System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting data on biodiversity from national and international databases; means for standardizing and organizing the collected data; means for inputting sensing data from on-site surveys; means for analyzing the data using a machine learning algorithm to identify areas of interest; means for developing a guideline based on the analysis results and generating a report; and means for displaying the generated report to a user and collecting feedback.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] This invention relates to a system for efficiently collecting and analyzing large amounts of data in biodiversity conservation and management, and formulating optimal guidelines for each region based on the results. With conventional technology, data collection and analysis were dispersed, making it difficult to formulate guidelines based on unified standards. This was particularly difficult in regions with diverse climatic zones, such as Japan, where formulating appropriate conservation measures was difficult. Therefore, this invention aims to provide efficient and integrated data management and analysis, and establish biodiversity conservation standards unique to Japan. [Means for solving the problem]

[0005] The present invention solves the above problems by the following means.

[0006] The system includes a means for collecting data on biodiversity from domestic and international databases, a means for standardizing the format of the collected data and organizing it, a means for inputting sensing data obtained through field surveys, a means for analyzing the data using a machine learning algorithm and identifying important areas, a means for formulating guidelines and generating reports based on the analysis results, and a means for displaying the generated reports to users and collecting feedback.

[0007] Furthermore, by updating the system based on the feedback and providing a means for continuously collecting, analyzing, and improving data, it becomes possible to achieve highly accurate biodiversity management that reflects user opinions. For example, if the data collection means obtains data from the International Biodiversity Information Facility and the sensing data input means includes field survey data obtained using drones, it becomes possible to collect wide-ranging and detailed data.

[0008] In addition, the machine learning algorithm includes a random forest algorithm, allowing for rapid and accurate analysis of large amounts of data, which will enable efficient provision of information necessary for regional biodiversity conservation and the formulation of Japan's own biodiversity conservation standards.

[0009] A "database" is a system that can centrally manage large amounts of data and efficiently retrieve and manipulate it.

[0010] "Biodiversity" is a concept that refers to the variety of organisms that exist on Earth, their variations, and the diversity of the ecosystems they form.

[0011] "Collection" refers to the act of gathering information or items for a specific purpose.

[0012] "Format unification" refers to the process of aligning data of different formats according to certain standards.

[0013] "Maintenance" refers to organizing data and information and keeping it in an easy-to-use state.

[0014] "Field research" refers to the act of going to a specific location to collect data and information directly.

[0015] "Sensing data" refers to digitized data obtained by measuring physical and chemical phenomena using sensors.

[0016] "Input" refers to the act of taking data or information into a system.

[0017] A "machine learning algorithm" refers to a computational method for learning patterns and rules from data and making predictions and classifications.

[0018] "Analysis" refers to the act of examining data to find information and trends within it.

[0019] "Guidelines" refer to guidelines or standards that must be followed to achieve a specific purpose.

[0020] "Report" refers to a report summarizing the results of an investigation or study.

[0021] "Display" refers to the visual presentation of data or information.

[0022] "Feedback" refers to opinions and evaluations obtained from users of a system or service.

[0023] "Update" refers to the act of replacing existing data or information with new data or information. [Brief explanation of the drawings]

[0024] [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

[0025] 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.

[0026] First, the terms used in the following description will be explained.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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."

[0032] [First embodiment]

[0033] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0034] 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.

[0035] 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).

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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."

[0045] The present invention relates to a system for efficiently collecting, integrating, and analyzing huge amounts of data in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. Specific embodiments of the system of the present invention are described below.

[0046] System Overview

[0047] The system includes the following elements:

[0048] 1. Data collection method: The server accesses domestic and international databases to collect data on biodiversity.

[0049] 2. Data preparation method: The server standardizes and organizes the collected data.

[0050] 3. Sensing data input method: The user inputs the sensing data acquired during the field survey into the system.

[0051] 4. Data analysis method: The server uses machine learning algorithms to analyze the data and identify key areas.

[0052] 5. Guideline formulation means: The server formulates guidelines based on the analysis results and generates a report.

[0053] 6. Feedback means: The terminal displays the generated report to the user and collects feedback.

[0054] 7. System update method: The server updates the system based on feedback, continuously collecting and analyzing data to improve it.

[0055] Natural language explanation of program processing

[0056] 1. Data Collection

[0057] The server collects biodiversity data from public and private databases both domestically and internationally.

