System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting environmental data from an external source; means for generating a virtual forest environment using the environmental data; user interface means for a user to perform an operation for managing a virtual forest; means for collecting management operation data by the user; means for transmitting the management operation data to a server; artificial intelligence analysis means for analyzing the management operation data and evaluating an efficient forest management method and profitability; and means for providing an analysis result to a public organization.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The present invention aims to solve the inefficiency of forest management in Japan and the resulting problems of abandoned forests and a decrease in the number of forestry workers. Specifically, the objective is to find efficient methods for effectively managing abandoned forests and improving forestry profitability, and to provide this information to public organizations. [Means for solving the problem]

[0005] The present invention includes means for collecting environmental data (weather data and topographical data) from external sources, means for generating a virtual forest environment using the environmental data, a user interface means for allowing a user to perform operations for managing the virtual forest, and means for collecting data on user management operations.

[0006] The system further includes a means for transmitting the management operation data to a server and a means for analyzing the management operation data and evaluating efficient forest management methods and profitability using artificial intelligence. It also includes a means for providing the analysis results to public organizations, and may further include a means for displaying the analysis results as feedback to the user. In this way, a system is provided that derives effective forest management methods based on the collected environmental data and contributes to the revitalization of Japan's forestry industry.

[0007] "Environmental data" refers to information collected from external sources that is used in managing and cultivating forests, and includes meteorological data, topographical data, and the like.

[0008] A "virtual forest environment" is a digital environment that simulates a forest and is generated on a computer based on collected environmental data.

[0009] "User interface means" refers to the operating and input means used by users to operate and manage the virtual forest via a computer or terminal.

[0010] "Management operation data" refers to data relating to operations and management performed by a user within a virtual forest environment, including actions such as planting trees and thinning.

[0011] "Artificial intelligence analysis means" refers to artificial intelligence technology that analyzes collected management operation data and uses the results to evaluate efficient forest management methods and profitability.

[0012] "Public body" refers to a local or national agency responsible for managing or conserving forests.

[0013] "Feedback" refers to providing the analysis results to the user and providing information so that the user can perform operations again based on the information obtained. [Brief explanation of the drawings]

[0014] [Figure 1]1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] 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.

[0019] 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.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] 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.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] System Overview

[0036] This invention is a system that provides a "forestry development game" aimed at improving the efficiency and profitability of forest management. This system collects weather and topographical data from external sources and generates a virtual forest environment based on that data. The user operates this virtual forest environment using a terminal, planting trees and thinning them. The user's operation data is sent from the terminal to a server, where it is analyzed using artificial intelligence. The analysis results are provided to public organizations and are also used as feedback to the user.

[0037] Program processing

[0038] 1. Data Collection and Storage

[0039] Server: Weather data is obtained from an external weather data provider via API. Topographical data is also obtained. This data is stored in a database on the server.

[0040] Example: The server retrieves data in JSON format from a weather data provider, parses it, and stores it in a database.

[0041] 2. Generating the Game Environment

[0042] Server: Generates a virtual forest environment based on collected weather and terrain data. The generated virtual forest environment is sent to the user's device and displayed by the game engine.

[0043] Example: The server integrates weather and terrain data to generate a 3D model of a forest environment and delivers it to the user's device.

[0044] 3. User Gameplay

[0045] User device: The user launches the game and plants and thins trees in the virtual forest environment. Operational data is collected as the user interacts with the user interface.

[0046] Example: When a user plants a tree at a specific location, the location and type of tree are recorded as operation data.

[0047] 4. Data collection and transmission

[0048] User device: The user's gameplay operation data is periodically sent to the server using the HTTP protocol.

[0049] Example: When a user plays a game for a certain period of time, all of the gameplay data is packaged and sent to the server.

[0050] 5. Data Analysis

[0051] Server: Analyzes the received operational data using an artificial intelligence model. The analysis uses an efficiency model and a profitability model, and integrates the results obtained from each model.

[0052] Example: The server receives user operation data, evaluates training efficiency using an efficiency model, and evaluates predicted revenue using a profitability model.

[0053] 6. Providing analysis results and feedback

[0054] Server: Provides analysis results to public organizations, sends data via email and API, and displays the analysis results as feedback to users to help them improve their gameplay.

[0055] Example: The server provides recommendations for efficient forest management to public organizations via email and dashboards, while simultaneously displaying analysis results to users as in-game messages.

[0056] As described above, this invention is a system that collects meteorological and topographical data, generates a virtual forest environment, allows users to operate it, and analyzes the operational data to derive efficient forest management methods. The analysis results will be provided to public organizations and used as feedback to users, contributing to the sustainable management of Japan's forests.

[0057] The processing flow will be explained below.

[0058] Step 1: Data collection

[0059] Server: Obtains weather data from an external weather data provider via API, parses the data in JSON format, and stores it in the database. Also obtains terrain data from a terrain data provider and stores it in the database.

[0060] Step 2: Creating a virtual forest environment

[0061] Server: Integrates collected weather and terrain data to generate a 3D model of the virtual forest environment, which is then sent to the user's device and displayed in the game engine.

[0062] Step 3: Launch the game

[0063] User: Launches the forest growing game on the device and starts a new game session. The user's information and preferences are saved and the virtual forest environment is displayed.

[0064] Step 4: Planting the tree

[0065] User: Through the user interface, the user plants a tree at a specific location within the virtual forest environment. At this time, information about the type of tree and the planting location is recorded as operational data.

[0066] Step 5: Thinning

[0067] User: Performs thinning operations to thin out trees in a specific area. The location of the thinning is also recorded as operation data.

[0068] Step 6: Collecting operational data

[0069] User device: A series of user operation data is periodically collected, including records of tree planting and thinning operations.

[0070] Step 7: Sending operation data to the server

[0071] User terminal: The collected operation data is sent to the server using the HTTP protocol, ensuring secure communication.

[0072] Step 8: Artificial intelligence analysis of the data

[0073] Server: Analyzes the received user operation data using AI models. The efficiency model evaluates training efficiency, and the profitability model evaluates predicted profits.

[0074] Step 9: Integrating the analysis results

[0075] Server: Integrates the results of the efficiency and profitability models to produce the final analysis, which includes optimal forest management practices and profitable practices.

[0076] Step 10: Provide analysis results

[0077] Server: Provides analysis results to public organizations. Data is sent via email or API and used as useful information for public organizations.

[0078] Step 11: View feedback

[0079] Server: Display the analysis results as feedback to the user, providing information that the user can use in their next play through in-game messages and notifications.

[0080] These are the specific processing steps of this system. It clearly shows how the server, terminal, and user are involved and operate at each step.

[0081] Example 1

[0082] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0083] Traditional forest management involves directly operating and managing actual forests, which makes it difficult to evaluate efficient management methods and profitability. Another issue is that data collection and analysis takes a lot of time and money, making it difficult to obtain immediate feedback. This makes management tasks such as forest cultivation and thinning inefficient, making sustainable forest management difficult.

[0084] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0085] In this invention, the server includes means for collecting environmental data from an external data providing service, means for generating a virtual forest environment using the environmental data and transmitting it to the user's terminal, and means for analyzing management operation data by the user using artificial intelligence analysis technology and evaluating efficient forest management methods and profitability. This allows the user to try out efficient management methods in the virtual environment and receive immediate feedback on the analysis results.

[0086] "External data provision service" is an online service that provides environmental information such as meteorological data and topographical data.

[0087] "Environmental data" refers to information about a specific region or environment, such as meteorological data or topographical data.

[0088] A "virtual forest environment" is a three-dimensional model of a forest recreated on a computer based on collected environmental data.

[0089] "User interface means" is a collective term for input devices and software that allow users to operate and manage the virtual forest environment.

[0090] "Administrative operation data" is data relating to operations performed by a user within the virtual forest environment.

[0091] A "user terminal" is an electronic device such as a computer, smartphone, or tablet that is operated by a user.

[0092] "Artificial intelligence analysis technology" is a technology that uses machine learning and data mining to analyze data and extract useful information.

[0093] "Efficient forest management methods" are forest management techniques that achieve maximum results with minimal resources and effort.

[0094] A "means for assessing profitability" is a means for analyzing operational data related to forest management and predicting future profits based on that data.

[0095] "Means for providing analysis results" refers to the method for communicating the information obtained through the analysis to public organizations and users.

[0096] This invention is a system for improving the efficiency and profitability of forest management. The system collects meteorological and topographical data from external data providers and generates a virtual forest environment based on that data. Users use their devices to perform operations such as planting and thinning trees in this virtual forest environment, and the operation data is sent to a server. The server then analyzes the data using artificial intelligence analysis technology and provides the analysis results to public organizations and users.

[0097] Data collection and storage

[0098] The server collects weather data from external data providers (for example, weather data providers) using APIs. It also collects terrain data and stores this data in a database. Specifically, the server obtains data in JSON format, parses it, and stores it.

[0099] Game environment generation

[0100] The server generates a virtual forest environment based on the stored weather and terrain data. It first analyzes the terrain data and then integrates weather data to create a realistic virtual environment. The generated virtual forest environment is sent to the user's device as a 3D model and displayed in a game engine such as Unity.

[0101] User gameplay

[0102] Users launch the game on their device and access the virtual forest environment. They perform operations within the game, such as planting and thinning trees, and these operation data are stored on the device in real time. Users can select specific areas using their fingers or a mouse to plant or cut down trees. Each time they perform an operation, their location information and operation details are recorded in the device's local storage.

[0103] Data collection and transmission

[0104] The user device periodically sends the collected operation data to the server. The HTTP protocol is used for transmission, and the data is sent in encrypted format. All operation data performed by the user over the course of one hour is compiled in JSON format and sent to the server. Transmission is secure using HTTPS.

[0105] Data analysis

[0106] The server analyzes the received operational data using artificial intelligence analysis technology. Two main models are used in the analysis: an efficiency model and a profitability model, and the results of each are integrated. The server uses an AI framework such as TENSORFLOW® to evaluate the efficiency of forest management using the efficiency model and to predict future profits using the profitability model.

[0107] Providing analysis results and feedback

[0108] The server provides the analysis results to the public organization and sends the data via email or API. It also displays the analysis results as feedback to the user, helping them to operate the virtual forest. Specifically, the server provides the public organization with recommendations for efficient forest management methods via email or dashboard, and simultaneously displays the analysis results to the user as in-game messages and pop-up notifications.

[0109] Prompt Sentence Examples

[0110] "I'm thinking of a system that allows users to plant trees in a virtual forest environment, collects and analyzes their growth data, and then proposes efficient forest management. I'd like to know the detailed processing steps and analysis methods of this system."

[0111] As described above, the present invention achieves efficient and sustainable forest management through a series of processes that begin with the collection of external data, then the generation of a virtual forest environment, the collection and analysis of user operation data, and finally the provision of the analysis results to public organizations and users.

[0112] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0113] Step 1: Data collection and storage

[0114] The server accesses the API of an external data provider to obtain weather and terrain data. It sends an API request as input and receives weather and terrain data in JSON format as output. Specifically, the server runs periodic jobs to collect environmental data such as temperature, precipitation, and wind speed from the OpenWeatherMap API, parses it, and stores it in a database.

[0115] Step 2: Generate the game environment

[0116] The server generates a virtual forest environment based on weather and terrain data stored in a database. It reads the weather and terrain data in the database as input and sends a 3D model of the virtual forest to the user's device as output. Specifically, the server uses 3D modeling software to recreate the terrain and integrates weather parameters onto it. The generated 3D model is exported in JSON format and sent to the user's device.

[0117] Step 3: User gameplay

[0118] The user launches the game on their device and accesses the virtual forest environment. The input is a 3D model of the virtual forest sent from the server, and the output is generated as user operation data. Specifically, the user plants or thins trees with their finger or mouse, and the operation data (location information and tree type) is recorded in real time in the device's local storage.

[0119] Step 4: Collect and send data

[0120] The user's device periodically sends the collected operation data to the server. The operation data stored in the device's local storage is read as input, and the operation data sent to the server is obtained as output. Specifically, after the user plays the game for one hour, all operation data collected during that time is compiled in JSON format and sent to the server in an encrypted state via HTTPS.

[0121] Step 5: Analyze the data

[0122] The server analyzes the received operation data using artificial intelligence analysis technology. It reads the operation data sent to the server as input and generates an analysis result of efficient forest management methods and profitability as output. Specifically, the server uses an AI framework such as TensorFlow to evaluate cultivation efficiency with an efficiency model and predict future profits with a profitability model. These results are integrated to obtain the analysis result.

[0123] Step 6: Providing analysis results and feedback

[0124] The server provides the analysis results to the public organization and displays them as feedback to the user. The generated analysis results are used as input, and the data is sent to the recipient as output, displaying the feedback. Specifically, the server sends the analysis results to the public organization via email or dashboard, and displays them to the user in the form of a pop-up or message within the game.

[0125] (Application example 1)

[0126] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0127] Current autonomous vehicles collect environmental data around roads, but there is a lack of systems that can effectively utilize this data to help manage and monitor the environment around roads. Furthermore, there is a need for a method to propose efficient and profitable green space management methods based on the collected data and provide feedback to public organizations and users. As a result, the efficiency and profitability of forest management and environmental protection are declining.

[0128] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0129] In this invention, the server includes means for collecting environmental data from external sources, means for generating a virtual forest environment using the environmental data, user interface means for users to perform operations to manage the virtual forest, means for collecting management operation data by users, means for transmitting the management operation data to the server, artificial intelligence analysis means for analyzing the management operation data and evaluating efficient forest management methods and profitability, means for providing the analysis results to public organizations, means for generating a virtual environment of the road surroundings using environmental data collected by an automated driving device, and means for collecting environmental data of the road surroundings in real time. This makes it possible to analyze the collected environmental data in real time, derive efficient environmental management methods, and quickly provide feedback to public organizations and users.

[0130] An "external source" is an external data providing service that provides weather data and topographical data collected by the server.

[0131] "Environmental data" refers to information about the natural environment, such as temperature, humidity, wind speed, and topographical information.

[0132] A "virtual forest environment" is a virtual forest simulation generated based on collected environmental data.

[0133] "User interface means" refers to a screen or operating device that allows the user to perform operations to manage the virtual forest.

[0134] "Management operation data" refers to data that includes the operations performed by the user in the virtual forest environment.

[0135] "Server" means a central computer that receives and stores environmental data from external sources, analyzes it, and distributes the results.

[0136] "Artificial intelligence analysis means" refers to AI technology that analyzes collected management operation data and evaluates efficient forest management methods and profitability.

[0137] "Public bodies" are local governments and government agencies responsible for forest management and environmental protection.

[0138] An "autonomous driving device" is a vehicle with autonomous driving capabilities that is equipped with sensors and communication functions for collecting environmental data.

[0139] "Means of collecting data in real time" refers to a mechanism by which the autonomous driving device continuously acquires environmental data while driving and transmits it to a server.

[0140] "Means of providing feedback on analysis results to users" refers to a system that notifies users of the analysis results via in-game messages or the dashboard.

[0141] The present invention relates to a system that utilizes data collected by autonomous vehicles to assist in the management and monitoring of roadside environments. The system includes a series of processes that collect environmental data from external sources, generate a virtual forest environment, allow users to manage the virtual forest, collect and analyze operational data, and evaluate efficient forest management methods and profitability.

[0142] First, the server collects environmental data from external sources. Specifically, sensors, LiDAR (Light Detection and Ranging) technology, and GPS are used. Weather data (temperature, humidity, wind speed, etc.) and terrain data along the road where the vehicle is traveling are collected in real time and stored on the server. Specific hardware used for this data collection includes LiDAR, GPS modules, and temperature and humidity sensors.

[0143] The server then uses the collected environmental data to generate a virtual forest environment using a database (e.g., MySQL®) and an analysis platform (e.g., TensorFlow). The generated virtual forest environment is displayed on the user's device as a 3D model or other format.

[0144] The user operates within the virtual forest environment through a terminal, such as an in-car display, smart glasses, or an in-car VR headset (e.g., Oculus Rift). The user visually checks the virtual forest environment and inputs operational data.

[0145] Next, the management operation data collected by users is periodically sent to a server. Data is transmitted securely using HTTP / HTTPS. The server receives this data and performs artificial intelligence analysis. The AI ​​analysis tools used here are TensorFlow and PyTorch. The server uses this analysis to evaluate efficient forest management methods and projected revenue.

[0146] The analysis results are provided to public organizations and users. Public organizations are provided with the results via dashboard, email, or API. Users are provided with feedback on the analysis results on the in-car display, along with instructions and recommended actions for the next trip.

[0147] As an example, we present a prompt that prompts the user to indicate whether new planting is needed at a particular location.

[0148] Input data:

[0149] Current location (Latitude: 35.6895, Longitude: 139.6917)

[0150] Current temperature: 22.3℃, humidity: 65%, wind speed: 8.5m / s

[0151] question:

[0152] Is new planting needed at this point?

[0153] Based on the data collected, please propose a forest management strategy for the next five years.

[0154] In response to this prompt, the AI ​​analysis tool will suggest appropriate management methods and strategies, and the results will be fed back to the user. This makes it possible to analyze collected environmental data in real time, derive efficient environmental management methods, and provide rapid feedback to public organizations and users.

[0155] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0156] Step 1:

[0157] The server collects environmental data from external sources. It uses sensors, LiDAR, and GPS installed in the autonomous vehicle to obtain weather data (temperature, humidity, wind speed) and terrain data in real time while driving. The input data is raw data from the sensors, and the output data is formatted data stored in the server's database. Specific operations include obtaining data from the sensors, converting the data format, and storing it in the database.

[0158] Step 2:

[0159] The server generates a virtual forest environment based on the collected environmental data. It reads the environmental data stored in the database and generates a 3D model using TensorFlow. The input data is the collected weather and terrain data, and the output data is a 3D model of the virtual forest environment. Specific operations include data reading, model generation, and distribution of the model to the user's device.

[0160] Step 3:

[0161] The user operates within the virtual forest environment using a terminal. The operation data by the user is collected and sent to the server. The input data is the management operation performed by the user (e.g., planting or thinning a tree), and the output data is the operation data sent to the server. Specific operations include operating the user interface, recording the operation data, and sending the data.

[0162] Step 4:

[0163] The server periodically receives and analyzes user management operation data. TensorFlow and PyTorch are used for the analysis to evaluate efficient forest management methods and profitability. The input data is user operation data and collected environmental data, and the output data is the analysis results (e.g., efficiency evaluation, predicted profits). Specific operations include receiving data, inputting it into the model, running the analysis, and generating results.

[0164] Step 5:

[0165] The server provides the analysis results to public organizations and users and provides feedback. Data is sent to public organizations via dashboard, email, or API, and feedback is given to users via in-car displays or devices. The input data is the analysis results, and the output data is the notification content sent to public organizations and users. Specific operations include sending the result data, generating a feedback message, and distributing it to the user's device.

[0166] Step 6:

[0167] The user performs the next management operation based on the provided feedback. The feedback includes efficient planting locations, predicted revenue, and recommended management methods. The input data is the feedback from the server, and the output data is the user's new management operation. Specific actions include checking the feedback, adjusting the operation plan, and executing the new management operation.

[0168] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0169] System Overview

[0170] This invention combines a system that provides a "forest cultivation game" aimed at improving the efficiency and profitability of forest management with an emotion engine that recognizes the user's emotions. The system collects weather and topographical data from external sources and generates a virtual forest environment based on that data. The user uses a terminal to operate this virtual forest environment, planting trees and thinning them. The user's operation data and emotion data are sent from the terminal to a server, where they are analyzed using artificial intelligence. The analysis results are provided to public organizations and also used as feedback to the user.

[0171] Program processing

[0172] 1. Data Collection and Storage

[0173] Server: Obtains weather data from an external weather data provider via API, parses the data in JSON format, and stores it in the database. Also obtains terrain data from a terrain data provider and stores it in the database.

[0174] Example: The server retrieves data in JSON format from a weather data provider, parses it, and stores it in a database.

[0175] 2. Creation of a virtual forest environment

[0176] Server: Generates a 3D model of a virtual forest environment based on collected weather and terrain data. This virtual forest environment is then sent to the user's device and displayed in the game engine.

[0177] Example: The server integrates weather and terrain data to generate a 3D model of a forest environment and delivers it to the user's device.

[0178] 3. Launch the game

[0179] User: Launches the forest growing game on the device and starts a new game session. The user's information and preferences are saved and the virtual forest environment is displayed.

[0180] Example: When a user launches a game, the device displays a configured virtual forest environment.

[0181] 4. Starting the emotion engine and collecting emotion data

[0182] User device: Recognizes user emotions in real time using an emotion engine. Analyzes the user's emotional state from data collected through input devices such as cameras and microphones, and collects it as emotion data.

