System

Abstract

Embodiments of the system are directed to integrating environmental data collection and analysis, wild animal monitoring, providing environmental education, and providing benefits.SOLUTION: A system includes an environmental data collection part, a data analysis part, an animal monitoring part, an education providing part, and a privilege providing part. The environmental data collection unit collects environmental data. The data analysis unit analyzes the environmental data collected by the environmental data collection unit. The animal monitoring part monitors the movement of the wild animal based on the data analyzed by the data analysis part. The education providing unit provides an environmental education program based on the data monitored by the animal monitoring unit. The privilege providing unit provides a privilege or a reward based on the program provided by the education providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Existing technologies do not integrate environmental data collection and analysis, wildlife monitoring, and the provision of environmental education and rewards, leaving room for improvement.

[0005] The system according to the embodiment aims to integrate environmental data collection and analysis, wildlife monitoring, environmental education provision and reward provision. [Means for solving the problem]

[0006] The system according to the embodiment includes an environmental data collection unit, a data analysis unit, an animal monitoring unit, an education provision unit, and a reward provision unit. The environmental data collection unit collects environmental data. The data analysis unit analyzes the environmental data collected by the environmental data collection unit. The animal monitoring unit monitors the movements of wild animals based on the data analyzed by the data analysis unit. The education provision unit provides an environmental education program based on the data monitored by the animal monitoring unit. The reward provision unit provides a reward or reward based on the program provided by the education provision unit. [Effects of the Invention]

[0007] An embodiment of the system can integrate environmental data collection and analysis, wildlife monitoring, providing environmental education and offering rewards. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An environmental monitoring system according to an embodiment of the present invention collects environmental data in real time, analyzes it using a generation AI, determines the risk of environmental pollution, monitors the movements of wild animals, provides environmental education programs, and provides rewards and benefits. This allows the environmental monitoring system to perform a comprehensive process from collecting and analyzing environmental data, to monitoring wild animals, providing educational programs, and providing rewards and benefits.

[0029] An environmental monitoring system according to an embodiment includes an environmental data collection unit, a data analysis unit, an animal monitoring unit, an education provision unit, and a reward provision unit. The environmental data collection unit collects environmental data. For example, sensors may be installed to measure the concentration of harmful substances in the air or changes in water quality. The environmental data collection unit may also collect data in real time using IoT technology. For example, the data collected by the sensors may be transmitted to the cloud for real-time monitoring. The data analysis unit uses a generation AI to analyze the environmental data collected by the environmental data collection unit. For example, the generation AI may analyze the data using a text generation AI (e.g., LLM) to determine the risk of environmental pollution. The generation AI may also detect anomalies in the data using a multimodal generation AI. The generation AI may also analyze data using statistical analysis or machine learning algorithms. For example, the generation AI may learn from large amounts of data and have advanced analytical capabilities. The animal monitoring unit monitors the movements of wild animals based on the data analyzed by the data analysis unit. For example, surveillance cameras may be installed to monitor wild animal habitats and drones may be used to track their movements over a wide area. The animal monitoring unit can also use AI to automatically identify animal species and behavioral patterns. For example, it can analyze surveillance camera footage and recognize specific animals. The education provider provides environmental education programs based on the data monitored by the animal monitoring unit. For example, AI can collect and analyze information on environmental protection and create educational programs based on that information. The education provider can also incorporate virtual reality (VR) technology to provide an immersive learning experience. For example, it can provide experiential learning in a virtual natural environment. The reward provider can provide rewards and rewards based on the programs provided by the education provider. For example, it can award points to people who participate in environmental protection activities and allow them to exchange those points for rewards and rewards. The reward provider can also use blockchain technology to build a transparent reward system. For example, it can issue tokens for each activity and award rewards. As a result, the environmental monitoring system according to the embodiment can perform a comprehensive process from collecting and analyzing environmental data, monitoring wildlife, providing educational programs, and providing rewards and rewards.For example, the output unit can display the grading results to students and teachers via a web or mobile application, print the results using a printer if students or teachers prefer paper feedback, or send the results via email, providing quick feedback by sending the results directly to students and parents.

[0030] The environmental data collection unit can detect minute environmental changes using nanosensor technology. The environmental data collection unit can, for example, use nanosensors to detect minute harmful substances in the air. For example, it can measure fine particles such as PM2.5 and PM1.0 in real time and send the data to the cloud. The environmental data collection unit can also use nanosensor technology to detect minute changes in water quality. For example, it can measure the concentration of harmful substances in water in real time and send the data to the cloud. The environmental data collection unit can also use nanosensor technology to detect minute changes in soil. For example, it can measure the concentration of harmful substances in soil in real time and send the data to the cloud. In this way, nanosensor technology can be used to detect minute environmental changes with high accuracy.

[0031] The environmental data collection unit can have a self-healing function, enabling long-term data collection. For example, the environmental data collection unit adds a self-healing function to a sensor to automatically repair physical damage or deterioration. For example, nanomaterials are used to self-heal the surface of the sensor. The environmental data collection unit can also use a sensor with a self-healing function to perform long-term data collection. For example, a self-healing polymer is used to protect the surface of the sensor, enabling long-term data collection. The environmental data collection unit can also use a sensor with a self-healing function to perform data collection in harsh environments. For example, a self-healing coating is used to protect the surface of the sensor, enabling data collection in harsh environments. As a result, the self-healing function enables the sensor to collect data for a long period of time.

[0032] The environmental data collection unit can collect data from both urban areas and natural environments and perform comparative analysis. The environmental data collection unit, for example, collects data from sensors installed in urban areas and sensors installed in natural environments and performs comparative analysis. For example, it compares the concentrations of harmful substances in the air. The environmental data collection unit can also collect water quality data from both urban areas and natural environments and perform comparative analysis. For example, it compares the concentrations of harmful substances in water. The environmental data collection unit can also collect soil data from both urban areas and natural environments and perform comparative analysis. For example, it compares the concentrations of harmful substances in the soil. In this way, by comparing and analyzing data from urban areas and natural environments, it is possible to clearly understand the differences between the environments.

