging, and displays the information on a dashboard. At the same time, an alert is sent to the user informing them of the possibility of illegal logging.

[1583] Example prompts for generative AI models

[1584] Detect patterns of illegal logging in this image.

[1585] This process allows for efficient, real-time wildlife monitoring, environmental health checks, and illegal logging detection, allowing users to take immediate action.

[1586] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1587] Wildlife Monitoring System

[1588] Step 1: Camera installation and motion detection

[1589] The device installs a high-resolution camera in a forest area. Location information and installation angle are required as input. The device has a motion detection function and uses an infrared sensor to detect animal movements even at night. When movement is detected, the camera captures image data. The captured image data is obtained as output.

[1590] How it works: The camera uses a motion detection algorithm to detect movement and an infrared sensor to capture animals even at night.

[1591] Step 2: Compress and transmit image data

[1592] The device receives the captured image data as input and compresses it using the JPEG compression algorithm. As output, the compressed image data is obtained, which is then transmitted to the server in real time using a secure communication protocol (e.g., HTTPS).

[1593] Specific operation: The device applies a compression algorithm to reduce the data size and transmits it over a secure network.

[1594] Step 3: Saving and analyzing image data

[1595] The server receives the compressed image data as input and stores it using a database management system (e.g., MySQL). The stored image data is then analyzed using an AI algorithm (e.g., an image recognition model using TensorFlow). The analysis results (animal species and movements) are obtained as output.

[1596] Specific operation: The server stores the received data in a database and runs an image recognition model to perform analysis.

[1597] Step 4: Visualize the analysis results

[1598] The server receives the analysis results and displays them visually using a dashboard. The input required is the analysis results (animal species and movements). The output is visualized information in the form of graphs and charts.

[1599] Specific operation: The server uses a visualization tool (e.g., D3.js or Tableau) to generate an interface that displays the analysis results.

[1600] Step 5: Alert Notification

[1601] When the server detects a new discovery or abnormal behavior, it uses the alert generation system to generate an alert as input and sends it to the terminal. As output, it gets an alert notification, which the terminal sends to the user.

[1602] Specific operation: The server uses an anomaly detection algorithm to create an alert and notify the user via email or SMS.

[1603] Environmental Health Check System

[1604] Step 1: Sensor installation and data collection

[1605] The terminal installs sensors in water bodies or forests. The installation location and the type of sensor are required as inputs. The sensors collect environmental data such as temperature, humidity, pH, etc. The collected environmental data is obtained as output.

[1606] Specific operation: The sensor measures a specific environmental parameter and sends the data to the device.

[1607] Step 2: Sending data

[1608] The terminal receives collected environmental data as input and transmits it to the server in real time. The transmitted data is obtained as output. MQTT and HTTP are used as communication protocols.

[1609] Specific operation: The device formats the data and sends it using a secure communication protocol.

[1610] Step 3: Data storage and analysis

[1611] The server receives the received environmental data as input and stores it using a database management system (e.g., PostgreSQL). The stored data is then analyzed in real time using an AI algorithm. The analysis results (anomalous environmental data) are obtained as output.

[1612] Specific operation: The server stores the data in a database and performs analysis using an anomaly detection model.

[1613] Step 4: Visualize the analysis results

[1614] The server receives the analysis results and visually displays them using a dashboard. As input, the analysis results (anomalous environmental data) are required. As output, visualized information is obtained in the form of graphs and charts.

[1615] Specific operation: The server uses a visualization tool to generate an interface that displays the analysis results.

[1616] Step 5: Alert Notification

[1617] When the server detects an anomaly, it uses the alert generation system to generate an alert as input and sends it to the terminal. As output, an alert notification is obtained. The terminal then sends the notification to the user.

[1618] Specific operation: The server uses an anomaly detection algorithm to create an alert and notify the user via email or SMS.

[1619] Illegal logging detection system

[1620] Step 1: Drone and ground sensor placement

[1621] The terminal deploys drones and ground sensors in the monitoring area. The deployment location and drone flight plan are required as inputs. The drone monitors the area using a high-resolution camera, and the ground sensors detect changes in the soil and vibrations from heavy machinery. The output is the captured image data and sensor data.

[1622] How it works: The drone flies periodically, taking pictures of the area with its camera, and ground sensors collecting environmental data.

[1623] Step 2: Capturing and Sending Data

[1624] The terminal receives data from the drone and ground sensors as input and transmits it to the server in real time. The transmitted data is obtained as output. A secure communication protocol (e.g., SSL / TLS) is used for transmission.

[1625] What happens: The device formats the data and sends it over a secure network.