[0183] Example: A user device uses a camera and microphone to analyze the user's facial expressions and tone of voice to gather information about their emotional state.

[0184] 5. Dynamic interface changes based on emotions

[0185] User terminal: Dynamically change the user interface based on the user's emotional state. For example, if the user is feeling stressed, simplify operations.

[0186] Example: If you recognize that the user is confused, provide more detailed guidance or easier options.

[0187] 6. Performing Management Operations

[0188] User: Plants and thins trees in the virtual forest environment through the user interface. Information on the type of tree, planting location, and thinning location is recorded as operational data.

[0189] Example: When a user plants a tree at a specific location, the location and type of tree are recorded as operation data.

[0190] 7. Collection and transmission of operational and emotional data

[0191] User device: User operation data and emotion data are periodically collected and sent to the server. Data is sent using the HTTP protocol, ensuring secure communication.

[0192] Example: When a user plays a game for a certain period of time, all operation data and emotional data from that time is packaged and sent to the server.

[0193] 8. Artificial Intelligence Analysis of Data

[0194] Server: The received operation data and emotional data are analyzed using an AI model. The efficiency model evaluates training efficiency, and the profitability model evaluates predicted revenue. In addition, by utilizing emotional data in the analysis, the user's stress and satisfaction are reflected in the evaluation.

[0195] Example: The server integrates and analyzes user operation data and emotional data to evaluate training efficiency, profitability, and user satisfaction.

[0196] 9. Integration and provision of analysis results

[0197] Server: Integrates the results of the efficiency and profitability models to produce the final analysis, which includes optimal forest management practices, profitable methods, and user satisfaction. The analysis results are provided to public organizations and fed back to users through in-game messages and notifications.

[0198] Example: The server provides the analysis results to the public body via email or dashboard, and also displays them to the user as an in-game message.

[0199] The above is the specific processing of this system that combines the emotion engine. It clearly shows how the server, terminal, and user are involved and operate at each step. The introduction of the emotion engine not only increases user satisfaction, but also makes it possible to provide more accurate analysis results.

[0200] The processing flow will be explained below.

[0201] Step 1: Data collection

[0202] Server: Obtains weather data from an external weather data provider via API. Parses the obtained data in JSON format and saves it in a database. Similarly, obtains terrain data from a terrain data provider and saves it in a database.

[0203] Example: The server retrieves data from a weather data provider using the "daily_weather_data" endpoint and stores it in the "weather_db". It also retrieves terrain data from the "terrain_data" endpoint and stores it in the "terrain_db".

[0204] Step 2: Creating a virtual forest environment

[0205] Server: Integrates collected weather and terrain data to generate a 3D model of the virtual forest environment. Sends this model to the user's device and prepares it for display in the game engine.

[0206] Example: The server reads data from "weather_db" and "terrain_db", generates a virtual forest using 3D modeling software, and saves the results in "virtual_forest_model".

[0207] Step 3: Launch the game

[0208] User: Launches the forest growing game on the device and starts a new game session. The initial user information and virtual forest environment are loaded.

[0209] Example: When a user opens the app, it downloads the "virtual_forest_model" from the server and starts the game with the user's initial settings.

[0210] Step 4: Launching the emotion engine and collecting emotion data

[0211] User device: Recognizes user emotions in real time using an emotion engine. Analyzes data collected through input devices such as cameras and microphones, and stores it as emotion data.

[0212] Example: The user device activates the camera and uses a facial expression recognition algorithm to analyze the user's emotions (e.g., happiness, sadness, surprise), and stores them in "emotion_data."

[0213] Step 5: Dynamically modifying the interface based on emotions

[0214] User terminal: Dynamically change the user interface based on the user's perceived emotional state. For example, simplify operations or provide positive feedback if the user is feeling stressed.

[0215] Example: If the user is perceived as confused, provide on-screen instructions or hints.

[0216] Step 6: Performing administrative operations

[0217] User: Through the user interface, the user can plant and thin trees in the virtual forest environment. Based on these operations, the type of tree, the location of the planting, and the location of the thinning are recorded as operational data.

[0218] Example: When a user plants an oak tree at a specific location, the location and type of tree are recorded in "operation_data."

[0219] Step 7: Collect and transmit operational and emotional data

[0220] User device: User operation data and emotion data are periodically collected and sent to the server. Data is sent using the HTTP protocol to ensure secure communication.

[0221] Example: After a user plays the game for a certain period of time, "operation_data" and "emotion_data" are sent to the server.

[0222] Step 8: Artificial intelligence analysis of the data

[0223] Server: The received operation data and emotion data are analyzed using an AI model. The efficiency model evaluates training efficiency, and the profitability model evaluates predicted revenue. In addition, by including emotion data in the analysis, evaluations take user experience into account.

[0224] Example: The server inputs "operation_data" and "emotion_data" into "efficiency_model" and "revenue_model" to calculate training efficiency, revenue forecast, and user satisfaction.

[0225] Step 9: Integrate and deliver the analysis results

[0226] Server: Integrates the analytical results obtained from the efficiency model and the profitability model to derive the final analytical results, which include optimal forest management methods, profitable practices, and improved user satisfaction. The analytical results are provided to public organizations and simultaneously displayed as feedback to users.

[0227] Example: The server displays the analytics results in the "forest_management_dashboard" and makes them available to public organizations, and also provides feedback to users via in-game notifications.

[0228] The above is the specific processing of this system that combines the emotion engine. It clearly shows how the server, terminal, and user are involved and operate at each step. The introduction of the emotion engine not only increases user satisfaction, but also makes it possible to provide more accurate analysis results.

[0229] Example 2

[0230] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0231] Current forest management systems lack the means to optimize interfaces that take user emotions into account and to improve user satisfaction. Furthermore, the analysis of collected data does not involve comprehensive evaluation, including user emotional data, which prevents sufficient improvements in management efficiency and profitability. Furthermore, there are also insufficient means to appropriately provide the results of these analyses to both public organizations and users.

[0232] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting environmental data from external sources, means for generating a virtual forest environment using the environmental data, a user interface means for users to perform operations to manage the virtual forest, means for collecting user emotion data in real time using an emotion engine, means for collecting user management operation data and emotion data, means for transmitting the management operation data and emotion data to the server, artificial intelligence analysis means for analyzing the management operation data and emotion data and evaluating efficient forest management methods and profitability, and means for providing the analysis results to public organizations. This allows the interface to be optimized based on the user emotion data, improving management efficiency and profitability. It also provides appropriate feedback to both public organizations and users.

[0233] An "external source" is a data source that exists outside the system and provides weather data, terrain data, and the like.

[0234] "Environmental data" refers to various information necessary for generating and managing a virtual forest environment, such as weather data and topographical data collected by the system from external sources.

[0235] A "virtual forest environment" is a 3D model of a virtual forest that is generated based on collected environmental data and that users can manage.

[0236] "User interface means" means software or hardware that allows a user to perform operations to manage a virtual forest.

[0237] An "emotion engine" is software or a system that uses input devices such as a camera or microphone to recognize and analyze a user's emotional state in real time.

[0238] "Emotional data" refers to information about the user's emotional state determined from facial expressions, tone of voice, etc. collected by the emotion engine.

[0239] "Administrative Operation Data" means information related to operations, such as planting or thinning trees, performed by a user within a virtual forest environment.

[0240] A "server" is a computer system that performs various processes such as data collection, analysis, and providing results.

[0241] "Artificial intelligence analysis means" refers to an analysis system that uses artificial intelligence to analyze collected management operation data and emotional data and evaluate efficient forest management methods and profitability.

[0242] "Analysis results" refers to information regarding efficient forest management methods, profitable methods, and user satisfaction obtained by the artificial intelligence analysis means analyzing management operation data and emotion data.

[0243] "Public organizations" refer to organizations that use the system's analysis results, such as public institutions and local governments.

[0244] This invention provides a "forestry cultivation game" system for improving the efficiency and profitability of forest management. This system aims to increase user satisfaction and provide more accurate analysis results by incorporating an emotion engine that recognizes the user's emotions.

[0245] The system mainly consists of the following elements: a server, a user terminal, an emotion engine, and an external data source.

[0246] Data collection and storage

[0247] The server obtains data from external weather data and terrain data services via APIs, parses it in JSON format, and stores it in a database. Specifically, it uses the OpenWeatherMap API and terrain data API, and MongoDB as the database.

[0248] Generating a virtual forest environment

[0249] The server uses Unity to generate a 3D model of the virtual forest environment based on the collected weather and terrain data, and then uses Blender's API to generate the 3D model, import it into a Unity project, and send it to the user's device.

[0250] Launching the game

[0251] A user launches the smartphone app or PC software and starts a new game session. User information and initial settings are sent to the server, and a virtual forest environment built with Unity is displayed on the device.

[0252] Activating the emotion engine and collecting emotion data

[0253] The user device activates the emotion engine and uses the camera and microphone to recognize the user's emotional state in real time. It uses FaceAPI and Watson® Tone Analyzer to analyze facial expressions and tone of voice and saves the data as emotion data.

[0254] Dynamic interface modification based on emotions

[0255] The user device dynamically changes the interface based on the user's emotional state. For example, if it detects confusion, it will respond by providing more guidance and easier operation options.

[0256] Performing administrative operations

[0257] Users plant and thin trees in the virtual forest environment, and the information is recorded as operational data. Specifically, when a user clicks on a specific spot to plant a tree, the location information and type of tree are recorded.

[0258] Collecting and transmitting operational and emotional data

[0259] The user device periodically collects operational and emotional data and sends it to the server using HTTPS. The data is compiled in JSON format to ensure secure communication.

[0260] AI analysis of data

[0261] The server analyzes the received operation data and sentiment data using artificial intelligence. A machine learning model built using Scikit-learn and TensorFlow evaluates management efficiency, profitability, and user satisfaction.

[0262] Integration and provision of analysis results

[0263] The server combines the analytical results from the efficiency model and the profitability model to derive the final results, which are then provided to the public organization and also provide feedback to users via Unity's notification system.

[0264] Prompt Sentence Examples

[0265] An example of a specific prompt to be input to the generative AI model is as follows:

[0266] "Based on user operation and sentiment data, suggest optimal forest management methods. If users are confused, consider easier operation options."

[0267] "Please use the following dataset to evaluate training efficiency and profitability, including user operation history and emotional state."

[0268] As described above, this system can achieve efficient forest management and improved profitability through the optimization of the user interface using an emotion engine and comprehensive data analysis. Furthermore, the effectiveness of the system can be enhanced by providing appropriate feedback to both public organizations and users.

[0269] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0270] Step 1: Data collection and storage

[0271] The server obtains data from external weather data and terrain data providers via API. The input includes authentication information such as an API key. The server parses the obtained data in JSON format and stores it in a MongoDB database. Specifically, the server calls the OpenWeatherMap API and terrain data API to obtain data and store it in the database.

[0272] Step 2: Creating a virtual forest environment

[0273] The server uses Unity to generate a 3D model of the virtual forest environment based on the collected weather and terrain data. The input includes weather and terrain data, and the output includes a Unity 3D model. Specifically, the server runs a Python script that uses Blender's API to generate the 3D model and import it into the Unity project.

[0274] Step 3: Launch the game

[0275] A user launches a smartphone app or PC software and starts a new game session. Input includes a user ID and password, and output includes a display of the virtual forest environment and session settings. Specifically, a user launches the app, enters their ID and password on the login screen, and a 3D model retrieved from the server is displayed.

[0276] Step 4: Launching the emotion engine and collecting emotion data

[0277] The user device runs an emotion engine and uses the camera and microphone to collect the user's emotional state in real time. The input includes the user's facial expression and voice information, and the output includes emotion data. Specifically, the device uses FaceAPI to analyze the video from the camera and Watson Tone Analyzer to analyze the tone of voice, and then stores the emotion data.

[0278] Step 5: Dynamically modifying the interface based on emotions

[0279] The user device dynamically changes the interface based on the user's recognized emotional state. The input includes emotional data, and the output includes interface modifications. Specifically, the device analyzes the emotional data and, for example, if it recognizes that the user is confused, it increases the number of guide displays and simplifies operation.

[0280] Step 6: Performing administrative operations

[0281] Users plant and thin trees in a virtual forest environment. The input includes information about the user's operations, and the output includes information about the type and location of the trees as a result of the operations. Specifically, the user selects a specific area using a mouse or touch display and performs operations to plant trees, and this information is recorded as operation data.

[0282] Step 7: Collect and transmit operational and emotional data

[0283] The user device periodically collects operation data and emotion data and sends them to the server using HTTPS. The input includes operation data and emotion data, and the output includes the results of transmission to the server. Specifically, the device compiles the data in JSON format at regular intervals and sends it to the server using a secure communication protocol.

[0284] Step 8: Artificial intelligence analysis of the data

[0285] The server uses artificial intelligence to analyze the received operation data and emotion data. The input includes operation data and emotion data, and the output includes the analysis results. Specifically, the server builds a machine learning model using Scikit-learn and TensorFlow to evaluate management efficiency, profitability, and user satisfaction.

[0286] Step 9: Integrate and deliver the analysis results

[0287] The server integrates the analytical results obtained from the efficiency model and the profitability model to derive the final result. The input includes the analytical results, and the output includes the integrated final result. Specifically, the server compiles the analytical results in JSON format and sends them to the public organization's web application via API, and also provides feedback to users using Unity Notifications.

[0288] (Application example 2)

[0289] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0290] Conventional forest management systems and user interfaces were not designed with user emotions in mind, resulting in problems such as poor user experience and operational efficiency. Furthermore, data analysis and feedback based on user emotions were not provided, resulting in insufficient evaluation of efficient forest management methods and profitability. Furthermore, the analysis results provided to public organizations did not reflect user emotions, making it difficult to establish forest management methods that fully considered actual user experience.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting environmental data from external sources, means for generating a virtual forest environment using the environmental data, means for collecting user emotion data, means for collecting user management operation data and emotion data, artificial intelligence analysis means for analyzing the management operation data and emotion data and evaluating efficient forest management methods and profitability, and means for dynamically changing the virtual environment and user interface based on the analysis results. This enables the provision of an operation interface that takes the user's emotional state into consideration and advanced data analysis using emotion data. Furthermore, since the user's experience is reflected in the analysis results provided to public organizations, it becomes possible to establish efficient forest management methods based on actual user behavior.

[0292] "Environmental Data" is data collected from external sources, including weather and terrain data.

[0293] A "virtual forest environment" is a 3D model of a forest environment generated based on collected environmental data.

[0294] "User Interface" means the interface through which a user operates and manages the virtual forest environment.

[0295] "Emotion data" is data that indicates the emotional state of the user, obtained from their facial expressions and voice.

[0296] "Management operation data" refers to data relating to operations such as planting and thinning trees that a user performs within a virtual forest environment.

[0297] The "server" is a computer system that collects environmental data, generates a virtual forest environment, and collects and analyzes management operation data and emotion data.

[0298] "Artificial intelligence analysis means" refers to an algorithm or program that analyzes management operation data and emotion data and evaluates efficient forest management methods and profitability.

[0299] "Public bodies" are bodies, including government agencies and local governments, involved in forest management.

[0300] The "dynamic change means" is a means having the function of adaptively changing the virtual environment and user interface based on the analysis results.

[0301] The present invention is a forest management simulation system that recognizes and utilizes the emotions of a user. Specific examples for carrying out the present invention will be described below.

[0302] System Configuration

[0303] 1. Data Collection and Storage:

[0304] The server acquires weather and terrain data through APIs to collect environmental data from external sources, and stores them in a database in JSON format. It uses weather data provider services and terrain data provider services.

[0305] Example: The server retrieves data in JSON format from a weather data provider, parses it, and stores it in a database.

[0306] 2. Creating a Virtual Forest Environment:

[0307] The server runs a program to generate a 3D model of a virtual forest environment based on the collected weather and topographical data, and transmits this data to the user's device. The 3D model is generated using a 3D modeling tool such as Unity.

[0308] Example: The server integrates weather and terrain data to generate a 3D model of a virtual forest environment and delivers it to the user's device.

[0309] 3. Launch the game:

[0310] The user launches the forest growing game on their device and starts a new game session. Initial settings and user information are saved, and the virtual forest environment is displayed.

[0311] Example: When a user launches a game, a virtual forest environment is displayed on the device.

[0312] 4. Launching the emotion engine and collecting emotion data:

[0313] The user device uses an emotion engine to recognize the user's emotions in real time, analyzing facial expressions and tone of voice from data collected through the camera and microphone, and collecting emotional data.

[0314] Example: A user device uses a camera and microphone to analyze the user's facial expressions and tone of voice to gather information about their emotional state.

[0315] 5. Dynamic interface changes based on emotions:

[0316] The user device has the ability to dynamically change the user interface based on the recognized emotional state, for example, simplifying the operation of the interface if it detects that the user is feeling stressed.

[0317] Example: If you recognize that a user is confused, offer more detailed guidance or easier options.

[0318] 6. Performing administrative operations:

[0319] Users plant and thin trees in the virtual forest environment, and these operations are recorded as management operation data.

[0320] Example: When a user plants a tree at a specific location, the location information and the type of tree are recorded as operation data.

[0321] 7. Collection and Transmission of Operational and Emotional Data:

[0322] The user terminal periodically collects the user's management operation data and emotion data and transmits them to the server using the HTTP protocol.

[0323] Example: When a user plays a game for a certain period of time, the operation data and emotional data from that time are packaged and sent to the server.

[0324] 8. Artificial intelligence analysis of data:

[0325] The server then analyzes the received operation data and emotion data using an artificial intelligence model. This analysis uses AI platforms such as TensorFlow and PyTorch. Along with models that evaluate efficient forest management methods and profitability, the server also uses emotion data to evaluate user satisfaction.

[0326] Example: The server integrates and analyzes user operation data and emotional data to evaluate training efficiency, profitability, and user satisfaction.

[0327] 9. Integration and provision of analytical results:

[0328] The server aggregates the results of the analysis and provides the final results to the public body, while also providing feedback to the user, which is displayed as in-game messages and notifications.

[0329] Example: The server provides the analysis results to public organizations via email or dashboard, and displays them to users as in-game messages.

[0330] Prompt Sentence Examples

[0331] For example, in a factory training simulator, the following prompts are fed to a generative AI model:

[0332] "You are a design engineer for a robot operation simulator. Your task is to create a program that will enable the system to recognize the emotions of factory workers in real time and dynamically change the work interface during training. As a concrete example of emotion analysis, if the user is nervous, simplify the interface and display a guide to help the user calm down."

[0333] In this way, detailed design and refinement can be carried out using generative AI models.

[0334] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0335] Step 1:

[0336] The server collects environmental data from external sources. Specifically, it obtains JSON-formatted data from weather data providers and terrain data providers via APIs, parses it, and stores it in a database. The input is weather data and terrain data obtained from the external APIs, and the output is the parsed environmental data stored in the database.

[0337] Step 2:

[0338] The server generates a 3D model of the virtual forest environment based on the collected environmental data. Specifically, it designs the virtual forest environment using a 3D modeling tool such as Unity and delivers the generated 3D model to the user's device. The input is the saved environmental data, and the output is the 3D model data of the virtual forest sent to the user's device.

[0339] Step 3:

[0340] A user launches a forest growing game on a device and starts a new game session. The device saves the initial settings and user information and displays the virtual forest environment. The input is the 3D model data and the user's initial settings information, and the output is the displayed virtual forest environment.

[0341] Step 4:

[0342] The user device activates the emotion engine to collect the user's emotional data. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice in real time to determine their emotional state. The input is camera video and audio data, and the output is analyzed emotional data.

[0343] Step 5:

[0344] The user terminal dynamically changes the user interface based on the emotion data. For example, if it detects that the user is feeling stressed, it changes the interface to simplify operation. The input is the detected emotion data, and the output is the changed user interface.

[0345] Step 6:

[0346] Users perform management operations such as planting and thinning trees in the virtual forest environment. The terminal records these operations as management operation data. The input is the user's operation, and the output is the recorded management operation data.

[0347] Step 7:

[0348] The user terminal periodically transmits management operation data and emotion data to the server. Data transmission is performed securely using the HTTP protocol. The input is the collected management operation data and emotion data, and the output is the packaged data sent to the server.

[0349] Step 8:

[0350] The server analyzes the received management operation data and emotion data using an artificial intelligence model. Specifically, it uses TensorFlow and PyTorch to evaluate efficient forest management methods, profitability, and user satisfaction. The input is packaged data, and the output is the analysis results.

[0351] Step 9:

[0352] The server consolidates the analysis results and provides them to the public organization while simultaneously providing feedback to the user. Specifically, the analysis results are provided to the public organization via email or dashboard, and displayed to the user as in-game messages or notifications. The input is the analysis results, and the output is the public organization's data and feedback messages.