[0033] The environmental data collection unit can collect data using an autonomous vehicle equipped with a mobile sensor. For example, the environmental data collection unit can mount an environmental sensor on an autonomous vehicle and collect data by patrolling urban areas and natural environments. For example, it can measure harmful substances in the air, temperature, and humidity. The environmental data collection unit can also collect data using a drone equipped with a mobile sensor. For example, it can collect data by patrolling a wide area of ​​forest. The environmental data collection unit can also collect data using a robot equipped with a mobile sensor. For example, it can collect environmental data inside a factory. This makes it possible to collect data over a wide area by using an autonomous vehicle.

[0034] The data analysis unit can add an anomaly detection algorithm and instantly detect sudden environmental changes. For example, the data analysis unit can introduce an anomaly detection algorithm into the AI ​​to instantly detect a sudden increase in harmful substances in the air. For example, it can detect pollution caused by factory exhaust gases. The data analysis unit can also use an anomaly detection algorithm to instantly detect sudden changes in water quality. For example, it can detect water pollution caused by factory wastewater. The data analysis unit can also use an anomaly detection algorithm to instantly detect sudden changes in soil. For example, it can detect soil pollution caused by chemical leaks. In this way, the anomaly detection algorithm can instantly detect sudden environmental changes.

[0035] The data analysis unit can introduce time series analysis to perform comparative analysis with past data. For example, the data analysis unit can introduce time series analysis into AI to compare past air quality data with current data. For example, it can analyze seasonal variations in air pollution. The data analysis unit can also use time series analysis to perform comparative analysis of past and current water quality data. For example, it can analyze variations in water quality throughout the year. The data analysis unit can also use time series analysis to perform comparative analysis of past and current soil data. For example, it can analyze variations in soil pollution over a long period of time. This allows past data to be compared and analyzed with current data using time series analysis.

[0036] The data analysis unit can share the results of the environmental data analysis with other cities and regions to make wide-area environmental risk assessments. The data analysis unit, for example, uploads the results of the environmental data analysis to the cloud and shares them with other cities and regions. For example, it compares air pollution data from multiple cities. The data analysis unit can also collect data from other cities and regions to make integrated risk assessments in order to make wide-area environmental risk assessments. For example, it can compare the impact of air pollution between regions. The data analysis unit can also share data with other cities and regions to make joint environmental risk assessments. For example, multiple cities can cooperate to take environmental protection measures. This makes it possible to make wide-area environmental risk assessments.

[0037] The data analysis unit can develop an interactive dashboard for visually displaying the results of analysis by AI. The data analysis unit can develop, for example, an interactive dashboard for visually displaying the results of analysis by AI. For example, the data can be displayed using graphs and charts. The data analysis unit can also develop a dashboard with a real-time data display function. For example, the data from sensors can be displayed in real time. The data analysis unit can also develop a dashboard with a user interaction function. For example, the dashboard can allow users to manipulate data to obtain detailed information. This allows users to intuitively understand the data by visually displaying it.

[0038] The animal monitoring unit can mount an infrared camera on the drone and track the movements of wild animals at night. The animal monitoring unit, for example, mounts an infrared camera on the drone and tracks the movements of wild animals at night. For example, it monitors the behavior patterns of nocturnal animals. The animal monitoring unit can also use the infrared camera to monitor the habitats of wild animals at night. For example, it installs an infrared camera to monitor the movements of animals at night. The animal monitoring unit can also use the infrared camera to analyze the behavior patterns of wild animals at night. For example, it analyzes images from the infrared camera to identify the behavior of the animals. This makes it possible to track the movements of wild animals at night with high accuracy.

[0039] The animal monitoring unit incorporates AI into the surveillance camera, allowing it to automatically identify the type of animal and its behavioral patterns. For example, the animal monitoring unit incorporates AI into the surveillance camera to automatically identify the type of animal. For example, it recognizes specific animals from camera footage. The animal monitoring unit can also use AI to automatically identify the behavioral patterns of animals. For example, it analyzes camera footage to identify the behavior of animals. The animal monitoring unit can also use AI to comprehensively analyze the type of animal and its behavioral patterns. For example, it can simultaneously identify the type of animal and its behavior based on camera footage. In this way, the use of AI makes it possible to automatically identify the type of animal and its behavioral patterns.

[0040] The animal monitoring department can use drones to monitor a wide range of ecosystems and assess the impact of environmental changes. For example, the animal monitoring department can use drones to monitor a wide range of forests and assess the impact of environmental changes. For example, the impact of deforestation can be analyzed. The animal monitoring department can also use drones to monitor a wide range of marine ecosystems. For example, the impact of marine pollution can be analyzed. The animal monitoring department can also use drones to monitor a wide range of wetland ecosystems. For example, the impact of drying out wetlands can be analyzed. This makes it possible to monitor a wide range of ecosystems and assess the impact of environmental changes.

[0041] The animal monitoring department can upload surveillance camera data to the cloud in real time, allowing multiple experts to analyze it simultaneously. For example, the animal monitoring department can build a system that uploads surveillance camera video data to the cloud in real time and allows multiple experts to analyze it simultaneously. For example, analyzing the behavioral patterns of wild animals. The animal monitoring department can also share data on the cloud, allowing experts to conduct joint analysis. For example, multiple experts can analyze data simultaneously and develop strategies for conservation activities. The animal monitoring department can also store data on the cloud, allowing them to analyze it later. For example, analyzing long-term animal behavior patterns based on past data. This allows multiple experts to analyze data simultaneously.

[0042] The education provision unit can use AI to provide customized educational programs according to the learning progress of each individual user. The education provision unit, for example, uses AI to analyze the user's learning progress in real time and provide a customized educational program. For example, the learning content can be individually adjusted. The education provision unit can also use AI to propose an optimal learning plan based on the user's learning history. For example, it can analyze past learning data and suggest what content should be learned next. The education provision unit can also use AI to provide educational programs according to the user's learning style. For example, a program that makes heavy use of visual content can be provided to a user who prefers visual learning. This makes it possible to provide a customized educational program according to the user's learning progress.