[1626] Step 3: Data storage and analysis

[1627] The server receives the received data as input and stores it using a database management system (e.g., MySQL). The stored data is then analyzed using an AI algorithm (e.g., an analytical model using Keras). The analysis results (patterns of illegal logging) are obtained as output.

[1628] Specific operation: The server stores the received data in a database and runs an analytical model to detect anomalies.

[1629] Step 4: Visualize the analysis results

[1630] The server receives the analysis results and displays them visually using a dashboard. The input required is the analysis results (patterns of illegal logging). The output is visualized information in the form of graphs and charts.

[1631] Specific operation: The server uses a visualization tool to generate an interface that displays the analysis results.

[1632] Step 5: Alert Notification

[1633] When the server detects suspected illegal logging, it uses the alert generation system to generate an alert as input and sends it to the terminal. The output is an alert notification, which the terminal then sends to the user.

[1634] Specific operation: The server uses an anomaly detection algorithm to create an alert and notify the user via email or SMS.

[1635] (Application example 1)

[1636] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1637] In modern society, effective wildlife monitoring, environmental health checks, and illegal logging detection are crucial. However, performing these monitoring and detection tasks efficiently and in real time poses technical challenges, particularly in the areas of wide-area monitoring and real-time data notification. Furthermore, existing systems are complex and require specialized knowledge, making them difficult for the general public to use. A system that can solve these problems and maintain a safe and healthy environment is needed.

[1638] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1639] In this invention, the server includes a photographing device, a transmitting device, a cloud server, an analysis device, a display device, a notification device, and a real-time notification device. This enables wide-area monitoring, real-time data analysis, and rapid notification to users. Furthermore, by using a generative AI model, a system is provided that can analyze environmental data and wildlife movements with high accuracy and quickly detect and notify abnormalities.

[1640] "Photography Device Means" refers to videography equipment for capturing wildlife and environmental activity.

[1641] The "transmission device means" is a device for compressing data obtained from the image capture device or sensor device and transmitting it to the cloud server.

[1642] "Cloud Server Means" refers to a remote computing resource for receiving, storing, and analyzing data transmitted from a Transmitter Means.

[1643] The "analysis means" is a means for analyzing data received by the cloud server means using a machine learning algorithm to recognize the type and movements of wildlife and detect illegal logging and environmental abnormalities.

[1644] The "display device means" is a device for visually displaying the results obtained by the analysis means.

[1645] "Notification device means" is a device for issuing a notification to the user in the event of an abnormality or new discovery.

[1646] The "real-time notification means" is a means for notifying the mobile terminal in real time of the data collected by the photographing device means and the transmitting device means.

[1647] A "sensor device means" is a device for collecting environmental data such as temperature, humidity, pH, etc.

[1648] "Unmanned Aerial Vehicle Means" means an unmanned aerial vehicle for periodically monitoring and collecting data from a surveillance area.

[1649] A "generative AI model" is an artificial intelligence model that is trained based on large datasets to perform specific tasks with high accuracy.

[1650] "Visualization means" refers to a means for visually displaying analysis results on the cloud.

[1651] This invention is a system for efficiently monitoring wildlife, checking environmental health, and detecting illegal logging. This system collects data using various imaging and sensor devices, transmits the data to a cloud server for analysis, and provides information to users in real time.

[1652] The server first receives image data obtained from the photographing device means and environmental data obtained from the sensor device means. Specific hardware examples include cameras for the photographing device means and temperature, humidity, and pH sensors for the sensor device means. These data are then transmitted to the cloud server via the transmitting device means. This transmission utilizes an internet connection.

[1653] The cloud server stores the received data and analyzes it using machine learning algorithms. Software such as TensorFlow and PyTorch is used for this analysis. The analysis results are visualized within the cloud server and provided to the user through the display device means. The results are displayed in a dashboard format so that the user can easily check them. In addition, if an abnormality or new discovery is detected, the notification device means issues an alert to the user through the real-time notification means.

[1654] As a practical example, a camera is installed in a forest area and captures the movement of wildlife (e.g., a bear) at night. The captured image data is sent to a cloud server in real time, where an AI algorithm analyzes the image and detects the presence of a bear. The results are displayed on a dashboard, and an alert is sent to the user that a "bear has appeared." This allows the user to respond quickly.

[1655] The system uses a generative AI model to analyze data with high accuracy and quickly detect anomalies. The generative AI model is trained using prompts such as:

[1656] "Please provide us with a dataset to identify animal species. Please provide us with an AI algorithm to develop a system that can monitor animal movements in real time using an environmentally friendly approach."

[1657] According to the embodiment of the present invention, users can efficiently and accurately monitor a wide area, check the environment, and detect illegal logging. This system will greatly contribute to sustainable environmental management and safety management.