[0353] 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.

[0354] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0355] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0356] [Second embodiment]

[0357] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0358] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0359] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0360] 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.

[0361] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0362] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0363] 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. 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.

[0364] 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.

[0365] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0366] 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.

[0367] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0368] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0369] System Overview

[0370] This invention is a system that provides a "forestry development game" aimed at improving the efficiency and profitability of forest management. This system collects weather and topographical data from external sources and generates a virtual forest environment based on that data. The user operates this virtual forest environment using a terminal, planting trees and thinning them. The user's operation data is sent from the terminal to a server, where it is analyzed using artificial intelligence. The analysis results are provided to public organizations and are also used as feedback to the user.

[0371] Program processing

[0372] 1. Data Collection and Storage

[0373] Server: Weather data is obtained from an external weather data provider via API. Topographical data is also obtained. This data is stored in a database on the server.

[0374] Example: The server retrieves data in JSON format from a weather data provider, parses it, and stores it in a database.

[0375] 2. Generating the Game Environment

[0376] Server: Generates a virtual forest environment based on collected weather and terrain data. The generated virtual forest environment is sent to the user's device and displayed by the game engine.

[0377] Example: The server integrates weather and terrain data to generate a 3D model of a forest environment and delivers it to the user's device.

[0378] 3. User Gameplay

[0379] User device: The user launches the game and plants and thins trees in the virtual forest environment. Operational data is collected as the user interacts with the user interface.

[0380] Example: When a user plants a tree at a specific location, the location and type of tree are recorded as operation data.

[0381] 4. Data collection and transmission

[0382] User device: The user's gameplay operation data is periodically sent to the server using the HTTP protocol.

[0383] Example: When a user plays a game for a certain period of time, all of the gameplay data is packaged and sent to the server.

[0384] 5. Data Analysis

[0385] Server: Analyzes the received operational data using an artificial intelligence model. The analysis uses an efficiency model and a profitability model, and integrates the results obtained from each model.

[0386] Example: The server receives user operation data, evaluates training efficiency using an efficiency model, and evaluates predicted revenue using a profitability model.

[0387] 6. Providing analysis results and feedback

[0388] Server: Provides analysis results to public organizations, sends data via email and API, and displays the analysis results as feedback to users to help them improve their gameplay.

[0389] Example: The server provides recommendations for efficient forest management to public organizations via email and dashboards, while simultaneously displaying analysis results to users as in-game messages.

[0390] As described above, this invention is a system that collects meteorological and topographical data, generates a virtual forest environment, allows users to operate it, and analyzes the operational data to derive efficient forest management methods. The analysis results will be provided to public organizations and used as feedback to users, contributing to the sustainable management of Japan's forests.

[0391] The processing flow will be explained below.

[0392] Step 1: Data collection

[0393] Server: Obtains weather data from an external weather data provider via API, parses the data in JSON format, and stores it in the database. Also obtains terrain data from a terrain data provider and stores it in the database.

[0394] Step 2: Creating a virtual forest environment

[0395] Server: Integrates collected weather and terrain data to generate a 3D model of the virtual forest environment, which is then sent to the user's device and displayed in the game engine.

[0396] Step 3: Launch the game

[0397] User: Launches the forest growing game on the device and starts a new game session. The user's information and preferences are saved and the virtual forest environment is displayed.

[0398] Step 4: Planting the tree

[0399] User: Through the user interface, the user plants a tree at a specific location within the virtual forest environment. At this time, information about the type of tree and the planting location is recorded as operational data.

[0400] Step 5: Thinning

[0401] User: Performs thinning operations to thin out trees in a specific area. The location of the thinning is also recorded as operation data.

[0402] Step 6: Collecting operational data

[0403] User device: A series of user operation data is periodically collected, including records of tree planting and thinning operations.

[0404] Step 7: Sending operation data to the server

[0405] User terminal: The collected operation data is sent to the server using the HTTP protocol, ensuring secure communication.

[0406] Step 8: Artificial intelligence analysis of the data

[0407] Server: Analyzes the received user operation data using AI models. The efficiency model evaluates training efficiency, and the profitability model evaluates predicted profits.

[0408] Step 9: Integrating the analysis results

[0409] Server: Integrates the results of the efficiency and profitability models to produce the final analysis, which includes optimal forest management practices and profitable practices.

[0410] Step 10: Provide analysis results

[0411] Server: Provides analysis results to public organizations. Data is sent via email or API and used as useful information for public organizations.

[0412] Step 11: View feedback

[0413] Server: Display the analysis results as feedback to the user, providing information that the user can use in their next play through in-game messages and notifications.

[0414] These are the specific processing steps of this system. It clearly shows how the server, terminal, and user are involved and operate at each step.

[0415] Example 1

[0416] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0417] Traditional forest management involves directly operating and managing actual forests, which makes it difficult to evaluate efficient management methods and profitability. Another issue is that data collection and analysis takes a lot of time and money, making it difficult to obtain immediate feedback. This makes management tasks such as forest cultivation and thinning inefficient, making sustainable forest management difficult.

[0418] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0419] In this invention, the server includes means for collecting environmental data from an external data providing service, means for generating a virtual forest environment using the environmental data and transmitting it to the user's terminal, and means for analyzing management operation data by the user using artificial intelligence analysis technology and evaluating efficient forest management methods and profitability. This allows the user to try out efficient management methods in the virtual environment and receive immediate feedback on the analysis results.

[0420] "External data provision service" is an online service that provides environmental information such as meteorological data and topographical data.

[0421] "Environmental data" refers to information about a specific region or environment, such as meteorological data or topographical data.

[0422] A "virtual forest environment" is a three-dimensional model of a forest recreated on a computer based on collected environmental data.

[0423] "User interface means" is a collective term for input devices and software that allow users to operate and manage the virtual forest environment.

[0424] "Administrative operation data" is data relating to operations performed by a user within the virtual forest environment.

[0425] A "user terminal" is an electronic device such as a computer, smartphone, or tablet that is operated by a user.

[0426] "Artificial intelligence analysis technology" is a technology that uses machine learning and data mining to analyze data and extract useful information.

[0427] "Efficient forest management methods" are forest management techniques that achieve maximum results with minimal resources and effort.

[0428] A "means for assessing profitability" is a means for analyzing operational data related to forest management and predicting future profits based on that data.

[0429] "Means for providing analysis results" refers to the method for communicating the information obtained through the analysis to public organizations and users.

[0430] This invention is a system for improving the efficiency and profitability of forest management. The system collects meteorological and topographical data from external data providers and generates a virtual forest environment based on that data. Users use their devices to perform operations such as planting and thinning trees in this virtual forest environment, and the operation data is sent to a server. The server then analyzes the data using artificial intelligence analysis technology and provides the analysis results to public organizations and users.

[0431] Data collection and storage

[0432] The server collects weather data from external data providers (for example, weather data providers) using APIs. It also collects terrain data and stores this data in a database. Specifically, the server obtains data in JSON format, parses it, and stores it.

[0433] Game environment generation

[0434] The server generates a virtual forest environment based on the stored weather and terrain data. It first analyzes the terrain data and then integrates weather data to create a realistic virtual environment. The generated virtual forest environment is sent to the user's device as a 3D model and displayed in a game engine such as Unity.

[0435] User gameplay

[0436] Users launch the game on their device and access the virtual forest environment. They perform operations within the game, such as planting and thinning trees, and these operation data are stored on the device in real time. Users can select specific areas using their fingers or a mouse to plant or cut down trees. Each time they perform an operation, their location information and operation details are recorded in the device's local storage.

[0437] Data collection and transmission

[0438] The user device periodically sends the collected operation data to the server. The HTTP protocol is used for transmission, and the data is sent in encrypted format. All operation data performed by the user over the course of one hour is compiled in JSON format and sent to the server. Transmission is secure using HTTPS.

[0439] Data analysis

[0440] The server uses AI analysis technology to analyze the received operational data. Two main models are used for the analysis: an efficiency model and a profitability model, and the results of each are integrated. The server uses AI frameworks such as TensorFlow to evaluate the efficiency of forest management using the efficiency model and to predict future profits using the profitability model.

[0441] Providing analysis results and feedback

[0442] The server provides the analysis results to the public organization and sends the data via email or API. It also displays the analysis results as feedback to the user, helping them to operate the virtual forest. Specifically, the server provides the public organization with recommendations for efficient forest management methods via email or dashboard, and simultaneously displays the analysis results to the user as in-game messages and pop-up notifications.

[0443] Prompt Sentence Examples

[0444] "I'm thinking of a system that allows users to plant trees in a virtual forest environment, collects and analyzes their growth data, and then proposes efficient forest management. I'd like to know the detailed processing steps and analysis methods of this system."

[0445] As described above, the present invention achieves efficient and sustainable forest management through a series of processes that begin with the collection of external data, then the generation of a virtual forest environment, the collection and analysis of user operation data, and finally the provision of the analysis results to public organizations and users.

[0446] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0447] Step 1: Data collection and storage

[0448] The server accesses the API of an external data provider to obtain weather and terrain data. It sends an API request as input and receives weather and terrain data in JSON format as output. Specifically, the server runs periodic jobs to collect environmental data such as temperature, precipitation, and wind speed from the OpenWeatherMap API, parses it, and stores it in a database.

[0449] Step 2: Generate the game environment

[0450] The server generates a virtual forest environment based on weather and terrain data stored in a database. It reads the weather and terrain data in the database as input and sends a 3D model of the virtual forest to the user's device as output. Specifically, the server uses 3D modeling software to recreate the terrain and integrates weather parameters onto it. The generated 3D model is exported in JSON format and sent to the user's device.

[0451] Step 3: User gameplay

[0452] The user launches the game on their device and accesses the virtual forest environment. The input is a 3D model of the virtual forest sent from the server, and the output is generated as user operation data. Specifically, the user plants or thins trees with their finger or mouse, and the operation data (location information and tree type) is recorded in real time in the device's local storage.

[0453] Step 4: Collect and send data

[0454] The user's device periodically sends the collected operation data to the server. The operation data stored in the device's local storage is read as input, and the operation data sent to the server is obtained as output. Specifically, after the user plays the game for one hour, all operation data collected during that time is compiled in JSON format and sent to the server in an encrypted state via HTTPS.

[0455] Step 5: Analyze the data

[0456] The server analyzes the received operation data using artificial intelligence analysis technology. It reads the operation data sent to the server as input and generates an analysis result of efficient forest management methods and profitability as output. Specifically, the server uses an AI framework such as TensorFlow to evaluate cultivation efficiency with an efficiency model and predict future profits with a profitability model. These results are integrated to obtain the analysis result.

[0457] Step 6: Providing analysis results and feedback

[0458] The server provides the analysis results to the public organization and displays them as feedback to the user. The generated analysis results are used as input, and the data is sent to the recipient as output, displaying the feedback. Specifically, the server sends the analysis results to the public organization via email or dashboard, and displays them to the user in the form of a pop-up or message within the game.

[0459] (Application example 1)

[0460] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0461] Current autonomous vehicles collect environmental data around roads, but there is a lack of systems that can effectively utilize this data to help manage and monitor the environment around roads. Furthermore, there is a need for a method to propose efficient and profitable green space management methods based on the collected data and provide feedback to public organizations and users. As a result, the efficiency and profitability of forest management and environmental protection are declining.

[0462] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0463] In this invention, the server includes means for collecting environmental data from external sources, means for generating a virtual forest environment using the environmental data, user interface means for users to perform operations to manage the virtual forest, means for collecting management operation data by users, means for transmitting the management operation data to the server, artificial intelligence analysis means for analyzing the management operation data and evaluating efficient forest management methods and profitability, means for providing the analysis results to public organizations, means for generating a virtual environment of the road surroundings using environmental data collected by an automated driving device, and means for collecting environmental data of the road surroundings in real time. This makes it possible to analyze the collected environmental data in real time, derive efficient environmental management methods, and quickly provide feedback to public organizations and users.

[0464] An "external source" is an external data providing service that provides weather data and topographical data collected by the server.

[0465] "Environmental data" refers to information about the natural environment, such as temperature, humidity, wind speed, and topographical information.

[0466] A "virtual forest environment" is a virtual forest simulation generated based on collected environmental data.

[0467] "User interface means" refers to a screen or operating device that allows the user to perform operations to manage the virtual forest.

[0468] "Management operation data" refers to data that includes the operations performed by the user in the virtual forest environment.

[0469] "Server" means a central computer that receives and stores environmental data from external sources, analyzes it, and distributes the results.

[0470] "Artificial intelligence analysis means" refers to AI technology that analyzes collected management operation data and evaluates efficient forest management methods and profitability.

[0471] "Public bodies" are local governments and government agencies responsible for forest management and environmental protection.

[0472] An "autonomous driving device" is a vehicle with autonomous driving capabilities that is equipped with sensors and communication functions for collecting environmental data.

[0473] "Means of collecting data in real time" refers to a mechanism by which the autonomous driving device continuously acquires environmental data while driving and transmits it to a server.

[0474] "Means of providing feedback on analysis results to users" refers to a system that notifies users of the analysis results via in-game messages or the dashboard.

[0475] The present invention relates to a system that utilizes data collected by autonomous vehicles to assist in the management and monitoring of roadside environments. The system includes a series of processes that collect environmental data from external sources, generate a virtual forest environment, allow users to manage the virtual forest, collect and analyze operational data, and evaluate efficient forest management methods and profitability.

[0476] First, the server collects environmental data from external sources. Specifically, sensors, LiDAR (Light Detection and Ranging) technology, and GPS are used. Weather data (temperature, humidity, wind speed, etc.) and terrain data along the road where the vehicle is traveling are collected in real time and stored on the server. Specific hardware used for this data collection includes LiDAR, GPS modules, and temperature and humidity sensors.

[0477] The server then uses the collected environmental data to generate a virtual forest environment using a database (e.g., MySQL) and an analysis platform (e.g., TensorFlow). The generated virtual forest environment is displayed on the user's device as a 3D model or other format.

[0478] The user operates within the virtual forest environment through a terminal, such as an in-car display, smart glasses, or an in-car VR headset (e.g., Oculus Rift). The user visually checks the virtual forest environment and inputs operational data.

[0479] Next, the management operation data collected by users is periodically sent to a server. Data is transmitted securely using HTTP / HTTPS. The server receives this data and performs artificial intelligence analysis. The AI ​​analysis tools used here are TensorFlow and PyTorch. The server uses this analysis to evaluate efficient forest management methods and projected revenue.

[0480] The analysis results are provided to public organizations and users. Public organizations are provided with the results via dashboard, email, or API. Users are provided with feedback on the analysis results on the in-car display, along with instructions and recommended actions for the next trip.

[0481] As an example, we present a prompt that prompts the user to indicate whether new planting is needed at a particular location.

[0482] Input data:

[0483] Current location (Latitude: 35.6895, Longitude: 139.6917)

[0484] Current temperature: 22.3℃, humidity: 65%, wind speed: 8.5m / s

[0485] question:

[0486] Is new planting needed at this point?

[0487] Based on the data collected, please propose a forest management strategy for the next five years.

[0488] In response to this prompt, the AI ​​analysis tool will suggest appropriate management methods and strategies, and the results will be fed back to the user. This makes it possible to analyze collected environmental data in real time, derive efficient environmental management methods, and provide rapid feedback to public organizations and users.

[0489] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0490] Step 1:

[0491] The server collects environmental data from external sources. It uses sensors, LiDAR, and GPS installed in the autonomous vehicle to obtain weather data (temperature, humidity, wind speed) and terrain data in real time while driving. The input data is raw data from the sensors, and the output data is formatted data stored in the server's database. Specific operations include obtaining data from the sensors, converting the data format, and storing it in the database.

[0492] Step 2:

[0493] The server generates a virtual forest environment based on the collected environmental data. It reads the environmental data stored in the database and generates a 3D model using TensorFlow. The input data is the collected weather and terrain data, and the output data is a 3D model of the virtual forest environment. Specific operations include data reading, model generation, and distribution of the model to the user's device.

[0494] Step 3:

[0495] The user operates within the virtual forest environment using a terminal. The operation data by the user is collected and sent to the server. The input data is the management operation performed by the user (e.g., planting or thinning a tree), and the output data is the operation data sent to the server. Specific operations include operating the user interface, recording the operation data, and sending the data.

[0496] Step 4:

[0497] The server periodically receives and analyzes user management operation data. TensorFlow and PyTorch are used for the analysis to evaluate efficient forest management methods and profitability. The input data is user operation data and collected environmental data, and the output data is the analysis results (e.g., efficiency evaluation, predicted profits). Specific operations include receiving data, inputting it into the model, running the analysis, and generating results.

[0498] Step 5:

[0499] The server provides the analysis results to public organizations and users and provides feedback. Data is sent to public organizations via dashboard, email, or API, and feedback is given to users via in-car displays or devices. The input data is the analysis results, and the output data is the notification content sent to public organizations and users. Specific operations include sending the result data, generating a feedback message, and distributing it to the user's device.

[0500] Step 6:

[0501] The user performs the next management operation based on the provided feedback. The feedback includes efficient planting locations, predicted revenue, and recommended management methods. The input data is the feedback from the server, and the output data is the user's new management operation. Specific actions include checking the feedback, adjusting the operation plan, and executing the new management operation.

[0502] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0503] System Overview

[0504] This invention combines a system that provides a "forest cultivation game" aimed at improving the efficiency and profitability of forest management with an emotion engine that recognizes the user's emotions. The system collects weather and topographical data from external sources and generates a virtual forest environment based on that data. The user uses a terminal to operate this virtual forest environment, planting trees and thinning them. The user's operation data and emotion data are sent from the terminal to a server, where they are analyzed using artificial intelligence. The analysis results are provided to public organizations and also used as feedback to the user.

[0505] Program processing

[0506] 1. Data Collection and Storage

[0507] Server: Obtains weather data from an external weather data provider via API, parses the data in JSON format, and stores it in the database. Also obtains terrain data from a terrain data provider and stores it in the database.

[0508] Example: The server retrieves data in JSON format from a weather data provider, parses it, and stores it in a database.

[0509] 2. Creation of a virtual forest environment

[0510] Server: Generates a 3D model of a virtual forest environment based on collected weather and terrain data. This virtual forest environment is then sent to the user's device and displayed in the game engine.

[0511] Example: The server integrates weather and terrain data to generate a 3D model of a forest environment and delivers it to the user's device.

[0512] 3. Launch the game

[0513] User: Launches the forest growing game on the device and starts a new game session. The user's information and preferences are saved and the virtual forest environment is displayed.

[0514] Example: When a user launches a game, the device displays a configured virtual forest environment.

[0515] 4. Starting the emotion engine and collecting emotion data

[0516] User device: Recognizes user emotions in real time using an emotion engine. Analyzes the user's emotional state from data collected through input devices such as cameras and microphones, and collects it as emotion data.

[0517] Example: A user device uses a camera and microphone to analyze the user's facial expressions and tone of voice to gather information about their emotional state.

[0518] 5. Dynamic interface changes based on emotions

[0519] User terminal: Dynamically change the user interface based on the user's emotional state. For example, if the user is feeling stressed, simplify operations.

[0520] Example: If you recognize that the user is confused, provide more detailed guidance or easier options.

[0521] 6. Performing Management Operations

[0522] User: Plants and thins trees in the virtual forest environment through the user interface. Information on the type of tree, planting location, and thinning location is recorded as operational data.

[0523] Example: When a user plants a tree at a specific location, the location and type of tree are recorded as operation data.

[0524] 7. Collection and transmission of operational and emotional data

[0525] User device: User operation data and emotion data are periodically collected and sent to the server. Data is sent using the HTTP protocol, ensuring secure communication.

[0526] Example: When a user plays a game for a certain period of time, all operation data and emotional data from that time is packaged and sent to the server.

[0527] 8. Artificial Intelligence Analysis of Data

[0528] Server: The received operation data and emotional data are analyzed using an AI model. The efficiency model evaluates training efficiency, and the profitability model evaluates predicted revenue. In addition, by utilizing emotional data in the analysis, the user's stress and satisfaction are reflected in the evaluation.

[0529] Example: The server integrates and analyzes user operation data and emotional data to evaluate training efficiency, profitability, and user satisfaction.

[0530] 9. Integration and provision of analysis results

[0531] Server: Integrates the results of the efficiency and profitability models to produce the final analysis, which includes optimal forest management practices, profitable methods, and user satisfaction. The analysis results are provided to public organizations and fed back to users through in-game messages and notifications.

[0532] Example: The server provides the analysis results to the public body via email or dashboard, and also displays them to the user as an in-game message.