[0043] The education provider can introduce virtual reality (VR) technology into environmental education programs to provide a more immersive learning experience. For example, the education provider can introduce VR technology into environmental education programs to allow users to learn in a virtual environment. For example, the education provider can provide experiential learning in a virtual natural environment. The education provider can also use VR technology to provide a program that allows users to actually experience environmental protection activities. For example, the user can experience tree planting in a virtual forest. The education provider can also use VR technology to provide a program that allows users to experience environmental problem sites. For example, the user can experience a virtual marine pollution site. In this way, the use of virtual reality technology can provide a more immersive learning experience.

[0044] The education provider can provide environmental education programs in multiple versions for different age groups and levels of expertise. For example, the education provider can provide environmental education programs for different age groups, such as for children, adults, and seniors. For example, simple language and illustrations are used extensively for children. The education provider can also provide environmental education programs for different levels of expertise, such as for beginners, intermediate learners, and advanced learners. For example, basic content is provided for beginners, applied content is provided for intermediate learners, and specialized content is provided for advanced learners. The education provider can also customize environmental education programs for different age groups and levels of expertise. For example, a program for children is provided that includes specialized content. This makes it possible to provide educational programs for different age groups and levels of expertise.

[0045] The education providing unit can gamify the content of an educational program to attract users' interest. For example, the education providing unit gamifies the content of an environmental education program, allowing users to learn while having fun. For example, it can provide a quiz-style game for learning about environmental issues. The education providing unit can also introduce a point system, allowing users to earn points as they progress in their studies. For example, points can be awarded each time a learning content is completed. The education providing unit can also introduce a level-up system, allowing users to level up as they progress in their studies. For example, each time a learning content is completed, the level increases and new content is unlocked. In this way, gamification can attract users' interest.

[0046] The reward provider can use blockchain technology to build a transparent reward system for users who participate in environmental protection activities. The reward provider, for example, builds a system using blockchain technology to grant rewards to users who participate in environmental protection activities. For example, a token is issued for each activity. The reward provider can also use blockchain technology to ensure the transparency of rewards. For example, the reward history can be recorded on the blockchain so that anyone can check it. The reward provider can also use blockchain technology to prevent fraudulent rewards. For example, the issuance and exchange of rewards can be managed on the blockchain. In this way, a transparent reward system can be built using blockchain technology.

[0047] The reward provision unit can customize the content of rewards and rewards according to the user's interests and concerns. For example, the reward provision unit builds a system that customizes the content of rewards and rewards according to the user's interests and concerns. For example, eco-friendly goods are provided to a user who is interested in environmental protection. The reward provision unit can also customize the content of rewards and rewards based on the user's past activity history. For example, rewards are provided according to activities participated in in the past. The reward provision unit can also customize the content of rewards and rewards based on user feedback. For example, rewards are selected based on the user's evaluation. This makes it possible to provide rewards and rewards according to the user's interests and concerns.

[0048] The reward provider can make it possible to exchange points earned by participating in environmental protection activities for other eco-friendly services or products. For example, the reward provider can build a system in which points earned by participating in environmental protection activities can be exchanged for eco-friendly products. For example, the points can be exchanged for recycled products or eco-bags. The reward provider can also make it possible to exchange points for eco-friendly services. For example, the points can be exchanged for renewable energy services. The reward provider can also make it possible to exchange points with other users. For example, the points can be transferred or shared. In this way, the points earned by participating in environmental protection activities can be exchanged for eco-friendly services or products.

[0049] The reward provider can provide a community platform where participants in environmental protection activities can interact with each other. The reward provider can, for example, provide an online community platform where participants in environmental protection activities can interact with each other. For example, by introducing a forum or chat function. The reward provider can also provide a platform where participants can share information with each other. For example, by sharing the results and experiences of activities. The reward provider can also provide a platform where participants can cooperate with each other on activities. For example, by working together on a project. In this way, by providing a community platform where participants can interact with each other, collaboration in activities is strengthened.

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

[0051] The Environmental Data Collection Department not only collects environmental data, but can also propose specific action plans for environmental improvement based on the collected data. For example, it can propose tree planting activities in areas with high concentrations of harmful substances in the air. Also, if changes in water quality are detected, it can recommend that local residents install filters to improve water quality. Furthermore, it can suggest the use of soil conditioners in areas with high concentrations of harmful substances in the soil. In this way, the Environmental Data Collection Department can not only collect data, but also propose specific environmental improvement actions.

[0052] The data analysis unit can create an environmental risk map for each region based on the results of the analysis of environmental data. For example, areas with a high risk of air pollution can be displayed in red, and areas with a low risk in green. It can also create a risk map of water pollution and color-code it according to the level of pollution. It can also create a risk map of soil pollution and color-code it according to the level of pollution. This allows users to visually grasp the environmental risk for each region.

[0053] The Animal Monitoring Department can propose locations for animal sanctuaries based on wild animal behavior data. For example, it can analyze the routes that specific animals frequently travel and propose the establishment of sanctuaries along those routes. It can also propose the expansion of sanctuaries in areas where animal habitats are declining. Furthermore, if animal breeding grounds are identified, it can propose designating those areas as sanctuaries. This allows the Animal Monitoring Department to propose effective establishment of animal sanctuaries based on data.

[0054] The data analysis unit can add an anomaly detection algorithm to instantly detect sudden environmental changes. For example, by introducing an anomaly detection algorithm into the AI, it can instantly detect a sudden increase in harmful substances in the air. For example, it can detect pollution caused by factory exhaust gases. The data analysis unit can also use an anomaly detection algorithm to instantly detect sudden changes in water quality. For example, it can detect water pollution caused by factory wastewater. The data analysis unit can also use an anomaly detection algorithm to instantly detect sudden changes in soil. For example, it can detect soil pollution caused by chemical leaks. In this way, the anomaly detection algorithm can instantly detect sudden environmental changes.