[0058] Example: A server downloads the latest species list from the International Biodiversity Information Facility via an API.

[0059] 2. Standardization and organization of data formats

[0060] The server compiles and organizes the collected data.

[0061] Example: The server converts data collected from different databases into a unified format and eliminates duplicate data.

[0062] 3. Input of sensing data

[0063] The user inputs the data acquired on-site into the system.

[0064] Example: A user uploads photos and location information of plants collected during a field survey.

[0065] 4. Data Analysis

[0066] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots.

[0067] Example: The server uses a random forest algorithm to identify biodiversity hotspots in rainy regions.

[0068] 5. Guideline formulation and report generation

[0069] The server formulates guidelines based on the analysis results and generates a report.

[0070] Example: The server generates reports of protection measures based on climate zone and season.

[0071] 6. Gathering Feedback

[0072] The terminal displays the generated report to the user and collects feedback.

[0073] Example: The device provides an interface for users to view reports and collect their ratings and comments.

[0074] 7. System Updates

[0075] The server updates the system based on feedback, continuously collecting and analyzing data, and optimizing Japan's unique biodiversity conservation standards.

[0076] Example: The server reflects user feedback and adds new items to the next data collection and analysis.

[0077] In this way, the system of the present invention will establish biodiversity conservation standards unique to Japan, enabling optimal conservation measures to be implemented in each region.

[0078] The processing flow will be explained below.

[0079] Step 1:

[0080] The server will access domestic and international databases and collect data on biodiversity.

[0081] Specific behavior:

[0082] 1. The server uses the API to send requests to the International Biodiversity Information Facility and other authoritative data sources.

[0083] 2. The server stores the acquired data in a local database for temporary storage.

[0084] Step 2:

[0085] The server standardizes the format of the collected data and organizes it.

[0086] Specific behavior:

[0087] 1. The server detects duplicate data and removes the duplicate records.

[0088] 2. The server converts data from different formats into a unified format, organizing the data into common fields such as species name, region, and publication year.

[0089] Step 3:

[0090] The user inputs sensing data acquired on-site into the system.

[0091] Specific behavior:

[0092] 1. The user logs into the system using a dedicated terminal or a web interface.

[0093] 2. The user enters the collected data (e.g., plant photos, latitude and longitude information, etc.) into the upload form.

[0094] 3. The sensing data is sent to the server and stored in a local database.

[0095] Step 4:

[0096] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots.

[0097] Specific behavior:

[0098] 1. The server pre-processes the data and creates a dataset for analysis.

[0099] 2. The server applies a machine learning model (e.g., random forest algorithm) to detect biodiversity hotspots in a specific area.

[0100] 3. The analysis results will be saved in a specified folder on the server.

[0101] Step 5:

[0102] The server formulates guidelines based on the analysis results and generates a report.

[0103] Specific behavior:

[0104] 1. The server extracts the necessary information from the analysis results.

[0105] 2. The server creates a report according to a standard format and generates it in PDF format.

[0106] 3. The report is saved to the specified folder on the server.

[0107] Step 6:

[0108] The terminal displays the generated report to the user and collects feedback.

[0109] Specific behavior:

[0110] 1. After user authentication, the terminal provides an interface to display the generated report.

[0111] 2. Users view the report and use the feedback form to provide their ratings and comments.

[0112] 3. The feedback is sent to the server.

[0113] Step 7:

[0114] The server updates the system based on feedback collected from users, and continuously collects, analyzes, and improves data.

[0115] Specific behavior:

[0116] 1. The server analyzes user feedback and identifies areas for improvement.

[0117] 2. The server updates the system configuration to incorporate new data collection and analysis algorithms.

[0118] 3. The server periodically re-runs the data collection and analysis and updates the reports.

[0119] Through these steps, the system will establish biodiversity conservation standards unique to Japan, enabling optimal conservation measures to be implemented in each region.

[0120] Example 1

[0121] 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."

[0122] Biodiversity conservation and management requires the collection, organization, and analysis of large amounts of data. However, this process is time-consuming and labor-intensive, making it difficult to carry out efficiently. Furthermore, to formulate optimal conservation guidelines for each region, it is necessary to effectively integrate and analyze field survey data and existing data. Furthermore, it is also necessary to reflect feedback and continuously improve the system. A system that can solve this problem and implement efficient and accurate biodiversity conservation measures is needed.