[0533] The above is the specific processing of this system that combines the emotion engine. It clearly shows how the server, terminal, and user are involved and operate at each step. The introduction of the emotion engine not only increases user satisfaction, but also makes it possible to provide more accurate analysis results.

[0534] The processing flow will be explained below.

[0535] Step 1: Data collection

[0536] Server: Obtains weather data from an external weather data provider via API. Parses the obtained data in JSON format and saves it in a database. Similarly, obtains terrain data from a terrain data provider and saves it in a database.

[0537] Example: The server retrieves data from a weather data provider using the "daily_weather_data" endpoint and stores it in the "weather_db". It also retrieves terrain data from the "terrain_data" endpoint and stores it in the "terrain_db".

[0538] Step 2: Creating a virtual forest environment

[0539] Server: Integrates collected weather and terrain data to generate a 3D model of the virtual forest environment. Sends this model to the user's device and prepares it for display in the game engine.

[0540] Example: The server reads data from "weather_db" and "terrain_db", generates a virtual forest using 3D modeling software, and saves the results in "virtual_forest_model".

[0541] Step 3: Launch the game

[0542] User: Launches the forest growing game on the device and starts a new game session. The initial user information and virtual forest environment are loaded.

[0543] Example: When a user opens the app, it downloads the "virtual_forest_model" from the server and starts the game with the user's initial settings.

[0544] Step 4: Launching the emotion engine and collecting emotion data

[0545] User device: Recognizes user emotions in real time using an emotion engine. Analyzes data collected through input devices such as cameras and microphones, and stores it as emotion data.

[0546] Example: The user device activates the camera and uses a facial expression recognition algorithm to analyze the user's emotions (e.g., happiness, sadness, surprise), and stores them in "emotion_data."

[0547] Step 5: Dynamically modifying the interface based on emotions

[0548] User terminal: Dynamically change the user interface based on the user's perceived emotional state. For example, simplify operations or provide positive feedback if the user is feeling stressed.

[0549] Example: If the user is perceived as confused, provide on-screen instructions or hints.

[0550] Step 6: Performing administrative operations

[0551] User: Through the user interface, the user can plant and thin trees in the virtual forest environment. Based on these operations, the type of tree, the location of the planting, and the location of the thinning are recorded as operational data.

[0552] Example: When a user plants an oak tree at a specific location, the location and type of tree are recorded in "operation_data."

[0553] Step 7: Collect and transmit operational and emotional data

[0554] User device: User operation data and emotion data are periodically collected and sent to the server. Data is sent using the HTTP protocol to ensure secure communication.

[0555] Example: After a user plays the game for a certain period of time, "operation_data" and "emotion_data" are sent to the server.

[0556] Step 8: Artificial intelligence analysis of the data

[0557] Server: The received operation data and emotion data are analyzed using an AI model. The efficiency model evaluates training efficiency, and the profitability model evaluates predicted revenue. In addition, by including emotion data in the analysis, evaluations take user experience into account.

[0558] Example: The server inputs "operation_data" and "emotion_data" into "efficiency_model" and "revenue_model" to calculate training efficiency, revenue forecast, and user satisfaction.

[0559] Step 9: Integrate and deliver the analysis results

[0560] Server: Integrates the analytical results obtained from the efficiency model and the profitability model to derive the final analytical results, which include optimal forest management methods, profitable practices, and improved user satisfaction. The analytical results are provided to public organizations and simultaneously displayed as feedback to users.

[0561] Example: The server displays the analytics results in the "forest_management_dashboard" and makes them available to public organizations, and also provides feedback to users via in-game notifications.

[0562] The above is the specific processing of this system that combines the emotion engine. It clearly shows how the server, terminal, and user are involved and operate at each step. The introduction of the emotion engine not only increases user satisfaction, but also makes it possible to provide more accurate analysis results.

[0563] Example 2

[0564] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0565] Current forest management systems lack the means to optimize interfaces that take user emotions into account and to improve user satisfaction. Furthermore, the analysis of collected data does not involve comprehensive evaluation, including user emotional data, which prevents sufficient improvements in management efficiency and profitability. Furthermore, there are also insufficient means to appropriately provide the results of these analyses to both public organizations and users.

[0566] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting environmental data from external sources, means for generating a virtual forest environment using the environmental data, a user interface means for users to perform operations to manage the virtual forest, means for collecting user emotion data in real time using an emotion engine, means for collecting user management operation data and emotion data, means for transmitting the management operation data and emotion data to the server, artificial intelligence analysis means for analyzing the management operation data and emotion data and evaluating efficient forest management methods and profitability, and means for providing the analysis results to public organizations. This allows the interface to be optimized based on the user emotion data, improving management efficiency and profitability. It also provides appropriate feedback to both public organizations and users.

[0567] An "external source" is a data source that exists outside the system and provides weather data, terrain data, and the like.

[0568] "Environmental data" refers to various information necessary for generating and managing a virtual forest environment, such as weather data and topographical data collected by the system from external sources.

[0569] A "virtual forest environment" is a 3D model of a virtual forest that is generated based on collected environmental data and that users can manage.

[0570] "User interface means" means software or hardware that allows a user to perform operations to manage a virtual forest.

[0571] An "emotion engine" is software or a system that uses input devices such as a camera or microphone to recognize and analyze a user's emotional state in real time.

[0572] "Emotional data" refers to information about the user's emotional state determined from facial expressions, tone of voice, etc. collected by the emotion engine.

[0573] "Administrative Operation Data" means information related to operations, such as planting or thinning trees, performed by a user within a virtual forest environment.

[0574] A "server" is a computer system that performs various processes such as data collection, analysis, and providing results.

[0575] "Artificial intelligence analysis means" refers to an analysis system that uses artificial intelligence to analyze collected management operation data and emotional data and evaluate efficient forest management methods and profitability.

[0576] "Analysis results" refers to information regarding efficient forest management methods, profitable methods, and user satisfaction obtained by the artificial intelligence analysis means analyzing management operation data and emotion data.

[0577] "Public organizations" refer to organizations that use the system's analysis results, such as public institutions and local governments.

[0578] This invention provides a "forestry cultivation game" system for improving the efficiency and profitability of forest management. This system aims to increase user satisfaction and provide more accurate analysis results by incorporating an emotion engine that recognizes the user's emotions.

[0579] The system mainly consists of the following elements: a server, a user terminal, an emotion engine, and an external data source.

[0580] Data collection and storage

[0581] The server obtains data from external weather data and terrain data services via APIs, parses it in JSON format, and stores it in a database. Specifically, it uses the OpenWeatherMap API and terrain data API, and MongoDB as the database.

[0582] Generating a virtual forest environment

[0583] The server uses Unity to generate a 3D model of the virtual forest environment based on the collected weather and terrain data, and then uses Blender's API to generate the 3D model, import it into a Unity project, and send it to the user's device.

[0584] Launching the game

[0585] A user launches the smartphone app or PC software and starts a new game session. User information and initial settings are sent to the server, and a virtual forest environment built with Unity is displayed on the device.

[0586] Activating the emotion engine and collecting emotion data

[0587] The user device activates the emotion engine and uses the camera and microphone to recognize the user's emotional state in real time. It uses FaceAPI and Watson Tone Analyzer to analyze facial expressions and tone of voice and store the emotional data.

[0588] Dynamic interface modification based on emotions

[0589] The user device dynamically changes the interface based on the user's emotional state. For example, if it detects confusion, it will respond by providing more guidance and easier operation options.

[0590] Performing administrative operations

[0591] Users plant and thin trees in the virtual forest environment, and the information is recorded as operational data. Specifically, when a user clicks on a specific spot to plant a tree, the location information and type of tree are recorded.

[0592] Collecting and transmitting operational and emotional data

[0593] The user device periodically collects operational and emotional data and sends it to the server using HTTPS. The data is compiled in JSON format to ensure secure communication.

[0594] AI analysis of data

[0595] The server analyzes the received operation data and sentiment data using artificial intelligence. A machine learning model built using Scikit-learn and TensorFlow evaluates management efficiency, profitability, and user satisfaction.

[0596] Integration and provision of analysis results

[0597] The server combines the analytical results from the efficiency model and the profitability model to derive the final results, which are then provided to the public organization and also provide feedback to users via Unity's notification system.

[0598] Prompt Sentence Examples

[0599] An example of a specific prompt to be input to the generative AI model is as follows:

[0600] "Based on user operation and sentiment data, suggest optimal forest management methods. If users are confused, consider easier operation options."

[0601] "Please use the following dataset to evaluate training efficiency and profitability, including user operation history and emotional state."

[0602] As described above, this system can achieve efficient forest management and improved profitability through the optimization of the user interface using an emotion engine and comprehensive data analysis. Furthermore, the effectiveness of the system can be enhanced by providing appropriate feedback to both public organizations and users.

[0603] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0604] Step 1: Data collection and storage

[0605] The server obtains data from external weather data and terrain data providers via API. The input includes authentication information such as an API key. The server parses the obtained data in JSON format and stores it in a MongoDB database. Specifically, the server calls the OpenWeatherMap API and terrain data API to obtain data and store it in the database.

[0606] Step 2: Creating a virtual forest environment

[0607] The server uses Unity to generate a 3D model of the virtual forest environment based on the collected weather and terrain data. The input includes weather and terrain data, and the output includes a Unity 3D model. Specifically, the server runs a Python script that uses Blender's API to generate the 3D model and import it into the Unity project.

[0608] Step 3: Launch the game

[0609] A user launches a smartphone app or PC software and starts a new game session. Input includes a user ID and password, and output includes a display of the virtual forest environment and session settings. Specifically, a user launches the app, enters their ID and password on the login screen, and a 3D model retrieved from the server is displayed.

[0610] Step 4: Launching the emotion engine and collecting emotion data

[0611] The user device runs an emotion engine and uses the camera and microphone to collect the user's emotional state in real time. The input includes the user's facial expression and voice information, and the output includes emotion data. Specifically, the device uses FaceAPI to analyze the video from the camera and Watson Tone Analyzer to analyze the tone of voice, and then stores the emotion data.

[0612] Step 5: Dynamically modifying the interface based on emotions

[0613] The user device dynamically changes the interface based on the user's recognized emotional state. The input includes emotional data, and the output includes interface modifications. Specifically, the device analyzes the emotional data and, for example, if it recognizes that the user is confused, it increases the number of guide displays and simplifies operation.

[0614] Step 6: Performing administrative operations

[0615] Users plant and thin trees in a virtual forest environment. The input includes information about the user's operations, and the output includes information about the type and location of the trees as a result of the operations. Specifically, the user selects a specific area using a mouse or touch display and performs operations to plant trees, and this information is recorded as operation data.

[0616] Step 7: Collect and transmit operational and emotional data

[0617] The user device periodically collects operation data and emotion data and sends them to the server using HTTPS. The input includes operation data and emotion data, and the output includes the results of transmission to the server. Specifically, the device compiles the data in JSON format at regular intervals and sends it to the server using a secure communication protocol.

[0618] Step 8: Artificial intelligence analysis of the data

[0619] The server uses artificial intelligence to analyze the received operation data and emotion data. The input includes operation data and emotion data, and the output includes the analysis results. Specifically, the server builds a machine learning model using Scikit-learn and TensorFlow to evaluate management efficiency, profitability, and user satisfaction.

[0620] Step 9: Integrate and deliver the analysis results

[0621] The server integrates the analytical results obtained from the efficiency model and the profitability model to derive the final result. The input includes the analytical results, and the output includes the integrated final result. Specifically, the server compiles the analytical results in JSON format and sends them to the public organization's web application via API, and also provides feedback to users using Unity Notifications.

[0622] (Application example 2)

[0623] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0624] Conventional forest management systems and user interfaces were not designed with user emotions in mind, resulting in problems such as poor user experience and operational efficiency. Furthermore, data analysis and feedback based on user emotions were not provided, resulting in insufficient evaluation of efficient forest management methods and profitability. Furthermore, the analysis results provided to public organizations did not reflect user emotions, making it difficult to establish forest management methods that fully considered actual user experience.

[0625] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting environmental data from external sources, means for generating a virtual forest environment using the environmental data, means for collecting user emotion data, means for collecting user management operation data and emotion data, artificial intelligence analysis means for analyzing the management operation data and emotion data and evaluating efficient forest management methods and profitability, and means for dynamically changing the virtual environment and user interface based on the analysis results. This enables the provision of an operation interface that takes the user's emotional state into consideration and advanced data analysis using emotion data. Furthermore, since the user's experience is reflected in the analysis results provided to public organizations, it becomes possible to establish efficient forest management methods based on actual user behavior.

[0626] "Environmental Data" is data collected from external sources, including weather and terrain data.

[0627] A "virtual forest environment" is a 3D model of a forest environment generated based on collected environmental data.

[0628] "User Interface" means the interface through which a user operates and manages the virtual forest environment.

[0629] "Emotion data" is data that indicates the emotional state of the user, obtained from their facial expressions and voice.

[0630] "Management operation data" refers to data relating to operations such as planting and thinning trees that a user performs within a virtual forest environment.

[0631] The "server" is a computer system that collects environmental data, generates a virtual forest environment, and collects and analyzes management operation data and emotion data.

[0632] "Artificial intelligence analysis means" refers to an algorithm or program that analyzes management operation data and emotion data and evaluates efficient forest management methods and profitability.

[0633] "Public bodies" are bodies, including government agencies and local governments, involved in forest management.

[0634] The "dynamic change means" is a means having the function of adaptively changing the virtual environment and user interface based on the analysis results.

[0635] The present invention is a forest management simulation system that recognizes and utilizes the emotions of a user. Specific examples for carrying out the present invention will be described below.

[0636] System Configuration

[0637] 1. Data Collection and Storage:

[0638] The server acquires weather and terrain data through APIs to collect environmental data from external sources, and stores them in a database in JSON format. It uses weather data provider services and terrain data provider services.

[0639] Example: The server retrieves data in JSON format from a weather data provider, parses it, and stores it in a database.

[0640] 2. Creating a Virtual Forest Environment:

[0641] The server runs a program to generate a 3D model of a virtual forest environment based on the collected weather and topographical data, and transmits this data to the user's device. The 3D model is generated using a 3D modeling tool such as Unity.

[0642] Example: The server integrates weather and terrain data to generate a 3D model of a virtual forest environment and delivers it to the user's device.

[0643] 3. Launch the game:

[0644] The user launches the forest growing game on their device and starts a new game session. Initial settings and user information are saved, and the virtual forest environment is displayed.

[0645] Example: When a user launches a game, a virtual forest environment is displayed on the device.

[0646] 4. Launching the emotion engine and collecting emotion data:

[0647] The user device uses an emotion engine to recognize the user's emotions in real time, analyzing facial expressions and tone of voice from data collected through the camera and microphone, and collecting emotional data.

[0648] Example: A user device uses a camera and microphone to analyze the user's facial expressions and tone of voice to gather information about their emotional state.

[0649] 5. Dynamic interface changes based on emotions:

[0650] The user device has the ability to dynamically change the user interface based on the recognized emotional state, for example, simplifying the operation of the interface if it detects that the user is feeling stressed.

[0651] Example: If you recognize that a user is confused, offer more detailed guidance or easier options.

[0652] 6. Performing administrative operations:

[0653] Users plant and thin trees in the virtual forest environment, and these operations are recorded as management operation data.

[0654] Example: When a user plants a tree at a specific location, the location information and the type of tree are recorded as operation data.

[0655] 7. Collection and Transmission of Operational and Emotional Data:

[0656] The user terminal periodically collects the user's management operation data and emotion data and transmits them to the server using the HTTP protocol.

[0657] Example: When a user plays a game for a certain period of time, the operation data and emotional data from that time are packaged and sent to the server.

[0658] 8. Artificial intelligence analysis of data:

[0659] The server then analyzes the received operation data and emotion data using an artificial intelligence model. This analysis uses AI platforms such as TensorFlow and PyTorch. Along with models that evaluate efficient forest management methods and profitability, the server also uses emotion data to evaluate user satisfaction.

[0660] Example: The server integrates and analyzes user operation data and emotional data to evaluate training efficiency, profitability, and user satisfaction.

[0661] 9. Integration and provision of analytical results:

[0662] The server aggregates the results of the analysis and provides the final results to the public body, while also providing feedback to the user, which is displayed as in-game messages and notifications.

[0663] Example: The server provides the analysis results to public organizations via email or dashboard, and displays them to users as in-game messages.

[0664] Prompt Sentence Examples

[0665] For example, in a factory training simulator, the following prompts are fed to a generative AI model:

[0666] "You are a design engineer for a robot operation simulator. Your task is to create a program that will enable the system to recognize the emotions of factory workers in real time and dynamically change the work interface during training. As a concrete example of emotion analysis, if the user is nervous, simplify the interface and display a guide to help the user calm down."

[0667] In this way, detailed design and refinement can be carried out using generative AI models.

[0668] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0669] Step 1:

[0670] The server collects environmental data from external sources. Specifically, it obtains JSON-formatted data from weather data providers and terrain data providers via APIs, parses it, and stores it in a database. The input is weather data and terrain data obtained from the external APIs, and the output is the parsed environmental data stored in the database.

[0671] Step 2:

[0672] The server generates a 3D model of the virtual forest environment based on the collected environmental data. Specifically, it designs the virtual forest environment using a 3D modeling tool such as Unity and delivers the generated 3D model to the user's device. The input is the saved environmental data, and the output is the 3D model data of the virtual forest sent to the user's device.

[0673] Step 3:

[0674] A user launches a forest growing game on a device and starts a new game session. The device saves the initial settings and user information and displays the virtual forest environment. The input is the 3D model data and the user's initial settings information, and the output is the displayed virtual forest environment.

[0675] Step 4:

[0676] The user device activates the emotion engine to collect the user's emotional data. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice in real time to determine their emotional state. The input is camera video and audio data, and the output is analyzed emotional data.

[0677] Step 5:

[0678] The user terminal dynamically changes the user interface based on the emotion data. For example, if it detects that the user is feeling stressed, it changes the interface to simplify operation. The input is the detected emotion data, and the output is the changed user interface.

[0679] Step 6:

[0680] Users perform management operations such as planting and thinning trees in the virtual forest environment. The terminal records these operations as management operation data. The input is the user's operation, and the output is the recorded management operation data.

[0681] Step 7:

[0682] The user terminal periodically transmits management operation data and emotion data to the server. Data transmission is performed securely using the HTTP protocol. The input is the collected management operation data and emotion data, and the output is the packaged data sent to the server.

[0683] Step 8:

[0684] The server analyzes the received management operation data and emotion data using an artificial intelligence model. Specifically, it uses TensorFlow and PyTorch to evaluate efficient forest management methods, profitability, and user satisfaction. The input is packaged data, and the output is the analysis results.

[0685] Step 9:

[0686] The server consolidates the analysis results and provides them to the public organization while simultaneously providing feedback to the user. Specifically, the analysis results are provided to the public organization via email or dashboard, and displayed to the user as in-game messages or notifications. The input is the analysis results, and the output is the public organization's data and feedback messages.

[0687] 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.

[0688] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0689] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0690] [Third embodiment]

[0691] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0692] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0693] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0694] 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.

[0695] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0696] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0697] 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. 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.

[0698] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0699] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0700] 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.

[0701] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0702] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0703] System Overview

[0704] This invention is a system that provides a "forestry development game" aimed at improving the efficiency and profitability of forest management. This system collects weather and topographical data from external sources and generates a virtual forest environment based on that data. The user operates this virtual forest environment using a terminal, planting trees and thinning them. The user's operation data is sent from the terminal to a server, where it is analyzed using artificial intelligence. The analysis results are provided to public organizations and are also used as feedback to the user.

[0705] Program processing

[0706] 1. Data Collection and Storage

[0707] Server: Weather data is obtained from an external weather data provider via API. Topographical data is also obtained. This data is stored in a database on the server.

[0708] Example: The server retrieves data in JSON format from a weather data provider, parses it, and stores it in a database.

[0709] 2. Generating the Game Environment

[0710] Server: Generates a virtual forest environment based on collected weather and terrain data. The generated virtual forest environment is sent to the user's device and displayed by the game engine.

[0711] Example: The server integrates weather and terrain data to generate a 3D model of a forest environment and delivers it to the user's device.

[0712] 3. User Gameplay

[0713] User device: The user launches the game and plants and thins trees in the virtual forest environment. Operational data is collected as the user interacts with the user interface.

[0714] Example: When a user plants a tree at a specific location, the location and type of tree are recorded as operation data.

[0715] 4. Data collection and transmission

[0716] User device: The user's gameplay operation data is periodically sent to the server using the HTTP protocol.

[0717] Example: When a user plays a game for a certain period of time, all of the gameplay data is packaged and sent to the server.