[0055] The data analysis unit can introduce time series analysis to perform comparative analysis with past data. For example, time series analysis can be introduced into AI to compare past air quality data with current data. For example, this can be used to analyze seasonal variations in air pollution. The data analysis unit can also use time series analysis to perform comparative analysis of past and current water quality data. For example, this can be used to analyze variations in water quality throughout the year. The data analysis unit can also use time series analysis to perform comparative analysis of past and current soil data. For example, this can be used to analyze variations in soil pollution over a long period of time. This allows for comparative analysis of past and current data using time series analysis.

[0056] The reward provider can make it possible to exchange points earned by participating in environmental protection activities for other eco-friendly services or products. For example, the reward provider can build a system in which points earned by participating in environmental protection activities can be exchanged for eco-friendly products. For example, the points can be exchanged for recycled products or eco-bags. The reward provider can also make it possible to exchange points for eco-friendly services. For example, the points can be exchanged for renewable energy services. The reward provider can also make it possible to exchange points with other users. For example, the points can be transferred or shared. In this way, the points earned by participating in environmental protection activities can be exchanged for eco-friendly services or products.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The environmental data collection unit collects environmental data. For example, sensors may be installed to measure the concentration of harmful substances in the air or changes in water quality. The environmental data collection unit can also collect data in real time using IoT technology. For example, the data collected by the sensors can be sent to the cloud for real-time monitoring. Step 2: In the data analysis unit, the generation AI analyzes the environmental data collected by the environmental data collection unit. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the data and determine the risk of environmental pollution. The generation AI may also use a multimodal generation AI to detect anomalies in the data. The generation AI may also analyze the data using statistical analysis and machine learning algorithms. For example, the generation AI has learned from a large amount of data and has advanced analytical capabilities. Step 3: The Animal Monitoring Department monitors the movements of wild animals based on the data analyzed by the Data Analysis Department. For example, it installs surveillance cameras to monitor wild animal habitats and flies drones to track their movements over a wide area. The Animal Monitoring Department can also use AI to automatically identify animal species and behavioral patterns. For example, it can analyze surveillance camera footage and recognize specific animals. Step 4: The Education Department provides environmental education programs based on the data monitored by the Animal Monitoring Department. For example, AI can collect and analyze information on environmental protection and create educational programs based on that information. The Education Department can also introduce virtual reality (VR) technology to provide an immersive learning experience. For example, it can provide experiential learning in a virtual natural environment. Step 5: The rewards provider provides rewards and rewards based on the program provided by the education provider. For example, points may be awarded to people who participate in environmental protection activities, and those points may be exchanged for rewards and rewards. The rewards provider may also use blockchain technology to build a transparent reward system. For example, tokens may be issued for each activity and rewards may be awarded.

[0059] (Example 2) An environmental monitoring system according to an embodiment of the present invention collects environmental data in real time, analyzes it using a generation AI, determines the risk of environmental pollution, monitors the movements of wild animals, provides environmental education programs, and provides rewards and benefits. This allows the environmental monitoring system to perform a comprehensive process from collecting and analyzing environmental data, to monitoring wild animals, providing educational programs, and providing rewards and benefits.

[0060] An environmental monitoring system according to an embodiment includes an environmental data collection unit, a data analysis unit, an animal monitoring unit, an education provision unit, and a reward provision unit. The environmental data collection unit collects environmental data. For example, sensors may be installed to measure the concentration of harmful substances in the air or changes in water quality. The environmental data collection unit may also collect data in real time using IoT technology. For example, the data collected by the sensors may be transmitted to the cloud for real-time monitoring. The data analysis unit uses a generation AI to analyze the environmental data collected by the environmental data collection unit. For example, the generation AI may analyze the data using a text generation AI (e.g., LLM) to determine the risk of environmental pollution. The generation AI may also detect anomalies in the data using a multimodal generation AI. The generation AI may also analyze data using statistical analysis or machine learning algorithms. For example, the generation AI may learn from large amounts of data and have advanced analytical capabilities. The animal monitoring unit monitors the movements of wild animals based on the data analyzed by the data analysis unit. For example, surveillance cameras may be installed to monitor wild animal habitats and drones may be used to track their movements over a wide area. The animal monitoring unit can also use AI to automatically identify animal species and behavioral patterns. For example, it can analyze surveillance camera footage and recognize specific animals. The education provider provides environmental education programs based on the data monitored by the animal monitoring unit. For example, AI can collect and analyze information on environmental protection and create educational programs based on that information. The education provider can also incorporate virtual reality (VR) technology to provide an immersive learning experience. For example, it can provide experiential learning in a virtual natural environment. The reward provider can provide rewards and rewards based on the programs provided by the education provider. For example, it can award points to people who participate in environmental protection activities and allow them to exchange those points for rewards and rewards. The reward provider can also use blockchain technology to build a transparent reward system. For example, it can issue tokens for each activity and award rewards. As a result, the environmental monitoring system according to the embodiment can perform a comprehensive process from collecting and analyzing environmental data, monitoring wildlife, providing educational programs, and providing rewards and rewards.For example, the output unit can display the grading results to students and teachers via a web or mobile application, print the results using a printer if students or teachers prefer paper feedback, or send the results via email, providing quick feedback by sending the results directly to students and parents.

[0061] The environmental data collection unit can detect minute environmental changes using nanosensor technology. The environmental data collection unit can, for example, use nanosensors to detect minute harmful substances in the air. For example, it can measure fine particles such as PM2.5 and PM1.0 in real time and send the data to the cloud. The environmental data collection unit can also use nanosensor technology to detect minute changes in water quality. For example, it can measure the concentration of harmful substances in water in real time and send the data to the cloud. The environmental data collection unit can also use nanosensor technology to detect minute changes in soil. For example, it can measure the concentration of harmful substances in soil in real time and send the data to the cloud. In this way, nanosensor technology can be used to detect minute environmental changes with high accuracy.