[1658] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1659] Step 1:

[1660] A camera captures the movement. The user configures and positions the camera to cover a specific area. When the camera detects movement, such as wildlife or illegal logging, it captures the movement as image data.

[1661] (Input: Wildlife or target movement,raw data; Output: Captured image data)

[1662] Step 2:

[1663] The device compresses the captured image data and sends it to the cloud server. The device compresses the captured raw data for efficient transmission. The compressed data is then sent to the cloud server via Wi-Fi or mobile network.

[1664] (Input: captured image data; Output: compressed image data)

[1665] Step 3:

[1666] The cloud server stores the received image data and analyzes it using machine learning algorithms. The cloud server stores the data in a database and analyzes it using machine learning frameworks such as TensorFlow and PyTorch. The analysis is performed using a generative AI model to identify the type of animal and its movements from the data.

[1667] (Input: Compressed image data; Output: Recognition results of animal species and movements)

[1668] Step 4:

[1669] The server visualizes the analysis results on the cloud and provides them to the user through a display device. The analyzed data is provided to the user in the form of graphs and dashboards, which can be accessed from a web browser or a dedicated application.

[1670] (Input: Recognition results of animal species and movements; Output: Visualized analysis results on a dashboard)

[1671] Step 5:

[1672] If the server detects an anomaly or new discovery, it will issue a real-time notification to the user through a notification device. When the server detects an anomaly based on the analysis results, it will quickly send a notification to the user's smartphone or computer. The notification will be delivered as a text message or in-app notification.

[1673] (Input: Anomaly detection results; Output: Real-time notification)

[1674] Step 6:

[1675] Users receive notifications and access the system dashboard to view detailed data. Users can check alerts from their smartphones or computers, log in to the dashboard, and view footage and data analysis results.

[1676] (Input: Real-time notification; Output: User confirmation of detailed data)

[1677] These processing steps enable the system to efficiently monitor wildlife, check environmental health, and detect illegal logging in real time. The use of generative AI models enables highly accurate data analysis, prompting users to take immediate action.

[1678] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1679] The present invention provides a system that provides more advanced feedback by combining an emotion engine that recognizes user emotions with a system that efficiently monitors wildlife, checks environmental health, and detects illegal logging. Specific embodiments of the present invention are described below.

[1680] Wildlife Monitoring System

[1681] Program processing

[1682] 1. The device installs a camera in a forest area and performs an operation check. When the camera detects movement, it captures image data.

[1683] 2. The device compresses the captured image data and sends it to the server using an encryption protocol.

[1684] 3. The server stores the received data and analyzes it using AI algorithms, using image recognition technology to identify the type and movements of wildlife.

[1685] 4. The server visualizes the analysis results on a dashboard, displaying them in graphs and charts so that users can intuitively understand the results.

[1686] 5. If the server detects an abnormality or new discovery, it will send an alert to the device using a notification method.

[1687] 6. The device issues an alert to the user, and at the same time, the emotion engine analyzes the user's reaction, thereby monitoring the user's emotional state in real time.

[1688] 7. The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user reacts with surprise or joy, that information is collected.

[1689] 8. The server adjusts the content and format of notifications based on the emotional data, personalizing feedback based on the user’s emotional state.

[1690] Specific examples

[1691] A user sets up a camera in a forested area. The camera captures the movements of large animals (e.g., bears) at night, and the device sends the data in real time to a server. The server analyzes the images, identifies the presence of a bear, and displays that information on a dashboard. The server then sends an alert to the user that a "bear has appeared." The device analyzes the user's reaction using an emotion engine, and if the user is surprised, the emotion data is sent to the server, and the next notification will be more gentle.

[1692] Environmental Health Check System

[1693] Program processing

[1694] 1. The device installs sensors in forests and water areas and checks their operation. The sensors collect environmental data (temperature, humidity, pH, etc.).

[1695] 2. The device sends the collected data to the server in real time.

[1696] 3. The server receives and stores the data, then uses AI algorithms to analyze it and detect outliers.

[1697] 4. The server displays the analysis results on a dashboard and issues an alert if an abnormal value is detected.

[1698] 5. If the server detects an abnormality, it sends that information to the terminal and notifies the user.

[1699] 6. At the same time that the device issues an alert, the emotion engine analyzes the user's reaction, monitoring the user's emotional state in real time.

[1700] 7. The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user shows a nervous reaction, that information is collected.

[1701] 8. The server adjusts the content and format of notifications based on the emotional data, personalizing feedback based on the user’s emotional state.