[0123] 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.

[0124] In this invention, the server includes means for collecting information on biodiversity from domestic and international sources, means for converting the collected information into a unified format and organizing it, means for inputting sensing information from field surveys, means for analyzing the information using a machine learning algorithm and identifying important ecological regions, means for formulating conservation guidelines and generating documents based on the analysis results, means for displaying the generated documents to users and collecting evaluations, and means for updating the system based on the evaluations and continuously collecting, analyzing, and improving the information. This enables the efficient collection, integration, and analysis of data necessary for biodiversity protection and management, and the formulation and implementation of optimal conservation measures for each region.

[0125] "Biodiversity" refers to the variety of mutations, species, individuals, and ecosystems of living organisms on Earth.

[0126] "Sources" refers to national and international databases and institutions that provide biodiversity data.

[0127] "Unified format" refers to converting data provided in different formats into a consistent data format.

[0128] "Maintenance" refers to the process of removing duplicates and missing data and cleansing the collected data.

[0129] "Sensing information" refers to data obtained during field surveys (e.g., photographs, GPS coordinates, observation notes, etc.).

[0130] "Machine learning algorithms" refer to computer algorithms that analyze large amounts of data to generate patterns and make predictions.

[0131] "Critical ecoregions" refer to specific areas that are rich in biodiversity and require protection.

[0132] "Conservation guidelines" refer to specific guidelines and measures for effectively promoting biodiversity conservation.

[0133] "Document" refers to reports and reports containing analytical findings and conservation guidelines.

[0134] "User" refers to the person or organization that uses the system to input, analyze and evaluate biodiversity data.

[0135] "Evaluation" refers to feedback and suggestions for improvement that users give to the generated document.

[0136] "Update" refers to the process of improving the system based on collected evaluations and making improvements to enable more effective data collection and analysis.

[0137] The present invention relates to a system for efficiently collecting, integrating, and analyzing vast amounts of information in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. Specific embodiments of the present invention are described below.

[0138] Data collection

[0139] The server collects biodiversity information from domestic and international sources, including databases from international biodiversity information agencies and local environmental organizations. The server retrieves data from these sources via APIs. For example, the server automatically downloads the latest species list from the international biodiversity information agency.

[0140] Standardization and organization of data formats

[0141] The server converts the collected information into a unified format and organizes it. This includes converting data provided in different formats (e.g., CSV, JSON, XML) into a unified format. The server also performs data cleansing to remove duplicate data and missing values.

[0142] Sensing data input

[0143] Users input sensing information acquired during field surveys into the system. Using a dedicated mobile app, users input photos of plants taken on-site, GPS coordinates, observation notes, etc. This information is sent to the server in real time and stored in a database. As a specific example, a user may upload a photo of a new species of plant during a field survey and input its location information and observation notes.

[0144] Data analysis

[0145] The server uses machine learning algorithms to analyze the collected and organized information and identify important ecological regions. It uses Python machine learning libraries (such as scikit-learn and TENSORFLOW®) to analyze areas with high biodiversity based on large amounts of data. For example, the server uses a random forest algorithm to identify biodiversity hotspots in areas with high rainfall.

[0146] Guideline formulation and report generation

[0147] The server formulates protection guidelines based on the analysis results and generates a document. A generative AI model (e.g., OpenAI's GPT-4®) is used to automatically generate guidelines outlining specific protection measures. These guidelines are output as a PDF report and can be downloaded by users through a web portal. A specific example would be a scenario in which the server generates a report stating that "in areas with annual rainfall of 1000 mm or more, protection of certain vegetation types is necessary."

[0148] Collecting feedback

[0149] The terminal displays the generated document to the user and collects their evaluation. The user is provided with an interface that allows them to check the report on the terminal and enter their evaluation of the generated guidelines and suggestions for improvement. For example, the user may submit comments on whether a specific guideline is feasible or what improvements can be made.

[0150] System Updates

[0151] The server updates the system based on the evaluations and continuously collects, analyzes, and improves the information. It analyzes user feedback to improve the machine learning model and data collection process. For example, the next time data is collected, new data items (such as soil pH data) are added and incorporated into the analysis algorithm.

[0152] Examples of prompt statements

[0153] As an example of a specific prompt, the following might be used when a user uploads data collected during a field survey:

[0154] "Please upload photos of plants taken during your field surveys and their locations into the system, along with notes on the habitat and weather conditions in which you observed them."