[0062] The environmental data collection unit can have a self-healing function, enabling long-term data collection. For example, the environmental data collection unit adds a self-healing function to a sensor to automatically repair physical damage or deterioration. For example, nanomaterials are used to self-heal the surface of the sensor. The environmental data collection unit can also use a sensor with a self-healing function to perform long-term data collection. For example, a self-healing polymer is used to protect the surface of the sensor, enabling long-term data collection. The environmental data collection unit can also use a sensor with a self-healing function to perform data collection in harsh environments. For example, a self-healing coating is used to protect the surface of the sensor, enabling data collection in harsh environments. As a result, the self-healing function enables the sensor to collect data for a long period of time.

[0063] The environmental data collection unit can use the emotion estimation function to analyze the emotion of the user when checking the environmental data and optimize the data display method. For example, the environmental data collection unit uses a camera to analyze the user's facial expression when checking the environmental data and estimate the emotion. For example, it detects facial expressions of surprise or anxiety and adjusts the data display. The environmental data collection unit also records the user's voice when checking the environmental data and estimates the emotion using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The environmental data collection unit also uses a sensor to collect biometric data (heart rate and electrodermal activity) when the user checks the environmental data and analyzes the emotion using an emotion estimation algorithm. For example, it calculates an emotion score based on fluctuations in heart rate. This makes it possible to optimize the data display method according to the user's emotion.

[0064] The environmental data collection unit can collect data from both urban areas and natural environments and perform comparative analysis. The environmental data collection unit, for example, collects data from sensors installed in urban areas and sensors installed in natural environments and performs comparative analysis. For example, it compares the concentrations of harmful substances in the air. The environmental data collection unit can also collect water quality data from both urban areas and natural environments and perform comparative analysis. For example, it compares the concentrations of harmful substances in water. The environmental data collection unit can also collect soil data from both urban areas and natural environments and perform comparative analysis. For example, it compares the concentrations of harmful substances in the soil. In this way, by comparing and analyzing data from urban areas and natural environments, it is possible to clearly understand the differences between the environments.

[0065] The environmental data collection unit can collect data using an autonomous vehicle equipped with a mobile sensor. For example, the environmental data collection unit can mount an environmental sensor on an autonomous vehicle and collect data by patrolling urban areas and natural environments. For example, it can measure harmful substances in the air, temperature, and humidity. The environmental data collection unit can also collect data using a drone equipped with a mobile sensor. For example, it can collect data by patrolling a wide area of ​​forest. The environmental data collection unit can also collect data using a robot equipped with a mobile sensor. For example, it can collect environmental data inside a factory. This makes it possible to collect data over a wide area by using an autonomous vehicle.

[0066] The environmental data collection unit can use the emotion estimation function to make environmental improvement suggestions to the user based on the results of environmental data collection. The environmental data collection unit, for example, estimates the user's emotions based on the results of environmental data collection and makes environmental improvement suggestions. For example, it can suggest the use of an air purifier. The environmental data collection unit can also make energy efficiency suggestions based on the user's emotions. For example, it can suggest reducing energy consumption. The environmental data collection unit can also make suggestions to promote recycling based on the user's emotions. For example, it can explain the importance of recycling and encourage recycling activities. This makes it possible to make more effective environmental improvement suggestions by making suggestions based on the user's emotions.

[0067] The data analysis unit can add an anomaly detection algorithm and instantly detect sudden environmental changes. For example, the data analysis unit can introduce an anomaly detection algorithm into the AI ​​to instantly detect a sudden increase in harmful substances in the air. For example, it can detect pollution caused by factory exhaust gases. The data analysis unit can also use an anomaly detection algorithm to instantly detect sudden changes in water quality. For example, it can detect water pollution caused by factory wastewater. The data analysis unit can also use an anomaly detection algorithm to instantly detect sudden changes in soil. For example, it can detect soil pollution caused by chemical leaks. In this way, the anomaly detection algorithm can instantly detect sudden environmental changes.

[0068] The data analysis unit can introduce time series analysis to perform comparative analysis with past data. For example, the data analysis unit can introduce time series analysis into AI to compare past air quality data with current data. For example, it can analyze seasonal variations in air pollution. The data analysis unit can also use time series analysis to perform comparative analysis of past and current water quality data. For example, it can analyze variations in water quality throughout the year. The data analysis unit can also use time series analysis to perform comparative analysis of past and current soil data. For example, it can analyze variations in soil pollution over a long period of time. This allows past data to be compared and analyzed with current data using time series analysis.

[0069] The data analysis unit can share the results of the environmental data analysis with other cities and regions to make wide-area environmental risk assessments. The data analysis unit, for example, uploads the results of the environmental data analysis to the cloud and shares them with other cities and regions. For example, it compares air pollution data from multiple cities. The data analysis unit can also collect data from other cities and regions to make integrated risk assessments in order to make wide-area environmental risk assessments. For example, it can compare the impact of air pollution between regions. The data analysis unit can also share data with other cities and regions to make joint environmental risk assessments. For example, multiple cities can cooperate to take environmental protection measures. This makes it possible to make wide-area environmental risk assessments.

[0070] The data analysis unit can develop an interactive dashboard for visually displaying the results of analysis by AI. The data analysis unit can develop, for example, an interactive dashboard for visually displaying the results of analysis by AI. For example, the data can be displayed using graphs and charts. The data analysis unit can also develop a dashboard with a real-time data display function. For example, the data from sensors can be displayed in real time. The data analysis unit can also develop a dashboard with a user interaction function. For example, the dashboard can allow users to manipulate data to obtain detailed information. This allows users to intuitively understand the data by visually displaying it.