[0718] 5. Data Analysis

[0719] Server: Analyzes the received operational data using an artificial intelligence model. The analysis uses an efficiency model and a profitability model, and integrates the results obtained from each model.

[0720] Example: The server receives user operation data, evaluates training efficiency using an efficiency model, and evaluates predicted revenue using a profitability model.

[0721] 6. Providing analysis results and feedback

[0722] Server: Provides analysis results to public organizations, sends data via email and API, and displays the analysis results as feedback to users to help them improve their gameplay.

[0723] Example: The server provides recommendations for efficient forest management to public organizations via email and dashboards, while simultaneously displaying analysis results to users as in-game messages.

[0724] As described above, this invention is a system that collects meteorological and topographical data, generates a virtual forest environment, allows users to operate it, and analyzes the operational data to derive efficient forest management methods. The analysis results will be provided to public organizations and used as feedback to users, contributing to the sustainable management of Japan's forests.

[0725] The processing flow will be explained below.

[0726] Step 1: Data collection

[0727] Server: Obtains weather data from an external weather data provider via API, parses the data in JSON format, and stores it in the database. Also obtains terrain data from a terrain data provider and stores it in the database.

[0728] Step 2: Creating a virtual forest environment

[0729] Server: Integrates collected weather and terrain data to generate a 3D model of the virtual forest environment, which is then sent to the user's device and displayed in the game engine.

[0730] Step 3: Launch the game

[0731] User: Launches the forest growing game on the device and starts a new game session. The user's information and preferences are saved and the virtual forest environment is displayed.

[0732] Step 4: Planting the tree

[0733] User: Through the user interface, the user plants a tree at a specific location within the virtual forest environment. At this time, information about the type of tree and the planting location is recorded as operational data.

[0734] Step 5: Thinning

[0735] User: Performs thinning operations to thin out trees in a specific area. The location of the thinning is also recorded as operation data.

[0736] Step 6: Collecting operational data

[0737] User device: A series of user operation data is periodically collected, including records of tree planting and thinning operations.

[0738] Step 7: Sending operation data to the server

[0739] User terminal: The collected operation data is sent to the server using the HTTP protocol, ensuring secure communication.

[0740] Step 8: Artificial intelligence analysis of the data

[0741] Server: Analyzes the received user operation data using AI models. The efficiency model evaluates training efficiency, and the profitability model evaluates predicted profits.

[0742] Step 9: Integrating the analysis results

[0743] Server: Integrates the results of the efficiency and profitability models to produce the final analysis, which includes optimal forest management practices and profitable practices.

[0744] Step 10: Provide analysis results

[0745] Server: Provides analysis results to public organizations. Data is sent via email or API and used as useful information for public organizations.

[0746] Step 11: View feedback

[0747] Server: Display the analysis results as feedback to the user, providing information that the user can use in their next play through in-game messages and notifications.

[0748] These are the specific processing steps of this system. It clearly shows how the server, terminal, and user are involved and operate at each step.

[0749] Example 1

[0750] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0751] Traditional forest management involves directly operating and managing actual forests, which makes it difficult to evaluate efficient management methods and profitability. Another issue is that data collection and analysis takes a lot of time and money, making it difficult to obtain immediate feedback. This makes management tasks such as forest cultivation and thinning inefficient, making sustainable forest management difficult.

[0752] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0753] In this invention, the server includes means for collecting environmental data from an external data providing service, means for generating a virtual forest environment using the environmental data and transmitting it to the user's terminal, and means for analyzing management operation data by the user using artificial intelligence analysis technology and evaluating efficient forest management methods and profitability. This allows the user to try out efficient management methods in the virtual environment and receive immediate feedback on the analysis results.

[0754] "External data provision service" is an online service that provides environmental information such as meteorological data and topographical data.

[0755] "Environmental data" refers to information about a specific region or environment, such as meteorological data or topographical data.

[0756] A "virtual forest environment" is a three-dimensional model of a forest recreated on a computer based on collected environmental data.

[0757] "User interface means" is a collective term for input devices and software that allow users to operate and manage the virtual forest environment.

[0758] "Administrative operation data" is data relating to operations performed by a user within the virtual forest environment.

[0759] A "user terminal" is an electronic device such as a computer, smartphone, or tablet that is operated by a user.

[0760] "Artificial intelligence analysis technology" is a technology that uses machine learning and data mining to analyze data and extract useful information.

[0761] "Efficient forest management methods" are forest management techniques that achieve maximum results with minimal resources and effort.

[0762] A "means for assessing profitability" is a means for analyzing operational data related to forest management and predicting future profits based on that data.

[0763] "Means for providing analysis results" refers to the method for communicating the information obtained through the analysis to public organizations and users.

[0764] This invention is a system for improving the efficiency and profitability of forest management. The system collects meteorological and topographical data from external data providers and generates a virtual forest environment based on that data. Users use their devices to perform operations such as planting and thinning trees in this virtual forest environment, and the operation data is sent to a server. The server then analyzes the data using artificial intelligence analysis technology and provides the analysis results to public organizations and users.

[0765] Data collection and storage

[0766] The server collects weather data from external data providers (for example, weather data providers) using APIs. It also collects terrain data and stores this data in a database. Specifically, the server obtains data in JSON format, parses it, and stores it.

[0767] Game environment generation

[0768] The server generates a virtual forest environment based on the stored weather and terrain data. It first analyzes the terrain data and then integrates weather data to create a realistic virtual environment. The generated virtual forest environment is sent to the user's device as a 3D model and displayed in a game engine such as Unity.

[0769] User gameplay

[0770] Users launch the game on their device and access the virtual forest environment. They perform operations within the game, such as planting and thinning trees, and these operation data are stored on the device in real time. Users can select specific areas using their fingers or a mouse to plant or cut down trees. Each time they perform an operation, their location information and operation details are recorded in the device's local storage.

[0771] Data collection and transmission

[0772] The user device periodically sends the collected operation data to the server. The HTTP protocol is used for transmission, and the data is sent in encrypted format. All operation data performed by the user over the course of one hour is compiled in JSON format and sent to the server. Transmission is secure using HTTPS.

[0773] Data analysis

[0774] The server uses AI analysis technology to analyze the received operational data. Two main models are used for the analysis: an efficiency model and a profitability model, and the results of each are integrated. The server uses AI frameworks such as TensorFlow to evaluate the efficiency of forest management using the efficiency model and to predict future profits using the profitability model.

[0775] Providing analysis results and feedback

[0776] The server provides the analysis results to the public organization and sends the data via email or API. It also displays the analysis results as feedback to the user, helping them to operate the virtual forest. Specifically, the server provides the public organization with recommendations for efficient forest management methods via email or dashboard, and simultaneously displays the analysis results to the user as in-game messages and pop-up notifications.

[0777] Prompt Sentence Examples

[0778] "I'm thinking of a system that allows users to plant trees in a virtual forest environment, collects and analyzes their growth data, and then proposes efficient forest management. I'd like to know the detailed processing steps and analysis methods of this system."

[0779] As described above, the present invention achieves efficient and sustainable forest management through a series of processes that begin with the collection of external data, then the generation of a virtual forest environment, the collection and analysis of user operation data, and finally the provision of the analysis results to public organizations and users.

[0780] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0781] Step 1: Data collection and storage

[0782] The server accesses the API of an external data provider to obtain weather and terrain data. It sends an API request as input and receives weather and terrain data in JSON format as output. Specifically, the server runs periodic jobs to collect environmental data such as temperature, precipitation, and wind speed from the OpenWeatherMap API, parses it, and stores it in a database.

[0783] Step 2: Generate the game environment

[0784] The server generates a virtual forest environment based on weather and terrain data stored in a database. It reads the weather and terrain data in the database as input and sends a 3D model of the virtual forest to the user's device as output. Specifically, the server uses 3D modeling software to recreate the terrain and integrates weather parameters onto it. The generated 3D model is exported in JSON format and sent to the user's device.

[0785] Step 3: User gameplay

[0786] The user launches the game on their device and accesses the virtual forest environment. The input is a 3D model of the virtual forest sent from the server, and the output is generated as user operation data. Specifically, the user plants or thins trees with their finger or mouse, and the operation data (location information and tree type) is recorded in real time in the device's local storage.

[0787] Step 4: Collect and send data

[0788] The user's device periodically sends the collected operation data to the server. The operation data stored in the device's local storage is read as input, and the operation data sent to the server is obtained as output. Specifically, after the user plays the game for one hour, all operation data collected during that time is compiled in JSON format and sent to the server in an encrypted state via HTTPS.

[0789] Step 5: Analyze the data

[0790] The server analyzes the received operation data using artificial intelligence analysis technology. It reads the operation data sent to the server as input and generates an analysis result of efficient forest management methods and profitability as output. Specifically, the server uses an AI framework such as TensorFlow to evaluate cultivation efficiency with an efficiency model and predict future profits with a profitability model. These results are integrated to obtain the analysis result.

[0791] Step 6: Providing analysis results and feedback

[0792] The server provides the analysis results to the public organization and displays them as feedback to the user. The generated analysis results are used as input, and the data is sent to the recipient as output, displaying the feedback. Specifically, the server sends the analysis results to the public organization via email or dashboard, and displays them to the user in the form of a pop-up or message within the game.

[0793] (Application example 1)

[0794] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0795] Current autonomous vehicles collect environmental data around roads, but there is a lack of systems that can effectively utilize this data to help manage and monitor the environment around roads. Furthermore, there is a need for a method to propose efficient and profitable green space management methods based on the collected data and provide feedback to public organizations and users. As a result, the efficiency and profitability of forest management and environmental protection are declining.

[0796] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0797] In this invention, the server includes means for collecting environmental data from external sources, means for generating a virtual forest environment using the environmental data, user interface means for users to perform operations to manage the virtual forest, means for collecting management operation data by users, means for transmitting the management operation data to the server, artificial intelligence analysis means for analyzing the management operation data and evaluating efficient forest management methods and profitability, means for providing the analysis results to public organizations, means for generating a virtual environment of the road surroundings using environmental data collected by an automated driving device, and means for collecting environmental data of the road surroundings in real time. This makes it possible to analyze the collected environmental data in real time, derive efficient environmental management methods, and quickly provide feedback to public organizations and users.

[0798] An "external source" is an external data providing service that provides weather data and topographical data collected by the server.

[0799] "Environmental data" refers to information about the natural environment, such as temperature, humidity, wind speed, and topographical information.

[0800] A "virtual forest environment" is a virtual forest simulation generated based on collected environmental data.

[0801] "User interface means" refers to a screen or operating device that allows the user to perform operations to manage the virtual forest.

[0802] "Management operation data" refers to data that includes the operations performed by the user in the virtual forest environment.

[0803] "Server" means a central computer that receives and stores environmental data from external sources, analyzes it, and distributes the results.

[0804] "Artificial intelligence analysis means" refers to AI technology that analyzes collected management operation data and evaluates efficient forest management methods and profitability.

[0805] "Public bodies" are local governments and government agencies responsible for forest management and environmental protection.

[0806] An "autonomous driving device" is a vehicle with autonomous driving capabilities that is equipped with sensors and communication functions for collecting environmental data.

[0807] "Means of collecting data in real time" refers to a mechanism by which the autonomous driving device continuously acquires environmental data while driving and transmits it to a server.

[0808] "Means of providing feedback on analysis results to users" refers to a system that notifies users of the analysis results via in-game messages or the dashboard.

[0809] The present invention relates to a system that utilizes data collected by autonomous vehicles to assist in the management and monitoring of roadside environments. The system includes a series of processes that collect environmental data from external sources, generate a virtual forest environment, allow users to manage the virtual forest, collect and analyze operational data, and evaluate efficient forest management methods and profitability.

[0810] First, the server collects environmental data from external sources. Specifically, sensors, LiDAR (Light Detection and Ranging) technology, and GPS are used. Weather data (temperature, humidity, wind speed, etc.) and terrain data along the road where the vehicle is traveling are collected in real time and stored on the server. Specific hardware used for this data collection includes LiDAR, GPS modules, and temperature and humidity sensors.

[0811] The server then uses the collected environmental data to generate a virtual forest environment using a database (e.g., MySQL) and an analysis platform (e.g., TensorFlow). The generated virtual forest environment is displayed on the user's device as a 3D model or other format.

[0812] The user operates within the virtual forest environment through a terminal, such as an in-car display, smart glasses, or an in-car VR headset (e.g., Oculus Rift). The user visually checks the virtual forest environment and inputs operational data.

[0813] Next, the management operation data collected by users is periodically sent to a server. Data is transmitted securely using HTTP / HTTPS. The server receives this data and performs artificial intelligence analysis. The AI ​​analysis tools used here are TensorFlow and PyTorch. The server uses this analysis to evaluate efficient forest management methods and projected revenue.

[0814] The analysis results are provided to public organizations and users. Public organizations are provided with the results via dashboard, email, or API. Users are provided with feedback on the analysis results on the in-car display, along with instructions and recommended actions for the next trip.

[0815] As an example, we present a prompt that prompts the user to indicate whether new planting is needed at a particular location.

[0816] Input data:

[0817] Current location (Latitude: 35.6895, Longitude: 139.6917)

[0818] Current temperature: 22.3℃, humidity: 65%, wind speed: 8.5m / s

[0819] question:

[0820] Is new planting needed at this point?

[0821] Based on the data collected, please propose a forest management strategy for the next five years.

[0822] In response to this prompt, the AI ​​analysis tool will suggest appropriate management methods and strategies, and the results will be fed back to the user. This makes it possible to analyze collected environmental data in real time, derive efficient environmental management methods, and provide rapid feedback to public organizations and users.

[0823] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0824] Step 1:

[0825] The server collects environmental data from external sources. It uses sensors, LiDAR, and GPS installed in the autonomous vehicle to obtain weather data (temperature, humidity, wind speed) and terrain data in real time while driving. The input data is raw data from the sensors, and the output data is formatted data stored in the server's database. Specific operations include obtaining data from the sensors, converting the data format, and storing it in the database.

[0826] Step 2:

[0827] The server generates a virtual forest environment based on the collected environmental data. It reads the environmental data stored in the database and generates a 3D model using TensorFlow. The input data is the collected weather and terrain data, and the output data is a 3D model of the virtual forest environment. Specific operations include data reading, model generation, and distribution of the model to the user's device.

[0828] Step 3:

[0829] The user operates within the virtual forest environment using a terminal. The operation data by the user is collected and sent to the server. The input data is the management operation performed by the user (e.g., planting or thinning a tree), and the output data is the operation data sent to the server. Specific operations include operating the user interface, recording the operation data, and sending the data.

[0830] Step 4:

[0831] The server periodically receives and analyzes user management operation data. TensorFlow and PyTorch are used for the analysis to evaluate efficient forest management methods and profitability. The input data is user operation data and collected environmental data, and the output data is the analysis results (e.g., efficiency evaluation, predicted profits). Specific operations include receiving data, inputting it into the model, running the analysis, and generating results.

[0832] Step 5:

[0833] The server provides the analysis results to public organizations and users and provides feedback. Data is sent to public organizations via dashboard, email, or API, and feedback is given to users via in-car displays or devices. The input data is the analysis results, and the output data is the notification content sent to public organizations and users. Specific operations include sending the result data, generating a feedback message, and distributing it to the user's device.

[0834] Step 6:

[0835] The user performs the next management operation based on the provided feedback. The feedback includes efficient planting locations, predicted revenue, and recommended management methods. The input data is the feedback from the server, and the output data is the user's new management operation. Specific actions include checking the feedback, adjusting the operation plan, and executing the new management operation.

[0836] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0837] System Overview

[0838] This invention combines a system that provides a "forest cultivation game" aimed at improving the efficiency and profitability of forest management with an emotion engine that recognizes the user's emotions. The system collects weather and topographical data from external sources and generates a virtual forest environment based on that data. The user uses a terminal to operate this virtual forest environment, planting trees and thinning them. The user's operation data and emotion data are sent from the terminal to a server, where they are analyzed using artificial intelligence. The analysis results are provided to public organizations and also used as feedback to the user.

[0839] Program processing

[0840] 1. Data Collection and Storage

[0841] Server: Obtains weather data from an external weather data provider via API, parses the data in JSON format, and stores it in the database. Also obtains terrain data from a terrain data provider and stores it in the database.

[0842] Example: The server retrieves data in JSON format from a weather data provider, parses it, and stores it in a database.

[0843] 2. Creation of a virtual forest environment

[0844] Server: Generates a 3D model of a virtual forest environment based on collected weather and terrain data. This virtual forest environment is then sent to the user's device and displayed in the game engine.

[0845] Example: The server integrates weather and terrain data to generate a 3D model of a forest environment and delivers it to the user's device.

[0846] 3. Launch the game

[0847] User: Launches the forest growing game on the device and starts a new game session. The user's information and preferences are saved and the virtual forest environment is displayed.

[0848] Example: When a user launches a game, the device displays a configured virtual forest environment.

[0849] 4. Starting the emotion engine and collecting emotion data

[0850] User device: Recognizes user emotions in real time using an emotion engine. Analyzes the user's emotional state from data collected through input devices such as cameras and microphones, and collects it as emotion data.

[0851] Example: A user device uses a camera and microphone to analyze the user's facial expressions and tone of voice to gather information about their emotional state.

[0852] 5. Dynamic interface changes based on emotions

[0853] User terminal: Dynamically change the user interface based on the user's emotional state. For example, if the user is feeling stressed, simplify operations.

[0854] Example: If you recognize that the user is confused, provide more detailed guidance or easier options.

[0855] 6. Performing Management Operations

[0856] User: Plants and thins trees in the virtual forest environment through the user interface. Information on the type of tree, planting location, and thinning location is recorded as operational data.

[0857] Example: When a user plants a tree at a specific location, the location and type of tree are recorded as operation data.

[0858] 7. Collection and transmission of operational and emotional data

[0859] User device: User operation data and emotion data are periodically collected and sent to the server. Data is sent using the HTTP protocol, ensuring secure communication.

[0860] Example: When a user plays a game for a certain period of time, all operation data and emotional data from that time is packaged and sent to the server.

[0861] 8. Artificial Intelligence Analysis of Data

[0862] Server: The received operation data and emotional data are analyzed using an AI model. The efficiency model evaluates training efficiency, and the profitability model evaluates predicted revenue. In addition, by utilizing emotional data in the analysis, the user's stress and satisfaction are reflected in the evaluation.

[0863] Example: The server integrates and analyzes user operation data and emotional data to evaluate training efficiency, profitability, and user satisfaction.

[0864] 9. Integration and provision of analysis results

[0865] Server: Integrates the results of the efficiency and profitability models to produce the final analysis, which includes optimal forest management practices, profitable methods, and user satisfaction. The analysis results are provided to public organizations and fed back to users through in-game messages and notifications.

[0866] Example: The server provides the analysis results to the public body via email or dashboard, and also displays them to the user as an in-game message.

[0867] The above is the specific processing of this system that combines the emotion engine. It clearly shows how the server, terminal, and user are involved and operate at each step. The introduction of the emotion engine not only increases user satisfaction, but also makes it possible to provide more accurate analysis results.

[0868] The processing flow will be explained below.

[0869] Step 1: Data collection

[0870] Server: Obtains weather data from an external weather data provider via API. Parses the obtained data in JSON format and saves it in a database. Similarly, obtains terrain data from a terrain data provider and saves it in a database.

[0871] Example: The server retrieves data from a weather data provider using the "daily_weather_data" endpoint and stores it in the "weather_db". It also retrieves terrain data from the "terrain_data" endpoint and stores it in the "terrain_db".

[0872] Step 2: Creating a virtual forest environment

[0873] Server: Integrates collected weather and terrain data to generate a 3D model of the virtual forest environment. Sends this model to the user's device and prepares it for display in the game engine.

[0874] Example: The server reads data from "weather_db" and "terrain_db", generates a virtual forest using 3D modeling software, and saves the results in "virtual_forest_model".

[0875] Step 3: Launch the game

[0876] User: Launches the forest growing game on the device and starts a new game session. The initial user information and virtual forest environment are loaded.

[0877] Example: When a user opens the app, it downloads the "virtual_forest_model" from the server and starts the game with the user's initial settings.

[0878] Step 4: Launching the emotion engine and collecting emotion data

[0879] User device: Recognizes user emotions in real time using an emotion engine. Analyzes data collected through input devices such as cameras and microphones, and stores it as emotion data.

[0880] Example: The user device activates the camera and uses a facial expression recognition algorithm to analyze the user's emotions (e.g., happiness, sadness, surprise), and stores them in "emotion_data."

[0881] Step 5: Dynamically modifying the interface based on emotions

[0882] User terminal: Dynamically change the user interface based on the user's perceived emotional state. For example, simplify operations or provide positive feedback if the user is feeling stressed.