[0155] And when the server identifies new biodiversity hotspots and generates guidelines, the prompt is:

[0156] "Calculate and identify biodiversity hotspots in high-rainfall areas using up-to-date rainfall and vegetation data. Then, based on the identified hotspots, develop guidelines and generate reports for conservation measures appropriate for the area."

[0157] In this way, the present invention is a system that enables the efficient collection, integration, and analysis of data necessary for the protection and management of biodiversity, and supports the formulation and implementation of optimal conservation measures for each region.

[0158] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0159] Step 1: Data collection

[0160] The server collects information on biodiversity from domestic and international sources.

[0161] Input: API endpoints from databases such as international biodiversity information agencies and local environmental protection agencies.

[0162] Output: Raw datasets (e.g., species lists, meteorological data, vegetation data)

[0163] Specific operation: The server periodically sends API requests to retrieve data and saves it in local storage. For example, a CRON job can be used to automatically retrieve the latest data at 3:00 AM every day.

[0164] Step 2: Standardize and organize data formats

[0165] The server converts the collected information into a unified format and organizes it.

[0166] Input: Raw datasets acquired in different formats (CSV, JSON, XML).

[0167] Output: A uniformly formatted dataset

[0168] What happens: The server uses ETL (Extract, Transform, Load) tools to transform the data into a consistent format. Data cleansing scripts correct and remove duplicate entries and missing values. Data frame manipulations are performed, for example, using the Python Pandas library.

[0169] Step 3: Input of sensing data

[0170] The user inputs sensing information acquired during field surveys into the system.

[0171] Input: Data obtained during field surveys (plant photos, GPS coordinates, observation notes, etc.).

[0172] Output: Sensing information stored on the server

[0173] Specific operation: A user uses a mobile app to input photos and text data, which the app then uploads to a server. For example, a user might upload a photo of a new species discovered in the Southern Alps region and enter its GPS coordinates.

[0174] Step 4: Data analysis

[0175] The server uses machine learning algorithms to analyze the collected and organized information and identify important ecological regions.

[0176] Input: Organized dataset (integrated data of collected data and sensing data)

[0177] Output: List of important ecological regions and their analysis results

[0178] How it works: The server uses Python machine learning libraries (scikit-learn, TensorFlow) to apply algorithms (e.g., random forests) to analyze the data, for example, to identify biodiversity hotspots based on rainfall and local vegetation data.

[0179] Step 5: Guideline development and report generation

[0180] The server formulates protection guidelines based on the analysis results and generates a document.

[0181] Input: List of important ecological regions and their analysis results

[0182] Output: PDF report with protection guidelines

[0183] How it works: The server uses a generative AI model (such as OpenAI's GPT-4) to automatically generate protection guidelines based on the analysis results, and creates a report using a PDF generation library, which users can download from a web portal.

[0184] Step 6: Gather feedback

[0185] The terminal displays the generated document to the user and collects ratings.

[0186] Input: User evaluation of the report and suggestions for improvement

[0187] Output: Feedback information stored in the rating database

[0188] Specific operation: The user views the report on their device and enters comments and suggestions using the feedback form. This information is sent to the server in real time and stored in the database. For example, the user can enter feedback such as "This part is specific and easy to understand" or "This part is unclear and needs improvement."

[0189] Step 7: Update your system

[0190] The server updates the system based on the evaluation and continuously collects and analyzes information to improve it.

[0191] Input: Feedback information stored in the ratings database

[0192] Output: Improved data collection and analysis processes and updated systems

[0193] Specific operation: The server analyzes the feedback and adds new data items or improves the algorithm. For example, the next time data is collected, new soil pH data is collected and incorporated into the analysis algorithm. This improves the accuracy and efficiency of the entire system.

[0194] (Application example 1)

[0195] 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."

[0196] Conventional biodiversity conservation management systems are inefficient because they require a lot of manpower and time for data collection and analysis, and they lack management measures that are particularly adapted to the environment inside and outside factories. As a result, it has been difficult to formulate appropriate guidelines for minimizing the impact on ecosystems. To solve this problem, a system that allows for efficient and accurate data collection and analysis, and that takes into account practicality in the field, is needed.

[0197] 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.