[0071] The data analysis unit can use the emotion estimation function to suggest actions to the user based on the results of the environmental risk assessment. For example, the data analysis unit estimates the user's emotions based on the results of the environmental risk assessment and suggests actions. For example, it can suggest the use of an air purifier. The data analysis unit can also suggest energy efficiency improvements based on the user's emotions. For example, it can suggest reducing energy consumption. The data analysis unit can also suggest recycling promotion based on the user's emotions. For example, it can explain the importance of recycling and encourage recycling activities. This makes it possible to make more effective suggestions by suggesting actions based on the user's emotions.

[0072] The animal monitoring unit can mount an infrared camera on the drone and track the movements of wild animals at night. The animal monitoring unit, for example, mounts an infrared camera on the drone and tracks the movements of wild animals at night. For example, it monitors the behavior patterns of nocturnal animals. The animal monitoring unit can also use the infrared camera to monitor the habitats of wild animals at night. For example, it installs an infrared camera to monitor the movements of animals at night. The animal monitoring unit can also use the infrared camera to analyze the behavior patterns of wild animals at night. For example, it analyzes images from the infrared camera to identify the behavior of the animals. This makes it possible to track the movements of wild animals at night with high accuracy.

[0073] The animal monitoring unit incorporates AI into the surveillance camera, allowing it to automatically identify the type of animal and its behavioral patterns. For example, the animal monitoring unit incorporates AI into the surveillance camera to automatically identify the type of animal. For example, it recognizes specific animals from camera footage. The animal monitoring unit can also use AI to automatically identify the behavioral patterns of animals. For example, it analyzes camera footage to identify the behavior of animals. The animal monitoring unit can also use AI to comprehensively analyze the type of animal and its behavioral patterns. For example, it can simultaneously identify the type of animal and its behavior based on camera footage. In this way, the use of AI makes it possible to automatically identify the type of animal and its behavioral patterns.

[0074] The animal monitoring unit can use the emotion estimation function to analyze the emotions of users participating in wildlife conservation activities and optimize the content of the activities. For example, the animal monitoring unit analyzes the facial expressions of users participating in wildlife conservation activities to estimate their emotions. For example, it detects joy or satisfaction during the activities. The animal monitoring unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The animal monitoring unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This makes it possible to optimize the content of conservation activities based on the user's emotions.

[0075] The animal monitoring department can use drones to monitor a wide range of ecosystems and assess the impact of environmental changes. For example, the animal monitoring department can use drones to monitor a wide range of forests and assess the impact of environmental changes. For example, the impact of deforestation can be analyzed. The animal monitoring department can also use drones to monitor a wide range of marine ecosystems. For example, the impact of marine pollution can be analyzed. The animal monitoring department can also use drones to monitor a wide range of wetland ecosystems. For example, the impact of drying out wetlands can be analyzed. This makes it possible to monitor a wide range of ecosystems and assess the impact of environmental changes.

[0076] The animal monitoring department can upload surveillance camera data to the cloud in real time, allowing multiple experts to analyze it simultaneously. For example, the animal monitoring department can build a system that uploads surveillance camera video data to the cloud in real time and allows multiple experts to analyze it simultaneously. For example, analyzing the behavioral patterns of wild animals. The animal monitoring department can also share data on the cloud, allowing experts to conduct joint analysis. For example, multiple experts can analyze data simultaneously and develop strategies for conservation activities. The animal monitoring department can also store data on the cloud, allowing them to analyze it later. For example, analyzing long-term animal behavior patterns based on past data. This allows multiple experts to analyze data simultaneously.

[0077] The animal monitoring unit can use the emotion estimation function to provide an educational program on wildlife conservation activities and enhance the emotions of participants. The animal monitoring unit, for example, uses the emotion estimation function to provide an educational program on wildlife conservation activities. For example, it analyzes the emotions of participants and adjusts the program content. The animal monitoring unit can also analyze the facial expressions of participants during the educational program to estimate their emotions. For example, it can detect excitement or satisfaction and adjust the program content. The animal monitoring unit can also record the voices of participants during the educational program and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of voice to calculate an emotion score. This can enhance the emotions of participants and increase their motivation to participate in conservation activities.

[0078] The education provision unit can use AI to provide customized educational programs according to the learning progress of each individual user. The education provision unit, for example, uses AI to analyze the user's learning progress in real time and provide a customized educational program. For example, the learning content can be individually adjusted. The education provision unit can also use AI to propose an optimal learning plan based on the user's learning history. For example, it can analyze past learning data and suggest what content should be learned next. The education provision unit can also use AI to provide educational programs according to the user's learning style. For example, a program that makes heavy use of visual content can be provided to a user who prefers visual learning. This makes it possible to provide a customized educational program according to the user's learning progress.

[0079] The education provider can introduce virtual reality (VR) technology into environmental education programs to provide a more immersive learning experience. For example, the education provider can introduce VR technology into environmental education programs to allow users to learn in a virtual environment. For example, the education provider can provide experiential learning in a virtual natural environment. The education provider can also use VR technology to provide a program that allows users to actually experience environmental protection activities. For example, the user can experience tree planting in a virtual forest. The education provider can also use VR technology to provide a program that allows users to experience environmental problem sites. For example, the user can experience a virtual marine pollution site. In this way, the use of virtual reality technology can provide a more immersive learning experience.

[0080] The education provider can provide environmental education programs in multiple versions for different age groups and levels of expertise. For example, the education provider can provide environmental education programs for different age groups, such as for children, adults, and seniors. For example, simple language and illustrations are used extensively for children. The education provider can also provide environmental education programs for different levels of expertise, such as for beginners, intermediate learners, and advanced learners. For example, basic content is provided for beginners, applied content is provided for intermediate learners, and specialized content is provided for advanced learners. The education provider can also customize environmental education programs for different age groups and levels of expertise. For example, a program for children is provided that includes specialized content. This makes it possible to provide educational programs for different age groups and levels of expertise.

[0081] The education providing unit can gamify the content of an educational program to attract users' interest. For example, the education providing unit gamifies the content of an environmental education program, allowing users to learn while having fun. For example, it can provide a quiz-style game for learning about environmental issues. The education providing unit can also introduce a point system, allowing users to earn points as they progress in their studies. For example, points can be awarded each time a learning content is completed. The education providing unit can also introduce a level-up system, allowing users to level up as they progress in their studies. For example, each time a learning content is completed, the level increases and new content is unlocked. In this way, gamification can attract users' interest.