[1702] Specific examples

[1703] The device installs a sensor in the water body and detects a sudden rise in pH level, sending the data to the server in real time. The server analyzes the data and determines that there is a possibility of water pollution. The server then sends an alert to the user stating that there is a possibility of water pollution. The device then analyzes the user's reaction using an emotion engine, and if the user becomes nervous, the emotion data is sent to the server, and the next notification will be more reassuring.

[1704] Illegal logging detection system

[1705] Program processing

[1706] 1. The device places drones and ground sensors in the surveillance area and sets the drone's flight path.

[1707] 2. Drones fly over the monitored area periodically and capture image data, and ground sensors collect data as well.

[1708] 3. The device compresses the captured data and sends it to the server using an encryption protocol.

[1709] 4. The server stores the received data and analyzes it using AI algorithms to detect patterns of illegal logging.

[1710] 5. The server visualizes the analysis results on a dashboard, allowing users to easily and intuitively understand the results.

[1711] 6. If the server detects suspicion of illegal logging, it will send an alert to the terminal using a notification method.

[1712] 7. The device issues an alert to the user, and at the same time, the emotion engine analyzes the user's reaction, monitoring the user's emotional state in real time.

[1713] 8. The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user reacts impatient or angry, that information is collected.

[1714] 9. The server adjusts the content and format of notifications based on the emotional data, personalizing feedback based on the user’s emotional state.

[1715] Specific examples

[1716] A user uses a drone to monitor a wide area of ​​forest. The drone captures abnormal logging activity in a certain area, and the device sends the data in real time to a server. The server analyzes the data and determines that illegal logging is suspected. The server then sends an alert to the user stating that "illegal logging is suspected." The device uses an emotion engine to analyze the user's reaction, and if the user becomes panicked, the emotional data is sent to the server, and the next notification will be delivered with content that allows the user to respond more calmly.

[1717] This process allows for efficient, real-time wildlife monitoring, environmental health checks, and illegal logging detection, while also providing personalized feedback based on the user's emotional state.

[1718] The processing flow will be explained below.

[1719] Combining emotion engines with wildlife monitoring systems

[1720] Program processing

[1721] Step 1:

[1722] The device installs a camera in the forest area. It turns on the camera and checks its operation. After confirming that it is working properly, it starts monitoring mode.

[1723] Step 2:

[1724] The camera detects motion and captures wildlife movements, triggering the generation of continuous image data.

[1725] Step 3:

[1726] The device compresses the captured image data and transmits it to a server in real time using an encryption protocol.

[1727] Step 4:

[1728] The server stores the received data and also performs backup processing to ensure data safety.

[1729] Step 5:

[1730] The server analyzes the stored image data with AI algorithms and uses image recognition technology to identify the type and movement of wildlife, for example by classifying animals based on their shape, color, and size.

[1731] Step 6:

[1732] The server visualizes the analysis results on a dashboard, generating and displaying graphs and charts that are easy for users to understand intuitively.

[1733] Step 7:

[1734] If the server detects any new findings or anomalies, it will use a notification mechanism to send an alert to the device, including details of the analysis results and an image.

[1735] Step 8:

[1736] The device issues an alert to the user. At the same time, the emotion engine analyzes the user's reaction and monitors the user's emotional state in real time after seeing the alert.

[1737] Step 9:

[1738] The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user shows a reaction of surprise or joy, that information is collected.

[1739] Step 10:

[1740] The server adjusts the content and format of notifications based on the emotional data, so the next notification will be personalized based on the user's emotional state.

[1741] Combining an environmental health check system with an emotion engine

[1742] Program processing

[1743] Step 1:

[1744] The device will install environmental sensors in forests and water areas, and check the operation of the sensors to ensure they are working properly.

[1745] Step 2:

[1746] The sensor collects environmental data (temperature, humidity, pH, etc.) at set intervals and stores the data in temporary storage.

[1747] Step 3:

[1748] The data collected by the terminal is sent to the server at regular intervals using an encryption protocol.

[1749] Step 4:

[1750] The server stores the received data, checks its integrity, and simultaneously backs up the data.

[1751] Step 5:

[1752] The server analyzes the received data in real time and uses AI algorithms to detect abnormalities in the environmental data (for example, sudden changes in pH).

[1753] Step 6:

[1754] The server visualizes the analysis results on a dashboard, and if anomalies are detected, details are displayed in graphs and charts.

[1755] Step 7:

[1756] If the server detects an abnormality, it will use a notification method to send an alert to the device, which will include the specific abnormal value and location information.

[1757] Step 8:

[1758] The device issues an alert to the user. At the same time, the emotion engine analyzes the user's reaction and monitors the user's emotional state in real time after seeing the alert.