[0198] In this invention, the server includes means for collecting data on biodiversity from domestic and international databases, means for standardizing and organizing the collected data, means for inputting sensing data from field surveys, means for analyzing the data using a machine learning algorithm and identifying important areas, means for formulating guidelines and generating reports based on the analysis results, means for displaying the generated reports to users and collecting feedback, and a robot that collects data from inside and outside a factory and proposes optimal management measures for ecosystem protection, including means for transmitting the data collected by the robot to the server and for the server to analyze the data, and means for generating the analysis results as guidelines and displaying them via the robot. This enables efficient and accurate data collection and analysis, and enables the formulation of appropriate guidelines that take practicality in the field into consideration.

[0199] A "database" is a collection of specific information that is systematically organized and stored so that it can be easily searched and retrieved.

[0200] "Biodiversity" is a term that refers to the variety of living species on Earth, their genetic variations, and the ecosystems they comprise.

[0201] "Data collection methods" are methods for obtaining information on biodiversity from domestic and international databases.

[0202] "Data preparation means" refers to the means of standardizing and organizing the collected data.

[0203] "Sensing data" refers to data on biodiversity obtained through field surveys.

[0204] A "machine learning algorithm" is a program that analyzes data and performs pattern recognition and prediction.

[0205] "Data analysis means" refers to a means of analyzing data using machine learning algorithms and identifying important areas.

[0206] A "guideline" is a document that provides guidelines or standards established to achieve a specific purpose.

[0207] The "report generation means" is a means for creating guidelines based on the analysis results and outputting them in document format.

[0208] The "feedback means" is a means for displaying the generated report to the user and collecting evaluations and comments from the user.

[0209] "Inside and outside the factory" is a term that refers to the inside of the factory and the surrounding area.

[0210] "Robots" are autonomous or semi-autonomous machines that collect biodiversity data from inside and outside factories and suggest management measures.

[0211] A "server" is a computer system that processes, stores, and provides data over a network.

[0212] The "analysis means" is a means for transmitting the collected data to a server and analyzing it using a machine learning algorithm.

[0213] The "display means" is a means for generating the analysis results as guidelines and displaying the information via the robot.

[0214] This invention relates to a system for efficiently collecting, integrating, and analyzing huge amounts of data in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. This system utilizes a robot that collects data from inside and outside factories and proposes optimal management measures for ecosystem protection.

[0215] 1. Data Collection

[0216] The server collects biodiversity information from domestic and international databases, such as the Global Biodiversity Information Facility (GBIF) and retrieves the latest species list via an API. The server also receives data collected by the robots inside and outside the factory and stores it in a database.

[0217] 2. Data Preparation

[0218] The server standardizes and organizes the collected data. This includes converting data collected from different databases into a unified format, eliminating duplicate data, normalizing data, etc. Organized data is important for improving the accuracy of subsequent data analysis.

[0219] 3. Input of sensing data

[0220] The user (or robot) inputs sensing data acquired on-site into the system. For example, they upload photos of plants collected during field surveys along with their location information. This data is then used for analysis on the server.

[0221] 4. Data Analysis

[0222] The server will analyze the data using machine learning algorithms to identify important biodiversity hotspots. For example, it will use a random forest algorithm to identify biodiversity hotspots in areas with high rainfall. The results of this analysis will form the basis for developing guidelines.

[0223] 5. Establishment of guidelines

[0224] The server then formulates guidelines based on the analysis results and generates reports, such as protection measures based on climate zones and seasons, and provides them to users. These guidelines include specific procedures and monitoring methods for ecosystem protection.

[0225] 6. Gathering Feedback

[0226] The terminal displays the generated report to the user and provides an interface for collecting feedback. For example, after viewing the report, the user can enter their rating and comments. This feedback is used to improve the system.

[0227] 7. System Updates

[0228] The server will update the system based on feedback and continuously improve data collection and analysis, thereby establishing Japan's own biodiversity conservation standards and implementing optimal conservation measures for each region.

[0229] Hardware and software used

[0230] Hardware:

[0231] Robots: Devices that collect data from inside and outside the factory.

[0232] Server: A computer system that processes and stores data.

[0233] software:

[0234] requests: A Python package for retrieving data from data collection APIs.

[0235] scikit-learn: A Python library for running machine learning algorithms (e.g., the Random Forest algorithm).