[0082] The education provider can use the emotion estimation function to evaluate the effectiveness of an educational program and improve the content based on the user's emotions. The education provider, for example, analyzes the user's facial expressions during the educational program to estimate the user's emotions. For example, it detects excitement or satisfaction and adjusts the program content. The education provider can also record the user's voice and estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The education provider can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze the user's emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This allows the content of the educational program to be improved based on the user's emotions.

[0083] The reward provider can use blockchain technology to build a transparent reward system for users who participate in environmental protection activities. The reward provider, for example, builds a system using blockchain technology to grant rewards to users who participate in environmental protection activities. For example, a token is issued for each activity. The reward provider can also use blockchain technology to ensure the transparency of rewards. For example, the reward history can be recorded on the blockchain so that anyone can check it. The reward provider can also use blockchain technology to prevent fraudulent rewards. For example, the issuance and exchange of rewards can be managed on the blockchain. In this way, a transparent reward system can be built using blockchain technology.

[0084] The reward provision unit can customize the content of rewards and rewards according to the user's interests and concerns. For example, the reward provision unit builds a system that customizes the content of rewards and rewards according to the user's interests and concerns. For example, eco-friendly goods are provided to a user who is interested in environmental protection. The reward provision unit can also customize the content of rewards and rewards based on the user's past activity history. For example, rewards are provided according to activities participated in in the past. The reward provision unit can also customize the content of rewards and rewards based on user feedback. For example, rewards are selected based on the user's evaluation. This makes it possible to provide rewards and rewards according to the user's interests and concerns.

[0085] The reward provision unit can use the emotion estimation function to optimize the method of providing rewards and rewards, thereby increasing user satisfaction. For example, the reward provision unit analyzes the user's facial expressions and estimates their emotions when providing rewards and rewards. For example, it detects joy or satisfaction and adjusts the method of providing them. The reward provision unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The reward provision unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This makes it possible to optimize the method of providing rewards and rewards based on the user's emotions.

[0086] The reward provider can make it possible to exchange points earned by participating in environmental protection activities for other eco-friendly services or products. For example, the reward provider can build a system in which points earned by participating in environmental protection activities can be exchanged for eco-friendly products. For example, the points can be exchanged for recycled products or eco-bags. The reward provider can also make it possible to exchange points for eco-friendly services. For example, the points can be exchanged for renewable energy services. The reward provider can also make it possible to exchange points with other users. For example, the points can be transferred or shared. In this way, the points earned by participating in environmental protection activities can be exchanged for eco-friendly services or products.

[0087] The reward provider can provide a community platform where participants in environmental protection activities can interact with each other. The reward provider can, for example, provide an online community platform where participants in environmental protection activities can interact with each other. For example, by introducing a forum or chat function. The reward provider can also provide a platform where participants can share information with each other. For example, by sharing the results and experiences of activities. The reward provider can also provide a platform where participants can cooperate with each other on activities. For example, by working together on a project. In this way, by providing a community platform where participants can interact with each other, collaboration in activities is strengthened.

[0088] The reward provision unit can use the emotion estimation function to analyze the user's emotional response to the provision of a reward or a reward and improve the content of the reward. For example, the reward provision unit analyzes the user's facial expression when providing a reward or a reward and estimates the user's emotion. For example, it detects joy or satisfaction and improves the content of the reward. The reward provision unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The reward provision unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze the emotion using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This makes it possible to improve the content of the reward or reward based on the user's emotion.

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

[0090] The Environmental Data Collection Department not only collects environmental data, but can also propose specific action plans for environmental improvement based on the collected data. For example, it can propose tree planting activities in areas with high concentrations of harmful substances in the air. Also, if changes in water quality are detected, it can recommend that local residents install filters to improve water quality. Furthermore, it can suggest the use of soil conditioners in areas with high concentrations of harmful substances in the soil. In this way, the Environmental Data Collection Department can not only collect data, but also propose specific environmental improvement actions.

[0091] The data analysis unit can create an environmental risk map for each region based on the results of the analysis of environmental data. For example, areas with a high risk of air pollution can be displayed in red, and areas with a low risk in green. It can also create a risk map of water pollution and color-code it according to the level of pollution. It can also create a risk map of soil pollution and color-code it according to the level of pollution. This allows users to visually grasp the environmental risk for each region.

[0092] The Animal Monitoring Department can propose locations for animal sanctuaries based on wild animal behavior data. For example, it can analyze the routes that specific animals frequently travel and propose the establishment of sanctuaries along those routes. It can also propose the expansion of sanctuaries in areas where animal habitats are declining. Furthermore, if animal breeding grounds are identified, it can propose designating those areas as sanctuaries. This allows the Animal Monitoring Department to propose effective establishment of animal sanctuaries based on data.

[0093] The education provider can use the emotion estimation function to provide feedback to increase the user's motivation to learn. For example, if the user shows excitement or satisfaction while learning, it can provide positive feedback that reinforces those emotions. Also, if the user feels anxious or stressed while learning, it can provide support to alleviate those emotions. Furthermore, it can adjust the learning content and pace based on the user's emotions. This makes it possible to provide an optimal learning experience that suits the user's emotions.

[0094] The reward provision unit can use the emotion estimation function to provide rewards and rewards based on the user's emotions. For example, if the user expresses joy or satisfaction when participating in environmental conservation activities, the reward provision unit can provide rewards that reinforce those emotions. Also, if the user feels anxiety or stress during the activity, the reward provision unit can provide rewards that alleviate those emotions. Furthermore, the content of rewards and rewards can be customized based on the user's emotions. This makes it possible to provide the most appropriate rewards and rewards according to the user's emotions.