[1759] Step 9:

[1760] The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user shows a nervous reaction, that information is collected.

[1761] Step 10:

[1762] The server adjusts the content and format of notifications based on the emotional data, so the next notification will be personalized based on the user's emotional state.

[1763] Combining an emotion engine with an illegal logging detection system

[1764] Program processing

[1765] Step 1:

[1766] The terminal places the drone and ground sensors in the monitoring area, sets the drone's flight path, and performs operational checks to confirm normal operation.

[1767] Step 2:

[1768] Drones fly according to a set schedule and capture image data of the monitored area, and ground sensors collect data as well.

[1769] Step 3:

[1770] The device compresses the captured data and sends it to the server using an encryption protocol.

[1771] Step 4:

[1772] The server saves the received data, checks the integrity of the data, and simultaneously backs up the received data.

[1773] Step 5:

[1774] The server analyzes the received data using AI algorithms to detect patterns of illegal logging. For example, if a large amount of timber is being cut all at once, it will detect this change.

[1775] Step 6:

[1776] The server visualizes the analysis results on a dashboard, displaying them in graphs and maps that are easy for users to understand intuitively.

[1777] Step 7:

[1778] If the server detects any suspicion of illegal logging, it will use a notification method to send an alert to the device, which will include specific location information and the results of image analysis.

[1779] Step 8:

[1780] The device issues an alert to the user. At the same time, the emotion engine analyzes the user's reaction and monitors the user's emotional state in real time after seeing the alert.

[1781] Step 9:

[1782] The emotion engine analyzes the user's emotional data and sends the results to the server. For example, if the user shows impatience or anger, that information is collected.

[1783] Step 10:

[1784] The server adjusts the content and format of notifications based on the emotional data, so the next notification will be personalized based on the user's emotional state.

[1785] This process allows for efficient, real-time wildlife monitoring, environmental health checks, and illegal logging detection, while also providing personalized feedback based on the user's emotional state.

[1786] Example 2

[1787] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1788] While existing systems for wildlife monitoring, environmental health checks, and illegal logging detection are efficient at collecting and analyzing data, they lack the ability to personalize feedback based on the user's emotional state. As a result, the amount and quality of information users receive is constant, and they lack the flexibility to adapt to specific situations. Furthermore, when detecting anomalies or new discoveries, they lack the interactive support to help users respond calmly.

[1789] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a photographing means, a device means, a server means, an analysis means, a display means, a notification means, an emotion engine and a means for transmitting the data to the server, and a feedback means. This makes it possible to efficiently collect data, analyze it in real time, and adjust the content and format of notifications based on the user's emotions. By analyzing the user's reactions with the emotion engine and providing personalized feedback according to each situation, a system is realized that can provide the optimal response for each usage scenario.

[1790] "Capture means" refers to a device that captures wildlife and environmental movements and obtains image data.

[1791] "Device means" refers to a device for compressing data obtained from the imaging means or sensor means and transmitting it to a server using an encryption protocol.

[1792] "Server means" refers to a server that receives data transmitted from device means, decrypts and stores it, and further analyzes it.

[1793] "Analysis means" refers to the function of analyzing data received by the server means using an AI algorithm to recognize or detect the type and movement of wildlife, abnormalities in environmental data, and illegal logging.

[1794] "Display means" refers to an interface that visually displays the analysis results and shows them in graphs or charts so that the user can intuitively understand them.

[1795] "Notification means" refers to the function of sending an alert to the user via the terminal in the event of an abnormality or new discovery.

[1796] An "emotion engine" refers to an analysis device that analyzes a user's reaction and sends the results to a server.

[1797] "Feedback means" refers to a function that adjusts the content and format of notifications based on the user's emotional data, thereby personalizing feedback.

[1798] The present invention provides a system for efficiently monitoring wildlife, checking environmental health, and detecting illegal logging by combining it with an emotion engine that recognizes user emotions to provide more advanced feedback. Specific embodiments of the present invention are described below.

[1799] Basic system configuration

[1800] This system is mainly composed of the following means:

[1801] 1. Filming Method

[1802] The photographing means includes a camera device for capturing wildlife movements, such as a highly sensitive infrared camera or a camera with motion detection capabilities. The camera is installed in a designated forest area and captures image data when it detects movement.

[1803] 2. Equipment means

[1804] The device means compresses the image data obtained from the photographing means and the environmental data obtained from the sensor means, protects the data using the AES encryption protocol, and transmits it to the server. Data compression reduces the communication load and enables efficient data transmission.

[1805] 3. Server Means

[1806] The server means receives, decrypts, and stores data sent from the device means. The server is equipped with high-performance storage capable of processing large amounts of data and an AI algorithm (e.g., YOLO or ResNet) for data analysis.