[0236] Use of such a system will enable efficient and accurate data collection and analysis, making it possible to formulate appropriate guidelines that take into account practicality in the field.

[0237] Examples of concrete examples and prompts

[0238] Examples:

[0239] For example, in high humidity areas of a factory, guidelines may be created such as "maintain specific plant habitats and conduct monthly monitoring."

[0240] Example prompt sentence:

[0241] "Implement a detailed ecosystem analysis using data obtained from API and a guideline development program based on the results."

[0242] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0243] Step 1:

[0244] The server collects biodiversity information from domestic and international databases. Specifically, the server uses APIs to send requests to each database and temporarily stores the acquired data. The input is the API endpoint and authentication information, and the output is the acquired biodiversity data.

[0245] Step 2:

[0246] The server standardizes and organizes the collected data. Because the acquired data is in different formats, the server converts it into a standard format and eliminates duplicate data. This process is called data cleaning and is important for improving the accuracy of data analysis. The input is raw biodiversity data, and the output is organized data.

[0247] Step 3:

[0248] Users input sensing data acquired during field surveys into the system. For example, they upload photos of plants taken during field surveys and GPS location information. This data is sent to the server and integrated with other data. The input is sensing data from field surveys, and the output is the integrated data uploaded to the server.

[0249] Step 4:

[0250] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots. For example, a random forest algorithm can be used to analyze the data and identify high biodiversity areas. The server inputs the data into the model and obtains a list of important areas as output. The input is the consolidated data, and the output is the analysis results.

[0251] Step 5:

[0252] The server formulates guidelines based on the analysis results and generates a report. Based on the analysis results, it creates a document detailing ecosystem protection measures for each region and formats it as a report. The input is the results of the data analysis, and the output is the generated report.

[0253] Step 6:

[0254] The terminal provides an interface for displaying the generated report to the user and collecting feedback. The user views the report and enters their evaluation and comments on its contents as feedback. The input is the generated report and the user's evaluation and comments, and the output is the collected feedback.

[0255] Step 7:

[0256] The server updates the system based on the feedback and continuously improves data collection and analysis. It analyzes the feedback information, makes necessary corrections and adds features, and improves the accuracy and usability of the entire system. The input is user feedback, and the output is an updated system.

[0257] 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.

[0258] The present invention relates to a system for efficiently collecting, integrating, and analyzing vast amounts of data in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. Furthermore, by combining it with an emotion engine that recognizes user emotions, the quality of user feedback is improved, contributing to the improvement of the entire system. A specific embodiment of the system of the present invention is described below.

[0259] System Overview

[0260] The system includes the following elements:

[0261] 1. Data collection method: The server accesses domestic and international databases to collect data on biodiversity.

[0262] 2. Data preparation method: The server standardizes and organizes the collected data.

[0263] 3. Sensing data input method: The user inputs the sensing data acquired during the field survey into the system.

[0264] 4. Data analysis method: The server uses machine learning algorithms to analyze the data and identify key areas.

[0265] 5. Guideline formulation means: The server formulates guidelines based on the analysis results and generates a report.

[0266] 6. Feedback means: The terminal displays the generated report to the user and collects feedback.

[0267] 7. Emotion engine: The emotion engine built into the device analyzes the user's emotions and understands their emotional state at the time of feedback.

[0268] 8. System update method: The server updates the system based on feedback and analysis results from the emotion engine, and continuously collects, analyzes, and improves data.

[0269] Natural language explanation of program processing

[0270] 1. Data Collection

[0271] The server collects biodiversity data from public and private databases both domestically and internationally.

[0272] Example: A server downloads the latest species list from the International Biodiversity Information Facility via an API.

[0273] 2. Standardization and organization of data formats

[0274] The server compiles and organizes the collected data.

[0275] Example: The server converts data collected from different databases into a unified format and eliminates duplicate data.

[0276] 3. Input of sensing data

[0277] The user inputs the data acquired on-site into the system.

[0278] Example: A user uploads photos and location information of plants collected during a field survey.

[0279] 4. Data Analysis

[0280] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots.

[0281] Example: The server uses a random forest algorithm to identify biodiversity hotspots in rainy regions.

[0282] 5. Guideline formulation and report generation

[0283] The server formulates guidelines based on the analysis results and generates a report.

[0284] Example: The server generates reports of protection measures based on climate zone and season.