[0095] The environmental data collection unit can use the emotion estimation function to analyze the emotion of the user when checking the environmental data and optimize the data display method. For example, it can use a camera to analyze the user's facial expression when checking the environmental data and estimate the emotion. For example, it can detect facial expressions of surprise or anxiety and adjust the data display. The environmental data collection unit also records the user's voice when checking the environmental data and estimates the emotion using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The environmental data collection unit also uses a sensor to collect biometric data (heart rate and electrodermal activity) when the user checks the environmental data and analyzes the emotion using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This makes it possible to optimize the data display method according to the user's emotion.

[0096] The data analysis unit can add an anomaly detection algorithm to instantly detect sudden environmental changes. For example, by introducing an anomaly detection algorithm into the AI, it can instantly detect a sudden increase in harmful substances in the air. For example, it can detect pollution caused by factory exhaust gases. The data analysis unit can also use an anomaly detection algorithm to instantly detect sudden changes in water quality. For example, it can detect water pollution caused by factory wastewater. The data analysis unit can also use an anomaly detection algorithm to instantly detect sudden changes in soil. For example, it can detect soil pollution caused by chemical leaks. In this way, the anomaly detection algorithm can instantly detect sudden environmental changes.

[0097] The data analysis unit can introduce time series analysis to perform comparative analysis with past data. For example, time series analysis can be introduced into AI to compare past air quality data with current data. For example, this can be used to analyze seasonal variations in air pollution. The data analysis unit can also use time series analysis to perform comparative analysis of past and current water quality data. For example, this can be used to analyze variations in water quality throughout the year. The data analysis unit can also use time series analysis to perform comparative analysis of past and current soil data. For example, this can be used to analyze variations in soil pollution over a long period of time. This allows for comparative analysis of past and current data using time series analysis.

[0098] The data analysis unit can use the emotion estimation function to suggest actions to the user based on the results of the environmental risk assessment. For example, the data analysis unit estimates the user's emotions based on the results of the environmental risk assessment and suggests actions. For example, it can suggest the use of an air purifier. The data analysis unit can also suggest energy efficiency improvements based on the user's emotions. For example, it can suggest reducing energy consumption. The data analysis unit can also suggest recycling promotion based on the user's emotions. For example, it can explain the importance of recycling and encourage recycling activities. This makes it possible to make more effective suggestions by suggesting actions based on the user's emotions.

[0099] The reward provider can make it possible to exchange points earned by participating in environmental protection activities for other eco-friendly services or products. For example, the reward provider can build a system in which points earned by participating in environmental protection activities can be exchanged for eco-friendly products. For example, the points can be exchanged for recycled products or eco-bags. The reward provider can also make it possible to exchange points for eco-friendly services. For example, the points can be exchanged for renewable energy services. The reward provider can also make it possible to exchange points with other users. For example, the points can be transferred or shared. In this way, the points earned by participating in environmental protection activities can be exchanged for eco-friendly services or products.

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

[0101] Step 1: The environmental data collection unit collects environmental data. For example, sensors may be installed to measure the concentration of harmful substances in the air or changes in water quality. The environmental data collection unit can also collect data in real time using IoT technology. For example, the data collected by the sensors can be sent to the cloud for real-time monitoring. Step 2: In the data analysis unit, the generation AI analyzes the environmental data collected by the environmental data collection unit. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the data and determine the risk of environmental pollution. The generation AI may also use a multimodal generation AI to detect anomalies in the data. The generation AI may also analyze the data using statistical analysis and machine learning algorithms. For example, the generation AI has learned from a large amount of data and has advanced analytical capabilities. Step 3: The Animal Monitoring Department monitors the movements of wild animals based on the data analyzed by the Data Analysis Department. For example, it installs surveillance cameras to monitor wild animal habitats and flies drones to track their movements over a wide area. The Animal Monitoring Department can also use AI to automatically identify animal species and behavioral patterns. For example, it can analyze surveillance camera footage and recognize specific animals. Step 4: The Education Department provides environmental education programs based on the data monitored by the Animal Monitoring Department. For example, AI can collect and analyze information on environmental protection and create educational programs based on that information. The Education Department can also introduce virtual reality (VR) technology to provide an immersive learning experience. For example, it can provide experiential learning in a virtual natural environment. Step 5: The rewards provider provides rewards and rewards based on the program provided by the education provider. For example, points may be awarded to people who participate in environmental protection activities, and those points may be exchanged for rewards and rewards. The rewards provider may also use blockchain technology to build a transparent reward system. For example, tokens may be issued for each activity and rewards may be awarded.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0136] 7, 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0161] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an environmental data collection unit that collects environmental data; a data analysis unit that analyzes the environmental data collected by the environmental data collection unit; an animal monitoring unit that monitors the movements of wild animals based on the data analyzed by the data analysis unit; an education providing unit that provides environmental education programs based on the data monitored by the animal monitoring unit; a reward provision unit that provides a reward or a reward based on the program provided by the education provision unit; A system characterized by:

2. The environmental data collection unit Using emotion estimation functionality, we analyze the emotions users feel when viewing environmental data and optimize the way data is displayed.

2. The system of claim 1.

3. The environmental data collection unit Using emotion estimation functionality to suggest environmental improvements to users based on the results of environmental data collection 2. The system of claim 1.

4. The data analysis unit Using emotion estimation functionality, we analyze the emotional responses of users who receive environmental risk warnings and optimize the content of the warnings.

2. The system of claim 1.

5. The animal monitoring unit: Using an emotion estimation function, the emotions of users participating in the wildlife conservation activities are analyzed and the content of the activities is optimized.

2. The system of claim 1.

6. The education providing department Analyzing users' emotional responses to educational program content using emotion estimation functionality and optimizing program content 2. The system of claim 1.

7. The benefit providing unit: Using the emotion estimation function, the method of providing the benefits and rewards is optimized to increase user satisfaction.

2. The system of claim 1.

8. The benefit providing unit: Using an emotion estimation function, the user's emotional response to the provision of the benefit or reward is analyzed, and the content of the provision is improved.

2. The system of claim 1.

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