[1807] 4. Analysis method

[1808] The analysis means uses AI algorithms to analyze data received and stored in the server means, and has the ability to recognize the types and movements of wildlife. Environmental data and illegal logging detection are also analyzed in the same way.

[1809] 5. Display means

[1810] The display means is a dashboard interface that visually represents the results of the analysis performed by the server. The dashboard visualizes the results in graphs and charts, allowing users to intuitively understand them.

[1811] 6. Means of notification

[1812] The notification function has the function of sending alerts to users if an abnormality or new discovery is detected based on the analysis results. Notifications are delivered to users via mobile app, email, or SMS.

[1813] 7. Emotion Engine

[1814] The emotion engine has the function of analyzing the user's reactions and sending the data to the server. For example, it analyzes the user's reactions such as surprise, joy, and tension, and collects the emotional data.

[1815] 8. Feedback channels

[1816] The feedback mechanism has a function that allows the server to adjust the content and format of the next notification based on data from the emotion engine, thereby personalizing the feedback, thereby providing the most appropriate information for the user.

[1817] Specific examples

[1818] For example, if a user installs a camera in a forest area and the camera captures the movement of a large animal (e.g., a bear) at night, the image data is sent in real time to a server by a device means. The server analyzes the data, identifies the presence of a bear, and displays the information on a dashboard. Based on the analysis results, the server sends an alert to the user through a notification means that a "bear has appeared." If the device analyzes the user's reaction with an emotion engine, and the user is surprised, the emotion data is sent to the server, and the next notification will be provided in a gentler tone.

[1819] Prompt Sentence Examples

[1820] "Create a program that describes a system in which a user-installed camera detects movement in a forested area and sends the image data to a server. The server then analyzes the data to identify the type of wildlife and notify the user."

[1821] As can be seen, the present invention can enhance wildlife monitoring, environmental health checks, illegal logging detection, and provide personalized feedback based on the user's emotional state.

[1822] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1823] Step 1:

[1824] The user installs the imaging device (camera) in a forest area and checks its operation. The user fixes the camera in an area specified by the user and checks that the motion detection function is working properly. The input for the imaging device includes the setting parameters for detecting motion and camera position information. The output is image data generated when motion is detected.

[1825] Step 2:

[1826] Image data is captured when the imaging means detects motion. For example, a camera may trigger a shutter in response to wildlife movement. The input is the detection result of a motion detection sensor, and the output is the captured image data.

[1827] Step 3:

[1828] The device compresses the image data obtained from the image capture means and sends it to the server using an encryption protocol. Specifically, the image data is compressed into a format such as JPEG and encrypted using an encryption method such as AES256. The input is the captured image data, and the output is compressed and encrypted data.

[1829] Step 4:

[1830] The server receives the data sent from the terminal, decrypts it, and stores it. Specifically, the server receives the encrypted data and decrypts it using an AES256 key. It then stores it in a database. The input is encrypted image data, and the output is the original decrypted image data, which is then stored in the database.

[1831] Step 5:

[1832] The server then analyzes the decoded data using AI algorithms. For example, image recognition techniques such as YOLO or ResNet are used to identify the wildlife species and their movements. The input is the decoded image data, and the output is information about the wildlife species and their movements.

[1833] Step 6:

[1834] The server displays the analysis results on a dashboard. It converts the data into graphs and charts and visualizes them so that users can easily understand them. The input is the analysis result data, and the output is visualized graphs and charts displayed on the dashboard screen.

[1835] Step 7:

[1836] If the server detects an abnormality or a new discovery, it will send an alert to the device using a notification method. Specifically, it will send an alert message using email, SMS, etc. The input is the analysis result, and the output is an alert message that is generated and sent to the device.

[1837] Step 8:

[1838] The device issues an alert to the user and analyzes the user's reaction using an emotion engine. For example, the alert can be displayed through a mobile app or a dedicated device, and the emotion engine can analyze the user's facial expression and tone of voice. The input is an alert message from the server, and the output is the user's emotional data.

[1839] Step 9:

[1840] The emotion engine analyzes the user's emotion data and sends it to the server. Specifically, the user's facial expressions and tone of voice are sent to the server as numerical data. The input is the user's emotion data, and the output is the emotion data sent to the server.

[1841] Step 10:

[1842] The server adjusts the content and format of notifications based on the emotion data. For example, if the user is surprised, the next notification will be sent in a softer tone. The input is the emotion data sent by the emotion engine, and the output is the adjusted content and format of the notification.

[1843] Through these steps, the wildlife monitoring system can efficiently collect and analyze data and provide personalized feedback based on the user's emotional state.