[0883] Example: If the user is perceived as confused, provide on-screen instructions or hints.

[0884] Step 6: Performing administrative operations

[0885] User: Through the user interface, the user can plant and thin trees in the virtual forest environment. Based on these operations, the type of tree, the location of the planting, and the location of the thinning are recorded as operational data.

[0886] Example: When a user plants an oak tree at a specific location, the location and type of tree are recorded in "operation_data."

[0887] Step 7: Collect and transmit operational and emotional data

[0888] User device: User operation data and emotion data are periodically collected and sent to the server. Data is sent using the HTTP protocol to ensure secure communication.

[0889] Example: After a user plays the game for a certain period of time, "operation_data" and "emotion_data" are sent to the server.

[0890] Step 8: Artificial intelligence analysis of the data

[0891] Server: The received operation data and emotion data are analyzed using an AI model. The efficiency model evaluates training efficiency, and the profitability model evaluates predicted revenue. In addition, by including emotion data in the analysis, evaluations take user experience into account.

[0892] Example: The server inputs "operation_data" and "emotion_data" into "efficiency_model" and "revenue_model" to calculate training efficiency, revenue forecast, and user satisfaction.

[0893] Step 9: Integrate and deliver the analysis results

[0894] Server: Integrates the analytical results obtained from the efficiency model and the profitability model to derive the final analytical results, which include optimal forest management methods, profitable practices, and improved user satisfaction. The analytical results are provided to public organizations and simultaneously displayed as feedback to users.

[0895] Example: The server displays the analytics results in the "forest_management_dashboard" and makes them available to public organizations, and also provides feedback to users via in-game notifications.

[0896] The above is the specific processing of this system that combines the emotion engine. It clearly shows how the server, terminal, and user are involved and operate at each step. The introduction of the emotion engine not only increases user satisfaction, but also makes it possible to provide more accurate analysis results.

[0897] Example 2

[0898] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0899] Current forest management systems lack the means to optimize interfaces that take user emotions into account and to improve user satisfaction. Furthermore, the analysis of collected data does not involve comprehensive evaluation, including user emotional data, which prevents sufficient improvements in management efficiency and profitability. Furthermore, there are also insufficient means to appropriately provide the results of these analyses to both public organizations and users.

[0900] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting environmental data from external sources, means for generating a virtual forest environment using the environmental data, a user interface means for users to perform operations to manage the virtual forest, means for collecting user emotion data in real time using an emotion engine, means for collecting user management operation data and emotion data, means for transmitting the management operation data and emotion data to the server, artificial intelligence analysis means for analyzing the management operation data and emotion data and evaluating efficient forest management methods and profitability, and means for providing the analysis results to public organizations. This allows the interface to be optimized based on the user emotion data, improving management efficiency and profitability. It also provides appropriate feedback to both public organizations and users.

[0901] An "external source" is a data source that exists outside the system and provides weather data, terrain data, and the like.

[0902] "Environmental data" refers to various information necessary for generating and managing a virtual forest environment, such as weather data and topographical data collected by the system from external sources.

[0903] A "virtual forest environment" is a 3D model of a virtual forest that is generated based on collected environmental data and that users can manage.

[0904] "User interface means" means software or hardware that allows a user to perform operations to manage a virtual forest.

[0905] An "emotion engine" is software or a system that uses input devices such as a camera or microphone to recognize and analyze a user's emotional state in real time.

[0906] "Emotional data" refers to information about the user's emotional state determined from facial expressions, tone of voice, etc. collected by the emotion engine.

[0907] "Administrative Operation Data" means information related to operations, such as planting or thinning trees, performed by a user within a virtual forest environment.

[0908] A "server" is a computer system that performs various processes such as data collection, analysis, and providing results.

[0909] "Artificial intelligence analysis means" refers to an analysis system that uses artificial intelligence to analyze collected management operation data and emotional data and evaluate efficient forest management methods and profitability.

[0910] "Analysis results" refers to information regarding efficient forest management methods, profitable methods, and user satisfaction obtained by the artificial intelligence analysis means analyzing management operation data and emotion data.

[0911] "Public organizations" refer to organizations that use the system's analysis results, such as public institutions and local governments.

[0912] This invention provides a "forestry cultivation game" system for improving the efficiency and profitability of forest management. This system aims to increase user satisfaction and provide more accurate analysis results by incorporating an emotion engine that recognizes the user's emotions.

[0913] The system mainly consists of the following elements: a server, a user terminal, an emotion engine, and an external data source.

[0914] Data collection and storage

[0915] The server obtains data from external weather data and terrain data services via APIs, parses it in JSON format, and stores it in a database. Specifically, it uses the OpenWeatherMap API and terrain data API, and MongoDB as the database.

[0916] Generating a virtual forest environment

[0917] The server uses Unity to generate a 3D model of the virtual forest environment based on the collected weather and terrain data, and then uses Blender's API to generate the 3D model, import it into a Unity project, and send it to the user's device.

[0918] Launching the game

[0919] A user launches the smartphone app or PC software and starts a new game session. User information and initial settings are sent to the server, and a virtual forest environment built with Unity is displayed on the device.

[0920] Activating the emotion engine and collecting emotion data

[0921] The user device activates the emotion engine and uses the camera and microphone to recognize the user's emotional state in real time. It uses FaceAPI and Watson Tone Analyzer to analyze facial expressions and tone of voice and store the emotional data.

[0922] Dynamic interface modification based on emotions

[0923] The user device dynamically changes the interface based on the user's emotional state. For example, if it detects confusion, it will respond by providing more guidance and easier operation options.

[0924] Performing administrative operations

[0925] Users plant and thin trees in the virtual forest environment, and the information is recorded as operational data. Specifically, when a user clicks on a specific spot to plant a tree, the location information and type of tree are recorded.

[0926] Collecting and transmitting operational and emotional data

[0927] The user device periodically collects operational and emotional data and sends it to the server using HTTPS. The data is compiled in JSON format to ensure secure communication.

[0928] AI analysis of data

[0929] The server analyzes the received operation data and sentiment data using artificial intelligence. A machine learning model built using Scikit-learn and TensorFlow evaluates management efficiency, profitability, and user satisfaction.

[0930] Integration and provision of analysis results

[0931] The server combines the analytical results from the efficiency model and the profitability model to derive the final results, which are then provided to the public organization and also provide feedback to users via Unity's notification system.

[0932] Prompt Sentence Examples

[0933] An example of a specific prompt to be input to the generative AI model is as follows:

[0934] "Based on user operation and sentiment data, suggest optimal forest management methods. If users are confused, consider easier operation options."

[0935] "Please use the following dataset to evaluate training efficiency and profitability, including user operation history and emotional state."

[0936] As described above, this system can achieve efficient forest management and improved profitability through the optimization of the user interface using an emotion engine and comprehensive data analysis. Furthermore, the effectiveness of the system can be enhanced by providing appropriate feedback to both public organizations and users.

[0937] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0938] Step 1: Data collection and storage

[0939] The server obtains data from external weather data and terrain data providers via API. The input includes authentication information such as an API key. The server parses the obtained data in JSON format and stores it in a MongoDB database. Specifically, the server calls the OpenWeatherMap API and terrain data API to obtain data and store it in the database.

[0940] Step 2: Creating a virtual forest environment

[0941] The server uses Unity to generate a 3D model of the virtual forest environment based on the collected weather and terrain data. The input includes weather and terrain data, and the output includes a Unity 3D model. Specifically, the server runs a Python script that uses Blender's API to generate the 3D model and import it into the Unity project.

[0942] Step 3: Launch the game

[0943] A user launches a smartphone app or PC software and starts a new game session. Input includes a user ID and password, and output includes a display of the virtual forest environment and session settings. Specifically, a user launches the app, enters their ID and password on the login screen, and a 3D model retrieved from the server is displayed.

[0944] Step 4: Launching the emotion engine and collecting emotion data

[0945] The user device runs an emotion engine and uses the camera and microphone to collect the user's emotional state in real time. The input includes the user's facial expression and voice information, and the output includes emotion data. Specifically, the device uses FaceAPI to analyze the video from the camera and Watson Tone Analyzer to analyze the tone of voice, and then stores the emotion data.

[0946] Step 5: Dynamically modifying the interface based on emotions

[0947] The user device dynamically changes the interface based on the user's recognized emotional state. The input includes emotional data, and the output includes interface modifications. Specifically, the device analyzes the emotional data and, for example, if it recognizes that the user is confused, it increases the number of guide displays and simplifies operation.

[0948] Step 6: Performing administrative operations

[0949] Users plant and thin trees in a virtual forest environment. The input includes information about the user's operations, and the output includes information about the type and location of the trees as a result of the operations. Specifically, the user selects a specific area using a mouse or touch display and performs operations to plant trees, and this information is recorded as operation data.

[0950] Step 7: Collect and transmit operational and emotional data

[0951] The user device periodically collects operation data and emotion data and sends them to the server using HTTPS. The input includes operation data and emotion data, and the output includes the results of transmission to the server. Specifically, the device compiles the data in JSON format at regular intervals and sends it to the server using a secure communication protocol.

[0952] Step 8: Artificial intelligence analysis of the data

[0953] The server uses artificial intelligence to analyze the received operation data and emotion data. The input includes operation data and emotion data, and the output includes the analysis results. Specifically, the server builds a machine learning model using Scikit-learn and TensorFlow to evaluate management efficiency, profitability, and user satisfaction.

[0954] Step 9: Integrate and deliver the analysis results

[0955] The server integrates the analytical results obtained from the efficiency model and the profitability model to derive the final result. The input includes the analytical results, and the output includes the integrated final result. Specifically, the server compiles the analytical results in JSON format and sends them to the public organization's web application via API, and also provides feedback to users using Unity Notifications.

[0956] (Application example 2)

[0957] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0958] Conventional forest management systems and user interfaces were not designed with user emotions in mind, resulting in problems such as poor user experience and operational efficiency. Furthermore, data analysis and feedback based on user emotions were not provided, resulting in insufficient evaluation of efficient forest management methods and profitability. Furthermore, the analysis results provided to public organizations did not reflect user emotions, making it difficult to establish forest management methods that fully considered actual user experience.

[0959] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting environmental data from external sources, means for generating a virtual forest environment using the environmental data, means for collecting user emotion data, means for collecting user management operation data and emotion data, artificial intelligence analysis means for analyzing the management operation data and emotion data and evaluating efficient forest management methods and profitability, and means for dynamically changing the virtual environment and user interface based on the analysis results. This enables the provision of an operation interface that takes the user's emotional state into consideration and advanced data analysis using emotion data. Furthermore, since the user's experience is reflected in the analysis results provided to public organizations, it becomes possible to establish efficient forest management methods based on actual user behavior.

[0960] "Environmental Data" is data collected from external sources, including weather and terrain data.

[0961] A "virtual forest environment" is a 3D model of a forest environment generated based on collected environmental data.

[0962] "User Interface" means the interface through which a user operates and manages the virtual forest environment.

[0963] "Emotion data" is data that indicates the emotional state of the user, obtained from their facial expressions and voice.

[0964] "Management operation data" refers to data relating to operations such as planting and thinning trees that a user performs within a virtual forest environment.

[0965] The "server" is a computer system that collects environmental data, generates a virtual forest environment, and collects and analyzes management operation data and emotion data.

[0966] "Artificial intelligence analysis means" refers to an algorithm or program that analyzes management operation data and emotion data and evaluates efficient forest management methods and profitability.

[0967] "Public bodies" are bodies, including government agencies and local governments, involved in forest management.

[0968] The "dynamic change means" is a means having the function of adaptively changing the virtual environment and user interface based on the analysis results.

[0969] The present invention is a forest management simulation system that recognizes and utilizes the emotions of a user. Specific examples for carrying out the present invention will be described below.

[0970] System Configuration

[0971] 1. Data Collection and Storage:

[0972] The server acquires weather and terrain data through APIs to collect environmental data from external sources, and stores them in a database in JSON format. It uses weather data provider services and terrain data provider services.

[0973] Example: The server retrieves data in JSON format from a weather data provider, parses it, and stores it in a database.

[0974] 2. Creating a Virtual Forest Environment:

[0975] The server runs a program to generate a 3D model of a virtual forest environment based on the collected weather and topographical data, and transmits this data to the user's device. The 3D model is generated using a 3D modeling tool such as Unity.

[0976] Example: The server integrates weather and terrain data to generate a 3D model of a virtual forest environment and delivers it to the user's device.

[0977] 3. Launch the game:

[0978] The user launches the forest growing game on their device and starts a new game session. Initial settings and user information are saved, and the virtual forest environment is displayed.

[0979] Example: When a user launches a game, a virtual forest environment is displayed on the device.

[0980] 4. Launching the emotion engine and collecting emotion data:

[0981] The user device uses an emotion engine to recognize the user's emotions in real time, analyzing facial expressions and tone of voice from data collected through the camera and microphone, and collecting emotional data.

[0982] Example: A user device uses a camera and microphone to analyze the user's facial expressions and tone of voice to gather information about their emotional state.

[0983] 5. Dynamic interface changes based on emotions:

[0984] The user device has the ability to dynamically change the user interface based on the recognized emotional state, for example, simplifying the operation of the interface if it detects that the user is feeling stressed.

[0985] Example: If you recognize that a user is confused, offer more detailed guidance or easier options.

[0986] 6. Performing administrative operations:

[0987] Users plant and thin trees in the virtual forest environment, and these operations are recorded as management operation data.

[0988] Example: When a user plants a tree at a specific location, the location information and the type of tree are recorded as operation data.

[0989] 7. Collection and Transmission of Operational and Emotional Data:

[0990] The user terminal periodically collects the user's management operation data and emotion data and transmits them to the server using the HTTP protocol.

[0991] Example: When a user plays a game for a certain period of time, the operation data and emotional data from that time are packaged and sent to the server.

[0992] 8. Artificial intelligence analysis of data:

[0993] The server then analyzes the received operation data and emotion data using an artificial intelligence model. This analysis uses AI platforms such as TensorFlow and PyTorch. Along with models that evaluate efficient forest management methods and profitability, the server also uses emotion data to evaluate user satisfaction.

[0994] Example: The server integrates and analyzes user operation data and emotional data to evaluate training efficiency, profitability, and user satisfaction.

[0995] 9. Integration and provision of analytical results:

[0996] The server aggregates the results of the analysis and provides the final results to the public body, while also providing feedback to the user, which is displayed as in-game messages and notifications.

[0997] Example: The server provides the analysis results to public organizations via email or dashboard, and displays them to users as in-game messages.

[0998] Prompt Sentence Examples

[0999] For example, in a factory training simulator, the following prompts are fed to a generative AI model:

[1000] "You are a design engineer for a robot operation simulator. Your task is to create a program that will enable the system to recognize the emotions of factory workers in real time and dynamically change the work interface during training. As a concrete example of emotion analysis, if the user is nervous, simplify the interface and display a guide to help the user calm down."

[1001] In this way, detailed design and refinement can be carried out using generative AI models.

[1002] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1003] Step 1:

[1004] The server collects environmental data from external sources. Specifically, it obtains JSON-formatted data from weather data providers and terrain data providers via APIs, parses it, and stores it in a database. The input is weather data and terrain data obtained from the external APIs, and the output is the parsed environmental data stored in the database.

[1005] Step 2:

[1006] The server generates a 3D model of the virtual forest environment based on the collected environmental data. Specifically, it designs the virtual forest environment using a 3D modeling tool such as Unity and delivers the generated 3D model to the user's device. The input is the saved environmental data, and the output is the 3D model data of the virtual forest sent to the user's device.

[1007] Step 3:

[1008] A user launches a forest growing game on a device and starts a new game session. The device saves the initial settings and user information and displays the virtual forest environment. The input is the 3D model data and the user's initial settings information, and the output is the displayed virtual forest environment.

[1009] Step 4:

[1010] The user device activates the emotion engine to collect the user's emotional data. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice in real time to determine their emotional state. The input is camera video and audio data, and the output is analyzed emotional data.

[1011] Step 5:

[1012] The user terminal dynamically changes the user interface based on the emotion data. For example, if it detects that the user is feeling stressed, it changes the interface to simplify operation. The input is the detected emotion data, and the output is the changed user interface.

[1013] Step 6:

[1014] Users perform management operations such as planting and thinning trees in the virtual forest environment. The terminal records these operations as management operation data. The input is the user's operation, and the output is the recorded management operation data.

[1015] Step 7:

[1016] The user terminal periodically transmits management operation data and emotion data to the server. Data transmission is performed securely using the HTTP protocol. The input is the collected management operation data and emotion data, and the output is the packaged data sent to the server.

[1017] Step 8:

[1018] The server analyzes the received management operation data and emotion data using an artificial intelligence model. Specifically, it uses TensorFlow and PyTorch to evaluate efficient forest management methods, profitability, and user satisfaction. The input is packaged data, and the output is the analysis results.

[1019] Step 9:

[1020] The server consolidates the analysis results and provides them to the public organization while simultaneously providing feedback to the user. Specifically, the analysis results are provided to the public organization via email or dashboard, and displayed to the user as in-game messages or notifications. The input is the analysis results, and the output is the public organization's data and feedback messages.

[1021] 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.

[1022] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1023] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1024] [Fourth embodiment]

[1025] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1026] 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.

[1027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1028] 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.

[1029] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1030] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1031] 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. 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.

[1032] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1033] 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.

[1034] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1035] 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.

[1036] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1038] System Overview

[1039] This invention is a system that provides a "forestry development game" aimed at improving the efficiency and profitability of forest management. This system collects weather and topographical data from external sources and generates a virtual forest environment based on that data. The user operates this virtual forest environment using a terminal, planting trees and thinning them. The user's operation data is sent from the terminal to a server, where it is analyzed using artificial intelligence. The analysis results are provided to public organizations and are also used as feedback to the user.

[1040] Program processing

[1041] 1. Data Collection and Storage

[1042] Server: Weather data is obtained from an external weather data provider via API. Topographical data is also obtained. This data is stored in a database on the server.

[1043] Example: The server retrieves data in JSON format from a weather data provider, parses it, and stores it in a database.

[1044] 2. Generating the Game Environment

[1045] Server: Generates a virtual forest environment based on collected weather and terrain data. The generated virtual forest environment is sent to the user's device and displayed by the game engine.

[1046] Example: The server integrates weather and terrain data to generate a 3D model of a forest environment and delivers it to the user's device.

[1047] 3. User Gameplay

[1048] User device: The user launches the game and plants and thins trees in the virtual forest environment. Operational data is collected as the user interacts with the user interface.

[1049] Example: When a user plants a tree at a specific location, the location and type of tree are recorded as operation data.

[1050] 4. Data collection and transmission

[1051] User device: The user's gameplay operation data is periodically sent to the server using the HTTP protocol.

[1052] Example: When a user plays a game for a certain period of time, all of the gameplay data is packaged and sent to the server.

[1053] 5. Data Analysis

[1054] Server: Analyzes the received operational data using an artificial intelligence model. The analysis uses an efficiency model and a profitability model, and integrates the results obtained from each model.

[1055] Example: The server receives user operation data, evaluates training efficiency using an efficiency model, and evaluates predicted revenue using a profitability model.

[1056] 6. Providing analysis results and feedback

[1057] Server: Provides analysis results to public organizations, sends data via email and API, and displays the analysis results as feedback to users to help them improve their gameplay.

[1058] Example: The server provides recommendations for efficient forest management to public organizations via email and dashboards, while simultaneously displaying analysis results to users as in-game messages.

[1059] As described above, this invention is a system that collects meteorological and topographical data, generates a virtual forest environment, allows users to operate it, and analyzes the operational data to derive efficient forest management methods. The analysis results will be provided to public organizations and used as feedback to users, contributing to the sustainable management of Japan's forests.

[1060] The processing flow will be explained below.

[1061] Step 1: Data collection

[1062] Server: Obtains weather data from an external weather data provider via API, parses the data in JSON format, and stores it in the database. Also obtains terrain data from a terrain data provider and stores it in the database.

[1063] Step 2: Creating a virtual forest environment

[1064] Server: Integrates collected weather and terrain data to generate a 3D model of the virtual forest environment, which is then sent to the user's device and displayed in the game engine.

[1065] Step 3: Launch the game

[1066] User: Launches the forest growing game on the device and starts a new game session. The user's information and preferences are saved and the virtual forest environment is displayed.

[1067] Step 4: Planting the tree

[1068] User: Through the user interface, the user plants a tree at a specific location within the virtual forest environment. At this time, information about the type of tree and the planting location is recorded as operational data.

[1069] Step 5: Thinning

[1070] User: Performs thinning operations to thin out trees in a specific area. The location of the thinning is also recorded as operation data.