[1844] (Application example 2)

[1845] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1846] While existing wildlife monitoring systems, environmental health check systems, and illegal logging detection systems are capable of advanced monitoring and data analysis, they lack the functionality to provide feedback based on the user's emotions, resulting in a limited user experience. In particular, if alert notifications cause excessive tension or anxiety in the user, this can hinder subsequent response.

[1847] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes emotion engine means for analyzing the user's emotion data, feedback means for transmitting the emotion data analyzed by the emotion engine means to the server and for personalizing the notification content, and notification means for issuing a notification to the user when an abnormality or new discovery is detected. This enables the alert notification to be personalized according to the user's emotional state, helping the user to respond appropriately and quickly.

[1848] "Wildlife" means any organism other than humans that inhabits the natural environment, especially animals.

[1849] "Camera means" refers to a device used to photograph wildlife and the environment and obtain image data thereof.

[1850] "Terminal means" refers to a device that compresses and transmits data obtained from camera means or sensor means.

[1851] "Server means" refers to a device or group of devices that receives and stores data sent from terminal means and performs the necessary processing.

[1852] "Analysis means" refers to a device or group of devices that analyzes data received by the server means using an AI algorithm and extracts and recognizes specific information.

[1853] The "dashboard means" is a device or interface for visually displaying the analysis results obtained by the analysis means.

[1854] "Notification means" refers to a device or function that issues an appropriate notification to the user in the event of an abnormality or new discovery.

[1855] The "emotion engine means" is a device or algorithm that analyzes the user's emotional data and personalizes the notification content based on that data.

[1856] The "feedback means" is a device or function for providing the user with optimal notification content using emotion data analyzed by the emotion engine means.

[1857] "Sensor means" refers to a device for collecting environmental data such as temperature, humidity, pH value, etc.

[1858] A "drone vehicle" is a flying device that periodically flies over a surveillance area from the air and captures image data.

[1859] This invention relates to an advanced environmental monitoring system for analyzing user emotions and optimizing the feedback. Specifically, by integrating an emotion engine into systems for wildlife monitoring, environmental health checks, and illegal logging detection, the system provides feedback according to the user's emotions.

[1860] 1. Wildlife Monitoring System

[1861] Hardware Configuration

[1862] In this system, multiple cameras are used to detect wildlife movement. When a camera detects movement, it captures image data and transmits it to a terminal device. The terminal device compresses and encrypts the data and transmits it to a server device.

[1863] Software Configuration

[1864] The server stores the received data and analyzes it using AI algorithms. The AI ​​uses image recognition technology to identify wildlife species and movements. The results are displayed visually on a dashboard in an intuitive format for users.

[1865] If an abnormality or new discovery is detected, the notification means issues a notification to the user. At the same time, the emotion engine analyzes the user's reaction and sends the emotional data to the server. Based on this data, the server personalizes the notification content and adjusts the next feedback.

[1866] Specific examples

[1867] A user sets up a camera in a forested area. The camera captures bear movements at night and sends the image data to a server. The server identifies the bear's presence and displays it on a dashboard. When an alert is sent to the user that a bear has appeared, the emotion engine analyzes the user's reaction to surprise. As a result, the next notification will be presented in a more toned-down format.

[1868] 2. Environmental Health Check System

[1869] Hardware Configuration

[1870] In this system, multiple sensors are used to collect environmental data such as temperature, humidity, pH, etc. The sensor means transmits the collected data in real time to the terminal means, and the terminal means transmits the data to the server means.

[1871] Software Configuration

[1872] The server means stores the received data and analyzes it using an AI algorithm. If an abnormality is detected, the notification means issues a notification to the user. The emotion engine analyzes the user's reaction and sends the emotion data to the server. The server uses this data to personalize the notification content.

[1873] Specific examples

[1874] A sensor installed by the user in a body of water detects a sudden rise in pH and sends the data to a server in real time. The server analyzes the data and determines that there is a possibility of water pollution. An alert is sent to the user stating "Possible water pollution" and an emotion engine analyzes the user's tense reaction. As a result, the next notification is presented in a reassuring format.

[1875] 3. Illegal Logging Detection System

[1876] Hardware Configuration

[1877] In this system, a drone is used to monitor a monitoring area from the air. Sensor means is also used to collect data on the ground. Data obtained from the drone and sensor means is transmitted to server means via terminal means.

[1878] Software Configuration

[1879] The server means stores the received data and uses an AI algorithm to detect illegal logging. If an abnormality is detected, the notification means issues a notification to the user. The emotion engine analyzes the user's reaction and sends the emotional data to the server. The server uses this data to personalize the notification content.