[1071] Step 6: Collecting operational data

[1072] User device: A series of user operation data is periodically collected, including records of tree planting and thinning operations.

[1073] Step 7: Sending operation data to the server

[1074] User terminal: The collected operation data is sent to the server using the HTTP protocol, ensuring secure communication.

[1075] Step 8: Artificial intelligence analysis of the data

[1076] Server: Analyzes the received user operation data using AI models. The efficiency model evaluates training efficiency, and the profitability model evaluates predicted profits.

[1077] Step 9: Integrating the analysis results

[1078] Server: Integrates the results of the efficiency and profitability models to produce the final analysis, which includes optimal forest management practices and profitable practices.

[1079] Step 10: Provide analysis results

[1080] Server: Provides analysis results to public organizations. Data is sent via email or API and used as useful information for public organizations.

[1081] Step 11: View feedback

[1082] Server: Display the analysis results as feedback to the user, providing information that the user can use in their next play through in-game messages and notifications.

[1083] These are the specific processing steps of this system. It clearly shows how the server, terminal, and user are involved and operate at each step.

[1084] Example 1

[1085] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1086] Traditional forest management involves directly operating and managing actual forests, which makes it difficult to evaluate efficient management methods and profitability. Another issue is that data collection and analysis takes a lot of time and money, making it difficult to obtain immediate feedback. This makes management tasks such as forest cultivation and thinning inefficient, making sustainable forest management difficult.

[1087] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1088] In this invention, the server includes means for collecting environmental data from an external data providing service, means for generating a virtual forest environment using the environmental data and transmitting it to the user's terminal, and means for analyzing management operation data by the user using artificial intelligence analysis technology and evaluating efficient forest management methods and profitability. This allows the user to try out efficient management methods in the virtual environment and receive immediate feedback on the analysis results.

[1089] "External data provision service" is an online service that provides environmental information such as meteorological data and topographical data.

[1090] "Environmental data" refers to information about a specific region or environment, such as meteorological data or topographical data.

[1091] A "virtual forest environment" is a three-dimensional model of a forest recreated on a computer based on collected environmental data.

[1092] "User interface means" is a collective term for input devices and software that allow users to operate and manage the virtual forest environment.

[1093] "Administrative operation data" is data relating to operations performed by a user within the virtual forest environment.

[1094] A "user terminal" is an electronic device such as a computer, smartphone, or tablet that is operated by a user.

[1095] "Artificial intelligence analysis technology" is a technology that uses machine learning and data mining to analyze data and extract useful information.

[1096] "Efficient forest management methods" are forest management techniques that achieve maximum results with minimal resources and effort.

[1097] A "means for assessing profitability" is a means for analyzing operational data related to forest management and predicting future profits based on that data.

[1098] "Means for providing analysis results" refers to the method for communicating the information obtained through the analysis to public organizations and users.

[1099] This invention is a system for improving the efficiency and profitability of forest management. The system collects meteorological and topographical data from external data providers and generates a virtual forest environment based on that data. Users use their devices to perform operations such as planting and thinning trees in this virtual forest environment, and the operation data is sent to a server. The server then analyzes the data using artificial intelligence analysis technology and provides the analysis results to public organizations and users.

[1100] Data collection and storage

[1101] The server collects weather data from external data providers (for example, weather data providers) using APIs. It also collects terrain data and stores this data in a database. Specifically, the server obtains data in JSON format, parses it, and stores it.

[1102] Game environment generation

[1103] The server generates a virtual forest environment based on the stored weather and terrain data. It first analyzes the terrain data and then integrates weather data to create a realistic virtual environment. The generated virtual forest environment is sent to the user's device as a 3D model and displayed in a game engine such as Unity.

[1104] User gameplay

[1105] Users launch the game on their device and access the virtual forest environment. They perform operations within the game, such as planting and thinning trees, and these operation data are stored on the device in real time. Users can select specific areas using their fingers or a mouse to plant or cut down trees. Each time they perform an operation, their location information and operation details are recorded in the device's local storage.

[1106] Data collection and transmission

[1107] The user device periodically sends the collected operation data to the server. The HTTP protocol is used for transmission, and the data is sent in encrypted format. All operation data performed by the user over the course of one hour is compiled in JSON format and sent to the server. Transmission is secure using HTTPS.

[1108] Data analysis

[1109] The server uses AI analysis technology to analyze the received operational data. Two main models are used for the analysis: an efficiency model and a profitability model, and the results of each are integrated. The server uses AI frameworks such as TensorFlow to evaluate the efficiency of forest management using the efficiency model and to predict future profits using the profitability model.

[1110] Providing analysis results and feedback

[1111] The server provides the analysis results to the public organization and sends the data via email or API. It also displays the analysis results as feedback to the user, helping them to operate the virtual forest. Specifically, the server provides the public organization with recommendations for efficient forest management methods via email or dashboard, and simultaneously displays the analysis results to the user as in-game messages and pop-up notifications.

[1112] Prompt Sentence Examples

[1113] "I'm thinking of a system that allows users to plant trees in a virtual forest environment, collects and analyzes their growth data, and then proposes efficient forest management. I'd like to know the detailed processing steps and analysis methods of this system."

[1114] As described above, the present invention achieves efficient and sustainable forest management through a series of processes that begin with the collection of external data, then the generation of a virtual forest environment, the collection and analysis of user operation data, and finally the provision of the analysis results to public organizations and users.

[1115] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1116] Step 1: Data collection and storage

[1117] The server accesses the API of an external data provider to obtain weather and terrain data. It sends an API request as input and receives weather and terrain data in JSON format as output. Specifically, the server runs periodic jobs to collect environmental data such as temperature, precipitation, and wind speed from the OpenWeatherMap API, parses it, and stores it in a database.

[1118] Step 2: Generate the game environment

[1119] The server generates a virtual forest environment based on weather and terrain data stored in a database. It reads the weather and terrain data in the database as input and sends a 3D model of the virtual forest to the user's device as output. Specifically, the server uses 3D modeling software to recreate the terrain and integrates weather parameters onto it. The generated 3D model is exported in JSON format and sent to the user's device.

[1120] Step 3: User gameplay

[1121] The user launches the game on their device and accesses the virtual forest environment. The input is a 3D model of the virtual forest sent from the server, and the output is generated as user operation data. Specifically, the user plants or thins trees with their finger or mouse, and the operation data (location information and tree type) is recorded in real time in the device's local storage.

[1122] Step 4: Collect and send data

[1123] The user's device periodically sends the collected operation data to the server. The operation data stored in the device's local storage is read as input, and the operation data sent to the server is obtained as output. Specifically, after the user plays the game for one hour, all operation data collected during that time is compiled in JSON format and sent to the server in an encrypted state via HTTPS.

[1124] Step 5: Analyze the data

[1125] The server analyzes the received operation data using artificial intelligence analysis technology. It reads the operation data sent to the server as input and generates an analysis result of efficient forest management methods and profitability as output. Specifically, the server uses an AI framework such as TensorFlow to evaluate cultivation efficiency with an efficiency model and predict future profits with a profitability model. These results are integrated to obtain the analysis result.

[1126] Step 6: Providing analysis results and feedback

[1127] The server provides the analysis results to the public organization and displays them as feedback to the user. The generated analysis results are used as input, and the data is sent to the recipient as output, displaying the feedback. Specifically, the server sends the analysis results to the public organization via email or dashboard, and displays them to the user in the form of a pop-up or message within the game.

[1128] (Application example 1)

[1129] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1130] Current autonomous vehicles collect environmental data around roads, but there is a lack of systems that can effectively utilize this data to help manage and monitor the environment around roads. Furthermore, there is a need for a method to propose efficient and profitable green space management methods based on the collected data and provide feedback to public organizations and users. As a result, the efficiency and profitability of forest management and environmental protection are declining.

[1131] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1132] In this invention, the server includes means for collecting environmental data from external sources, means for generating a virtual forest environment using the environmental data, user interface means for users to perform operations to manage the virtual forest, means for collecting management operation data by users, means for transmitting the management operation data to the server, artificial intelligence analysis means for analyzing the management operation data and evaluating efficient forest management methods and profitability, means for providing the analysis results to public organizations, means for generating a virtual environment of the road surroundings using environmental data collected by an automated driving device, and means for collecting environmental data of the road surroundings in real time. This makes it possible to analyze the collected environmental data in real time, derive efficient environmental management methods, and quickly provide feedback to public organizations and users.

[1133] An "external source" is an external data providing service that provides weather data and topographical data collected by the server.

[1134] "Environmental data" refers to information about the natural environment, such as temperature, humidity, wind speed, and topographical information.

[1135] A "virtual forest environment" is a virtual forest simulation generated based on collected environmental data.

[1136] "User interface means" refers to a screen or operating device that allows the user to perform operations to manage the virtual forest.

[1137] "Management operation data" refers to data that includes the operations performed by the user in the virtual forest environment.

[1138] "Server" means a central computer that receives and stores environmental data from external sources, analyzes it, and distributes the results.

[1139] "Artificial intelligence analysis means" refers to AI technology that analyzes collected management operation data and evaluates efficient forest management methods and profitability.

[1140] "Public bodies" are local governments and government agencies responsible for forest management and environmental protection.

[1141] An "autonomous driving device" is a vehicle with autonomous driving capabilities that is equipped with sensors and communication functions for collecting environmental data.

[1142] "Means of collecting data in real time" refers to a mechanism by which the autonomous driving device continuously acquires environmental data while driving and transmits it to a server.

[1143] "Means of providing feedback on analysis results to users" refers to a system that notifies users of the analysis results via in-game messages or the dashboard.

[1144] The present invention relates to a system that utilizes data collected by autonomous vehicles to assist in the management and monitoring of roadside environments. The system includes a series of processes that collect environmental data from external sources, generate a virtual forest environment, allow users to manage the virtual forest, collect and analyze operational data, and evaluate efficient forest management methods and profitability.

[1145] First, the server collects environmental data from external sources. Specifically, sensors, LiDAR (Light Detection and Ranging) technology, and GPS are used. Weather data (temperature, humidity, wind speed, etc.) and terrain data along the road where the vehicle is traveling are collected in real time and stored on the server. Specific hardware used for this data collection includes LiDAR, GPS modules, and temperature and humidity sensors.

[1146] The server then uses the collected environmental data to generate a virtual forest environment using a database (e.g., MySQL) and an analysis platform (e.g., TensorFlow). The generated virtual forest environment is displayed on the user's device as a 3D model or other format.

[1147] The user operates within the virtual forest environment through a terminal, such as an in-car display, smart glasses, or an in-car VR headset (e.g., Oculus Rift). The user visually checks the virtual forest environment and inputs operational data.

[1148] Next, the management operation data collected by users is periodically sent to a server. Data is transmitted securely using HTTP / HTTPS. The server receives this data and performs artificial intelligence analysis. The AI ​​analysis tools used here are TensorFlow and PyTorch. The server uses this analysis to evaluate efficient forest management methods and projected revenue.

[1149] The analysis results are provided to public organizations and users. Public organizations are provided with the results via dashboard, email, or API. Users are provided with feedback on the analysis results on the in-car display, along with instructions and recommended actions for the next trip.

[1150] As an example, we present a prompt that prompts the user to indicate whether new planting is needed at a particular location.

[1151] Input data:

[1152] Current location (Latitude: 35.6895, Longitude: 139.6917)

[1153] Current temperature: 22.3℃, humidity: 65%, wind speed: 8.5m / s

[1154] question:

[1155] Is new planting needed at this point?

[1156] Based on the data collected, please propose a forest management strategy for the next five years.

[1157] In response to this prompt, the AI ​​analysis tool will suggest appropriate management methods and strategies, and the results will be fed back to the user. This makes it possible to analyze collected environmental data in real time, derive efficient environmental management methods, and provide rapid feedback to public organizations and users.

[1158] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1159] Step 1:

[1160] The server collects environmental data from external sources. It uses sensors, LiDAR, and GPS installed in the autonomous vehicle to obtain weather data (temperature, humidity, wind speed) and terrain data in real time while driving. The input data is raw data from the sensors, and the output data is formatted data stored in the server's database. Specific operations include obtaining data from the sensors, converting the data format, and storing it in the database.

[1161] Step 2:

[1162] The server generates a virtual forest environment based on the collected environmental data. It reads the environmental data stored in the database and generates a 3D model using TensorFlow. The input data is the collected weather and terrain data, and the output data is a 3D model of the virtual forest environment. Specific operations include data reading, model generation, and distribution of the model to the user's device.

[1163] Step 3:

[1164] The user operates within the virtual forest environment using a terminal. The operation data by the user is collected and sent to the server. The input data is the management operation performed by the user (e.g., planting or thinning a tree), and the output data is the operation data sent to the server. Specific operations include operating the user interface, recording the operation data, and sending the data.

[1165] Step 4:

[1166] The server periodically receives and analyzes user management operation data. TensorFlow and PyTorch are used for the analysis to evaluate efficient forest management methods and profitability. The input data is user operation data and collected environmental data, and the output data is the analysis results (e.g., efficiency evaluation, predicted profits). Specific operations include receiving data, inputting it into the model, running the analysis, and generating results.

[1167] Step 5:

[1168] The server provides the analysis results to public organizations and users and provides feedback. Data is sent to public organizations via dashboard, email, or API, and feedback is given to users via in-car displays or devices. The input data is the analysis results, and the output data is the notification content sent to public organizations and users. Specific operations include sending the result data, generating a feedback message, and distributing it to the user's device.

[1169] Step 6:

[1170] The user performs the next management operation based on the provided feedback. The feedback includes efficient planting locations, predicted revenue, and recommended management methods. The input data is the feedback from the server, and the output data is the user's new management operation. Specific actions include checking the feedback, adjusting the operation plan, and executing the new management operation.

[1171] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1172] System Overview

[1173] This invention combines a system that provides a "forest cultivation game" aimed at improving the efficiency and profitability of forest management with an emotion engine that recognizes the user's emotions. The system collects weather and topographical data from external sources and generates a virtual forest environment based on that data. The user uses a terminal to operate this virtual forest environment, planting trees and thinning them. The user's operation data and emotion data are sent from the terminal to a server, where they are analyzed using artificial intelligence. The analysis results are provided to public organizations and also used as feedback to the user.

[1174] Program processing

[1175] 1. Data Collection and Storage

[1176] Server: Obtains weather data from an external weather data provider via API, parses the data in JSON format, and stores it in the database. Also obtains terrain data from a terrain data provider and stores it in the database.

[1177] Example: The server retrieves data in JSON format from a weather data provider, parses it, and stores it in a database.

[1178] 2. Creation of a virtual forest environment

[1179] Server: Generates a 3D model of a virtual forest environment based on collected weather and terrain data. This virtual forest environment is then sent to the user's device and displayed in the game engine.

[1180] Example: The server integrates weather and terrain data to generate a 3D model of a forest environment and delivers it to the user's device.

[1181] 3. Launch the game

[1182] User: Launches the forest growing game on the device and starts a new game session. The user's information and preferences are saved and the virtual forest environment is displayed.

[1183] Example: When a user launches a game, the device displays a configured virtual forest environment.

[1184] 4. Starting the emotion engine and collecting emotion data

[1185] User device: Recognizes user emotions in real time using an emotion engine. Analyzes the user's emotional state from data collected through input devices such as cameras and microphones, and collects it as emotion data.

[1186] Example: A user device uses a camera and microphone to analyze the user's facial expressions and tone of voice to gather information about their emotional state.

[1187] 5. Dynamic interface changes based on emotions

[1188] User terminal: Dynamically change the user interface based on the user's emotional state. For example, if the user is feeling stressed, simplify operations.

[1189] Example: If you recognize that the user is confused, provide more detailed guidance or easier options.

[1190] 6. Performing Management Operations

[1191] User: Plants and thins trees in the virtual forest environment through the user interface. Information on the type of tree, planting location, and thinning location is recorded as operational data.

[1192] Example: When a user plants a tree at a specific location, the location and type of tree are recorded as operation data.

[1193] 7. Collection and transmission of operational and emotional data

[1194] User device: User operation data and emotion data are periodically collected and sent to the server. Data is sent using the HTTP protocol, ensuring secure communication.

[1195] Example: When a user plays a game for a certain period of time, all operation data and emotional data from that time is packaged and sent to the server.

[1196] 8. Artificial Intelligence Analysis of Data

[1197] Server: The received operation data and emotional data are analyzed using an AI model. The efficiency model evaluates training efficiency, and the profitability model evaluates predicted revenue. In addition, by utilizing emotional data in the analysis, the user's stress and satisfaction are reflected in the evaluation.

[1198] Example: The server integrates and analyzes user operation data and emotional data to evaluate training efficiency, profitability, and user satisfaction.

[1199] 9. Integration and provision of analysis results

[1200] Server: Integrates the results of the efficiency and profitability models to produce the final analysis, which includes optimal forest management practices, profitable methods, and user satisfaction. The analysis results are provided to public organizations and fed back to users through in-game messages and notifications.

[1201] Example: The server provides the analysis results to the public body via email or dashboard, and also displays them to the user as an in-game message.

[1202] The above is the specific processing of this system that combines the emotion engine. It clearly shows how the server, terminal, and user are involved and operate at each step. The introduction of the emotion engine not only increases user satisfaction, but also makes it possible to provide more accurate analysis results.

[1203] The processing flow will be explained below.

[1204] Step 1: Data collection

[1205] Server: Obtains weather data from an external weather data provider via API. Parses the obtained data in JSON format and saves it in a database. Similarly, obtains terrain data from a terrain data provider and saves it in a database.

[1206] Example: The server retrieves data from a weather data provider using the "daily_weather_data" endpoint and stores it in the "weather_db". It also retrieves terrain data from the "terrain_data" endpoint and stores it in the "terrain_db".

[1207] Step 2: Creating a virtual forest environment

[1208] Server: Integrates collected weather and terrain data to generate a 3D model of the virtual forest environment. Sends this model to the user's device and prepares it for display in the game engine.

[1209] Example: The server reads data from "weather_db" and "terrain_db", generates a virtual forest using 3D modeling software, and saves the results in "virtual_forest_model".

[1210] Step 3: Launch the game

[1211] User: Launches the forest growing game on the device and starts a new game session. The initial user information and virtual forest environment are loaded.

[1212] Example: When a user opens the app, it downloads the "virtual_forest_model" from the server and starts the game with the user's initial settings.

[1213] Step 4: Launching the emotion engine and collecting emotion data

[1214] User device: Recognizes user emotions in real time using an emotion engine. Analyzes data collected through input devices such as cameras and microphones, and stores it as emotion data.

[1215] Example: The user device activates the camera and uses a facial expression recognition algorithm to analyze the user's emotions (e.g., happiness, sadness, surprise), and stores them in "emotion_data."

[1216] Step 5: Dynamically modifying the interface based on emotions

[1217] User terminal: Dynamically change the user interface based on the user's perceived emotional state. For example, simplify operations or provide positive feedback if the user is feeling stressed.

[1218] Example: If the user is perceived as confused, provide on-screen instructions or hints.

[1219] Step 6: Performing administrative operations

[1220] User: Through the user interface, the user can plant and thin trees in the virtual forest environment. Based on these operations, the type of tree, the location of the planting, and the location of the thinning are recorded as operational data.

[1221] Example: When a user plants an oak tree at a specific location, the location and type of tree are recorded in "operation_data."

[1222] Step 7: Collect and transmit operational and emotional data

[1223] User device: User operation data and emotion data are periodically collected and sent to the server. Data is sent using the HTTP protocol to ensure secure communication.

[1224] Example: After a user plays the game for a certain period of time, "operation_data" and "emotion_data" are sent to the server.

[1225] Step 8: Artificial intelligence analysis of the data

[1226] Server: The received operation data and emotion data are analyzed using an AI model. The efficiency model evaluates training efficiency, and the profitability model evaluates predicted revenue. In addition, by including emotion data in the analysis, evaluations take user experience into account.

[1227] Example: The server inputs "operation_data" and "emotion_data" into "efficiency_model" and "revenue_model" to calculate training efficiency, revenue forecast, and user satisfaction.

[1228] Step 9: Integrate and deliver the analysis results

[1229] Server: Integrates the analytical results obtained from the efficiency model and the profitability model to derive the final analytical results, which include optimal forest management methods, profitable practices, and improved user satisfaction. The analytical results are provided to public organizations and simultaneously displayed as feedback to users.

[1230] Example: The server displays the analytics results in the "forest_management_dashboard" and makes them available to public organizations, and also provides feedback to users via in-game notifications.

[1231] The above is the specific processing of this system that combines the emotion engine. It clearly shows how the server, terminal, and user are involved and operate at each step. The introduction of the emotion engine not only increases user satisfaction, but also makes it possible to provide more accurate analysis results.

[1232] Example 2

[1233] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1234] Current forest management systems lack the means to optimize interfaces that take user emotions into account and to improve user satisfaction. Furthermore, the analysis of collected data does not involve comprehensive evaluation, including user emotional data, which prevents sufficient improvements in management efficiency and profitability. Furthermore, there are also insufficient means to appropriately provide the results of these analyses to both public organizations and users.

[1235] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting environmental data from external sources, means for generating a virtual forest environment using the environmental data, a user interface means for users to perform operations to manage the virtual forest, means for collecting user emotion data in real time using an emotion engine, means for collecting user management operation data and emotion data, means for transmitting the management operation data and emotion data to the server, artificial intelligence analysis means for analyzing the management operation data and emotion data and evaluating efficient forest management methods and profitability, and means for providing the analysis results to public organizations. This allows the interface to be optimized based on the user emotion data, improving management efficiency and profitability. It also provides appropriate feedback to both public organizations and users.

[1236] An "external source" is a data source that exists outside the system and provides weather data, terrain data, and the like.

[1237] "Environmental data" refers to various information necessary for generating and managing a virtual forest environment, such as weather data and topographical data collected by the system from external sources.

[1238] A "virtual forest environment" is a 3D model of a virtual forest that is generated based on collected environmental data and that users can manage.

[1239] "User interface means" means software or hardware that allows a user to perform operations to manage a virtual forest.

[1240] An "emotion engine" is software or a system that uses input devices such as a camera or microphone to recognize and analyze a user's emotional state in real time.

[1241] "Emotional data" refers to information about the user's emotional state determined from facial expressions, tone of voice, etc. collected by the emotion engine.

[1242] "Administrative Operation Data" means information related to operations, such as planting or thinning trees, performed by a user within a virtual forest environment.

[1243] A "server" is a computer system that performs various processes such as data collection, analysis, and providing results.

[1244] "Artificial intelligence analysis means" refers to an analysis system that uses artificial intelligence to analyze collected management operation data and emotional data and evaluate efficient forest management methods and profitability.

[1245] "Analysis results" refers to information regarding efficient forest management methods, profitable methods, and user satisfaction obtained by the artificial intelligence analysis means analyzing management operation data and emotion data.

[1246] "Public organizations" refer to organizations that use the system's analysis results, such as public institutions and local governments.

[1247] This invention provides a "forestry cultivation game" system for improving the efficiency and profitability of forest management. This system aims to increase user satisfaction and provide more accurate analysis results by incorporating an emotion engine that recognizes the user's emotions.

[1248] The system mainly consists of the following elements: a server, a user terminal, an emotion engine, and an external data source.

[1249] Data collection and storage

[1250] The server obtains data from external weather data and terrain data services via APIs, parses it in JSON format, and stores it in a database. Specifically, it uses the OpenWeatherMap API and terrain data API, and MongoDB as the database.

[1251] Generating a virtual forest environment

[1252] The server uses Unity to generate a 3D model of the virtual forest environment based on the collected weather and terrain data, and then uses Blender's API to generate the 3D model, import it into a Unity project, and send it to the user's device.

[1253] Launching the game

[1254] A user launches the smartphone app or PC software and starts a new game session. User information and initial settings are sent to the server, and a virtual forest environment built with Unity is displayed on the device.

[1255] Activating the emotion engine and collecting emotion data

[1256] The user device activates the emotion engine and uses the camera and microphone to recognize the user's emotional state in real time. It uses FaceAPI and Watson Tone Analyzer to analyze facial expressions and tone of voice and store the emotional data.

[1257] Dynamic interface modification based on emotions

[1258] The user device dynamically changes the interface based on the user's emotional state. For example, if it detects confusion, it will respond by providing more guidance and easier operation options.

[1259] Performing administrative operations

[1260] Users plant and thin trees in the virtual forest environment, and the information is recorded as operational data. Specifically, when a user clicks on a specific spot to plant a tree, the location information and type of tree are recorded.

[1261] Collecting and transmitting operational and emotional data

[1262] The user device periodically collects operational and emotional data and sends it to the server using HTTPS. The data is compiled in JSON format to ensure secure communication.

[1263] AI analysis of data

[1264] The server analyzes the received operation data and sentiment data using artificial intelligence. A machine learning model built using Scikit-learn and TensorFlow evaluates management efficiency, profitability, and user satisfaction.

[1265] Integration and provision of analysis results

[1266] The server combines the analytical results from the efficiency model and the profitability model to derive the final results, which are then provided to the public organization and also provide feedback to users via Unity's notification system.

[1267] Prompt Sentence Examples

[1268] An example of a specific prompt to be input to the generative AI model is as follows:

[1269] "Based on user operation and sentiment data, suggest optimal forest management methods. If users are confused, consider easier operation options."

[1270] "Please use the following dataset to evaluate training efficiency and profitability, including user operation history and emotional state."

[1271] As described above, this system can achieve efficient forest management and improved profitability through the optimization of the user interface using an emotion engine and comprehensive data analysis. Furthermore, the effectiveness of the system can be enhanced by providing appropriate feedback to both public organizations and users.

[1272] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1273] Step 1: Data collection and storage

[1274] The server obtains data from external weather data and terrain data providers via API. The input includes authentication information such as an API key. The server parses the obtained data in JSON format and stores it in a MongoDB database. Specifically, the server calls the OpenWeatherMap API and terrain data API to obtain data and store it in the database.

[1275] Step 2: Creating a virtual forest environment

[1276] The server uses Unity to generate a 3D model of the virtual forest environment based on the collected weather and terrain data. The input includes weather and terrain data, and the output includes a Unity 3D model. Specifically, the server runs a Python script that uses Blender's API to generate the 3D model and import it into the Unity project.

[1277] Step 3: Launch the game

[1278] A user launches a smartphone app or PC software and starts a new game session. Input includes a user ID and password, and output includes a display of the virtual forest environment and session settings. Specifically, a user launches the app, enters their ID and password on the login screen, and a 3D model retrieved from the server is displayed.

[1279] Step 4: Launching the emotion engine and collecting emotion data

[1280] The user device runs an emotion engine and uses the camera and microphone to collect the user's emotional state in real time. The input includes the user's facial expression and voice information, and the output includes emotion data. Specifically, the device uses FaceAPI to analyze the video from the camera and Watson Tone Analyzer to analyze the tone of voice, and then stores the emotion data.

[1281] Step 5: Dynamically modifying the interface based on emotions

[1282] The user device dynamically changes the interface based on the user's recognized emotional state. The input includes emotional data, and the output includes interface modifications. Specifically, the device analyzes the emotional data and, for example, if it recognizes that the user is confused, it increases the number of guide displays and simplifies operation.

[1283] Step 6: Performing administrative operations

[1284] Users plant and thin trees in a virtual forest environment. The input includes information about the user's operations, and the output includes information about the type and location of the trees as a result of the operations. Specifically, the user selects a specific area using a mouse or touch display and performs operations to plant trees, and this information is recorded as operation data.

[1285] Step 7: Collect and transmit operational and emotional data

[1286] The user device periodically collects operation data and emotion data and sends them to the server using HTTPS. The input includes operation data and emotion data, and the output includes the results of transmission to the server. Specifically, the device compiles the data in JSON format at regular intervals and sends it to the server using a secure communication protocol.

[1287] Step 8: Artificial intelligence analysis of the data

[1288] The server uses artificial intelligence to analyze the received operation data and emotion data. The input includes operation data and emotion data, and the output includes the analysis results. Specifically, the server builds a machine learning model using Scikit-learn and TensorFlow to evaluate management efficiency, profitability, and user satisfaction.

[1289] Step 9: Integrate and deliver the analysis results

[1290] The server integrates the analytical results obtained from the efficiency model and the profitability model to derive the final result. The input includes the analytical results, and the output includes the integrated final result. Specifically, the server compiles the analytical results in JSON format and sends them to the public organization's web application via API, and also provides feedback to users using Unity Notifications.

[1291] (Application example 2)

[1292] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1293] Conventional forest management systems and user interfaces were not designed with user emotions in mind, resulting in problems such as poor user experience and operational efficiency. Furthermore, data analysis and feedback based on user emotions were not provided, resulting in insufficient evaluation of efficient forest management methods and profitability. Furthermore, the analysis results provided to public organizations did not reflect user emotions, making it difficult to establish forest management methods that fully considered actual user experience.

[1294] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting environmental data from external sources, means for generating a virtual forest environment using the environmental data, means for collecting user emotion data, means for collecting user management operation data and emotion data, artificial intelligence analysis means for analyzing the management operation data and emotion data and evaluating efficient forest management methods and profitability, and means for dynamically changing the virtual environment and user interface based on the analysis results. This enables the provision of an operation interface that takes the user's emotional state into consideration and advanced data analysis using emotion data. Furthermore, since the user's experience is reflected in the analysis results provided to public organizations, it becomes possible to establish efficient forest management methods based on actual user behavior.

[1295] "Environmental Data" is data collected from external sources, including weather and terrain data.

[1296] A "virtual forest environment" is a 3D model of a forest environment generated based on collected environmental data.

[1297] "User Interface" means the interface through which a user operates and manages the virtual forest environment.

[1298] "Emotion data" is data that indicates the emotional state of the user, obtained from their facial expressions and voice.

[1299] "Management operation data" refers to data relating to operations such as planting and thinning trees that a user performs within a virtual forest environment.

[1300] The "server" is a computer system that collects environmental data, generates a virtual forest environment, and collects and analyzes management operation data and emotion data.

[1301] "Artificial intelligence analysis means" refers to an algorithm or program that analyzes management operation data and emotion data and evaluates efficient forest management methods and profitability.

[1302] "Public bodies" are bodies, including government agencies and local governments, involved in forest management.

[1303] The "dynamic change means" is a means having the function of adaptively changing the virtual environment and user interface based on the analysis results.

[1304] The present invention is a forest management simulation system that recognizes and utilizes the emotions of a user. Specific examples for carrying out the present invention will be described below.

[1305] System Configuration

[1306] 1. Data Collection and Storage:

[1307] The server acquires weather and terrain data through APIs to collect environmental data from external sources, and stores them in a database in JSON format. It uses weather data provider services and terrain data provider services.

[1308] Example: The server retrieves data in JSON format from a weather data provider, parses it, and stores it in a database.

[1309] 2. Creating a Virtual Forest Environment:

[1310] The server runs a program to generate a 3D model of a virtual forest environment based on the collected weather and topographical data, and transmits this data to the user's device. The 3D model is generated using a 3D modeling tool such as Unity.

[1311] Example: The server integrates weather and terrain data to generate a 3D model of a virtual forest environment and delivers it to the user's device.

[1312] 3. Launch the game:

[1313] The user launches the forest growing game on their device and starts a new game session. Initial settings and user information are saved, and the virtual forest environment is displayed.

[1314] Example: When a user launches a game, a virtual forest environment is displayed on the device.

[1315] 4. Launching the emotion engine and collecting emotion data:

[1316] The user device uses an emotion engine to recognize the user's emotions in real time, analyzing facial expressions and tone of voice from data collected through the camera and microphone, and collecting emotional data.

[1317] Example: A user device uses a camera and microphone to analyze the user's facial expressions and tone of voice to gather information about their emotional state.

[1318] 5. Dynamic interface changes based on emotions:

[1319] The user device has the ability to dynamically change the user interface based on the recognized emotional state, for example, simplifying the operation of the interface if it detects that the user is feeling stressed.

[1320] Example: If you recognize that a user is confused, offer more detailed guidance or easier options.

[1321] 6. Performing administrative operations:

[1322] Users plant and thin trees in the virtual forest environment, and these operations are recorded as management operation data.

[1323] Example: When a user plants a tree at a specific location, the location information and the type of tree are recorded as operation data.

[1324] 7. Collection and Transmission of Operational and Emotional Data:

[1325] The user terminal periodically collects the user's management operation data and emotion data and transmits them to the server using the HTTP protocol.

[1326] Example: When a user plays a game for a certain period of time, the operation data and emotional data from that time are packaged and sent to the server.

[1327] 8. Artificial intelligence analysis of data:

[1328] The server then analyzes the received operation data and emotion data using an artificial intelligence model. This analysis uses AI platforms such as TensorFlow and PyTorch. Along with models that evaluate efficient forest management methods and profitability, the server also uses emotion data to evaluate user satisfaction.

[1329] Example: The server integrates and analyzes user operation data and emotional data to evaluate training efficiency, profitability, and user satisfaction.

[1330] 9. Integration and provision of analytical results:

[1331] The server aggregates the results of the analysis and provides the final results to the public body, while also providing feedback to the user, which is displayed as in-game messages and notifications.

[1332] Example: The server provides the analysis results to public organizations via email or dashboard, and displays them to users as in-game messages.

[1333] Prompt Sentence Examples

[1334] For example, in a factory training simulator, the following prompts are fed to a generative AI model:

[1335] "You are a design engineer for a robot operation simulator. Your task is to create a program that will enable the system to recognize the emotions of factory workers in real time and dynamically change the work interface during training. As a concrete example of emotion analysis, if the user is nervous, simplify the interface and display a guide to help the user calm down."

[1336] In this way, detailed design and refinement can be carried out using generative AI models.

[1337] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1338] Step 1:

[1339] The server collects environmental data from external sources. Specifically, it obtains JSON-formatted data from weather data providers and terrain data providers via APIs, parses it, and stores it in a database. The input is weather data and terrain data obtained from the external APIs, and the output is the parsed environmental data stored in the database.

[1340] Step 2:

[1341] The server generates a 3D model of the virtual forest environment based on the collected environmental data. Specifically, it designs the virtual forest environment using a 3D modeling tool such as Unity and delivers the generated 3D model to the user's device. The input is the saved environmental data, and the output is the 3D model data of the virtual forest sent to the user's device.

[1342] Step 3:

[1343] A user launches a forest growing game on a device and starts a new game session. The device saves the initial settings and user information and displays the virtual forest environment. The input is the 3D model data and the user's initial settings information, and the output is the displayed virtual forest environment.

[1344] Step 4:

[1345] The user device activates the emotion engine to collect the user's emotional data. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice in real time to determine their emotional state. The input is camera video and audio data, and the output is analyzed emotional data.

[1346] Step 5:

[1347] The user terminal dynamically changes the user interface based on the emotion data. For example, if it detects that the user is feeling stressed, it changes the interface to simplify operation. The input is the detected emotion data, and the output is the changed user interface.

[1348] Step 6:

[1349] Users perform management operations such as planting and thinning trees in the virtual forest environment. The terminal records these operations as management operation data. The input is the user's operation, and the output is the recorded management operation data.

[1350] Step 7:

[1351] The user terminal periodically transmits management operation data and emotion data to the server. Data transmission is performed securely using the HTTP protocol. The input is the collected management operation data and emotion data, and the output is the packaged data sent to the server.

[1352] Step 8:

[1353] The server analyzes the received management operation data and emotion data using an artificial intelligence model. Specifically, it uses TensorFlow and PyTorch to evaluate efficient forest management methods, profitability, and user satisfaction. The input is packaged data, and the output is the analysis results.

[1354] Step 9:

[1355] The server consolidates the analysis results and provides them to the public organization while simultaneously providing feedback to the user. Specifically, the analysis results are provided to the public organization via email or dashboard, and displayed to the user as in-game messages or notifications. The input is the analysis results, and the output is the public organization's data and feedback messages.

[1356] 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.

[1357] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1358] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1359] 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.

[1360] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1361] 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.

[1362] 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).

[1363] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1364] 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."

[1365] 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.

[1366] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1367] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1368] 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.

[1369] 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.

[1370] 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.

[1371] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1372] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1373] 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.

[1374] 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.

[1375] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1376] 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.

[1377] The following is further disclosed regarding the above embodiment.

[1378] (Claim 1)

[1379] a means for collecting environmental data from external sources;

[1380] means for generating a virtual forest environment using the environmental data;

[1381] a user interface means for allowing a user to perform operations for managing the virtual forest;

[1382] A means for collecting data on user management operations;

[1383] means for transmitting the management operation data to a server;

[1384] an artificial intelligence analysis means for analyzing the management operation data and evaluating an efficient forest management method and profitability;

[1385] a means for providing the analysis results to a public entity;

[1386] A system including:

[1387] (Claim 2)

[1388] 10. The system of claim 1, further comprising means for displaying the analysis results as feedback to the user.

[1389] (Claim 3)

[1390] 10. The system of claim 1, wherein the collected environmental data includes meteorological data and topographical data.

[1391] "Example 1"

[1392] (Claim 1)

[1393] A means for collecting environmental data from an external data providing service;

[1394] means for generating a virtual forest environment using the environmental data and transmitting the generated virtual forest environment to a user's terminal;

[1395] a user interface means for allowing a user to perform operations to manage the virtual forest environment;

[1396] A means for collecting and storing user management operation data on the device;

[1397] means for periodically transmitting the management operation data to a server;

[1398] a means for analyzing the management operation data using artificial intelligence analysis technology and evaluating an efficient forest management method and profitability;

[1399] a means for providing the analysis results to a public entity;

[1400] A system including:

[1401] (Claim 2)

[1402] 10. The system of claim 1, further comprising means for displaying the analysis results as feedback to the user.

[1403] (Claim 3)

[1404] 10. The system of claim 1, wherein the collected environmental data includes meteorological data and topographical data.

[1405] "Application Example 1"

[1406] (Claim 1)

[1407] a means for collecting environmental data from external sources;

[1408] means for generating a virtual forest environment using the environmental data;

[1409] a user interface means for allowing a user to perform operations for managing the virtual forest;

[1410] A means for collecting data on user management operations;

[1411] means for transmitting the management operation data to a server;

[1412] an artificial intelligence analysis means for analyzing the management operation data and evaluating an efficient forest management method and profitability;

[1413] a means for providing the analysis results to a public entity;

[1414] a means for generating a virtual environment of the road surroundings using environmental data collected by the automated driving device;

[1415] A means of collecting environmental data around the road in real time;

[1416] A system including:

[1417] (Claim 2)

[1418] 10. The system of claim 1, further comprising means for displaying the analysis results as feedback to the user.

[1419] (Claim 3)

[1420] 10. The system of claim 1, wherein the collected environmental data includes meteorological data and topographical data.

[1421] "Example 2: Combining Emotion Engines"

[1422] (Claim 1)

[1423] a means for collecting environmental data from external sources;

[1424] means for generating a virtual forest environment using the environmental data;

[1425] a user interface means for allowing a user to perform operations for managing the virtual forest;

[1426] a means for collecting user sentiment data in real time using an emotion engine;

[1427] A means for collecting user management operation data and sentiment data;

[1428] means for transmitting the management operation data and emotion data to a server;

[1429] an artificial intelligence analysis means for analyzing the management operation data and emotion data and evaluating an efficient forest management method and profitability;

[1430] a means for providing the analysis results to a public entity;

[1431] A system including:

[1432] (Claim 2)

[1433] 10. The system of claim 1, further comprising means for displaying the analysis results as feedback to the user.

[1434] (Claim 3)

[1435] 10. The system of claim 1, wherein the collected environmental data includes meteorological data and topographical data.

[1436] "Application example 2 when combining emotion engines"

[1437] (Claim 1)

[1438] a means for collecting environmental data from external sources;

[1439] means for generating a virtual forest environment using the environmental data;

[1440] a user interface means for allowing a user to perform operations for managing the virtual forest;

[1441] a means for collecting user emotional data;

[1442] A means for collecting user management operation data and sentiment data;

[1443] means for transmitting the management operation data and emotion data to a server;

[1444] an artificial intelligence analysis means for analyzing the management operation data and emotion data and evaluating an efficient forest management method and profitability;

[1445] a means for providing the analysis results to a public entity;

[1446] means for dynamically modifying the virtual environment and the user interface based on the analysis results;

[1447] A system including:

[1448] (Claim 2)

[1449] 10. The system of claim 1, further comprising means for displaying the analysis results as feedback to the user.

[1450] (Claim 3)

[1451] 10. The system of claim 1, wherein the collected environmental data includes meteorological data and topographical data. [Explanation of symbols]

[1452] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for collecting environmental data from external sources; means for generating a virtual forest environment using the environmental data; a user interface means for allowing a user to perform operations for managing the virtual forest; A means for collecting data on user management operations; means for transmitting the management operation data to a server; an artificial intelligence analysis means for analyzing the management operation data and evaluating an efficient forest management method and profitability; a means for providing the analysis results to a public entity; A system including:

2. The system of claim 1 further comprising means for displaying the analysis results as feedback to the user.

3. 10. The system of claim 1, wherein the collected environmental data includes meteorological data and terrain data.

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