[1880] Specific examples

[1881] A user uses a drone to monitor a wide area of ​​forest. The drone captures abnormal logging activity in a certain area and sends the data to a server in real time. The server analyzes the data and detects possible illegal logging. An alert is sent to the user stating "Possible illegal logging" and an emotion engine analyzes the user's panicked reaction. As a result, the next notification is presented in a format that encourages a calm response.

[1882] Prompt Sentence Examples

[1883] "How can I combine sentiment analysis with notifications from my home security system to provide personalized feedback based on the user's response?"

[1884] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1885] Program processing flow

[1886] Step 1: The camera detects motion and captures an image

[1887] Input: The camera detects motion within the monitored area.

[1888] Data processing: Capture images using movement as a trigger.

[1889] Output: The captured image data is sent to the terminal means.

[1890] Specific operation: When the camera means detects movement, it automatically releases the shutter and saves the image at that time in memory.

[1891] Step 2: The device compresses the image data and sends it to the server

[1892] Input: Image data captured from a camera means.

[1893] Data processing: Compression and encryption of image data.

[1894] Output: Send the compressed image data to the server means.

[1895] Specific operation: The terminal means compresses the image data from the camera to reduce its size, and transmits the data to the server means using an encryption protocol.

[1896] Step 3: The server stores the received data and analyzes it with AI

[1897] Input: Compressed and encrypted image data sent from the terminal means.

[1898] Data processing: decryption and data storage.

[1899] Output: Extract the analysis results and send them to a dashboard.

[1900] What it does: The server decrypts the compressed data and stores it in a database. It uses AI algorithms to perform image recognition and identify wildlife species and movements.

[1901] Step 4: Display the analysis results on the dashboard

[1902] Input: Recognition results from AI analysis.

[1903] Data processing: Converting data into a visual format.

[1904] Output: Displayed on the dashboard.

[1905] Specific operation: The server means converts the analysis results into graphs or charts and displays them on the user interface.

[1906] Step 5: Issue a user notification when an error occurs

[1907] Input: Data whose analysis results indicate an anomaly.

[1908] Data processing: Generation of notification content.

[1909] Output: Sends a notification to the user.

[1910] Specific operation: If the notification means detects an abnormality, it will issue an alert to the user based on pre-defined rules.

[1911] Step 6: Analyze user reactions with the emotion engine

[1912] Input: Real-time user responses (e.g., data from a smartphone camera or sensors).

[1913] Data processing: Data analysis using sentiment analysis algorithms.

[1914] Output: Generate emotion data and send it to the server.

[1915] Specific operation: The emotion engine means performs facial and voice recognition on the user to identify emotions such as surprise or anxiety.

[1916] Step 7: The server analyzes the emotion data and adjusts the notification content.

[1917] Input: Emotion data sent from the emotion engine means.

[1918] Data processing: Feedback adjustment based on emotion data.

[1919] Output: Adjustment result for next notification content.

[1920] Specific operation: The server analyzes the emotional data and adjusts the next notification content, such as softening it, depending on the user's emotional state.

[1921] Step 8: Sending feedback to the server

[1922] Input: Reconciliation result by server means.

[1923] Data processing: generating the final feedback content.

[1924] Output: The revised notification is sent to the user.

[1925] Specific operation: The server means generates tailored notification content and sends feedback that gives the user peace of mind.

[1926] 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.

[1927] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 ...

Claims

1. 1. A system for wildlife monitoring, comprising: camera means for capturing wildlife movement; a terminal means for compressing image data obtained from the camera means and transmitting the compressed image data to a server; a server means for receiving and storing image data transmitted from the terminal means; an analysis means for analyzing the data received by the server means using an AI algorithm to recognize the type and movement of wildlife; a dashboard means for visually displaying the analysis results; The system includes a notification mechanism that issues notifications to users in the event of anomalies or new findings.

2. 1. A system for checking environmental health, comprising: a sensor means for collecting environmental data such as temperature, humidity, pH, etc.; a terminal means for transmitting data collected from the sensor means to a server; a server means for receiving data transmitted from the terminal means and storing the data in a database; analysis means for analyzing the data analyzed by the server means in real time and detecting anomalies; The system includes a notification means for issuing a notification to a user when an abnormality is detected.

3. A system for detecting illegal logging, comprising: a drone means for monitoring the surveillance area; a sensor means for collecting ground data; a terminal means for transmitting data obtained from the drone means and the sensor means to a server; a server means for receiving and storing data transmitted from the terminal means; an analysis means for analyzing the data received by the server means using an AI algorithm to detect illegal logging; The system includes a notification means for issuing a notification to a user when illegal logging is suspected.

Images