[0285] 6. Gathering Feedback

[0286] The terminal displays the generated report to the user and collects feedback.

[0287] Example: The device provides an interface for users to view reports and collect their ratings and comments.

[0288] 7. Emotion Analysis Using an Emotion Engine

[0289] The terminal analyzes the user's emotions using an emotion engine when the user inputs feedback.

[0290] Example: The device uses a camera and microphone to analyze the user's facial expressions and tone of voice to understand the user's emotional state (e.g., satisfaction, dissatisfaction, interest).

[0291] 8. System Updates

[0292] The server updates the system based on feedback and the results of the emotion engine analysis, continuously collecting and analyzing data to optimize Japan's unique biodiversity conservation standards.

[0293] Example: The server reflects user feedback and sentiment analysis and adds new items to the next data collection and analysis.

[0294] In this way, the system of the present invention will establish biodiversity conservation standards unique to Japan and enable the implementation of optimal conservation measures for each region based on high-quality feedback that also takes user emotions into consideration.

[0295] The processing flow will be explained below.

[0296] Step 1:

[0297] The server will access domestic and international databases and collect data on biodiversity.

[0298] Specific behavior:

[0299] 1. The server uses the API to send requests to the International Biodiversity Information Facility and other authoritative data sources.

[0300] 2. The server stores the acquired data in a local database for temporary storage.

[0301] Step 2:

[0302] The server standardizes the format of the collected data and organizes it.

[0303] Specific behavior:

[0304] 1. The server detects duplicate data and removes the duplicate records.

[0305] 2. The server converts data from different formats into a unified format, organizing the data into common fields such as species name, region, and publication year.

[0306] Step 3:

[0307] The user inputs sensing data acquired on-site into the system.

[0308] Specific behavior:

[0309] 1. The user logs into the system using a dedicated terminal or a web interface.

[0310] 2. The user enters the collected data (e.g., plant photos, latitude and longitude information, etc.) into the upload form.

[0311] 3. The sensing data is sent to the server and stored in a local database.

[0312] Step 4:

[0313] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots.

[0314] Specific behavior:

[0315] 1. The server pre-processes the data and creates a dataset for analysis.

[0316] 2. The server applies a machine learning model (e.g., random forest algorithm) to detect biodiversity hotspots in a specific area.

[0317] 3. The analysis results will be saved in a specified folder on the server.

[0318] Step 5:

[0319] The server formulates guidelines based on the analysis results and generates a report.

[0320] Specific behavior:

[0321] 1. The server extracts the necessary information from the analysis results.

[0322] 2. The server creates a report according to a standard format and generates it in PDF format.

[0323] 3. The report is saved to the specified folder on the server.

[0324] Step 6:

[0325] The terminal displays the generated report to the user and collects feedback.

[0326] Specific behavior:

[0327] 1. After user authentication, the terminal provides an interface to display the generated report.

[0328] 2. Users view the report and use the feedback form to provide their ratings and comments.

[0329] 3. The feedback is sent to the server.

[0330] Step 7:

[0331] The terminal analyzes the user's emotions using an emotion engine when the user inputs feedback.

[0332] Specific behavior:

[0333] 1. The device uses a camera and microphone to record the user's facial expressions and tone of voice.

[0334] 2. The device uses an emotion engine to analyze this data and determine the user's emotional state.

[0335] 3. The sentiment analysis results are sent to the server.

[0336] Step 8:

[0337] The server updates the system based on feedback collected from users and the analysis results of the emotion engine, and continuously collects, analyzes, and improves the data.

[0338] Specific behavior:

[0339] 1. The server analyzes user feedback and sentiment analysis results to identify areas for improvement.

[0340] 2. The server updates the system configuration to incorporate new data collection and analysis algorithms.

[0341] 3. The server periodically re-runs the data collection and analysis and updates the reports.

[0342] Through these steps, the system will establish Japan's own biodiversity conservation standards and enable optimal conservation measures to be implemented for each region. Furthermore, by taking user sentiment into account, the system will be able to reflect more effective feedback and improve the accuracy of the system.

[0343] Example 2

[0344] 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."

[0345] The problem that this invention aims to solve is to achieve sustainable conservation of biodiversity by efficiently collecting, integrating, and analyzing huge amounts of data and formulating optimal guidelines for each region based on the results. The invention also aims to provide a system that incorporates a function to recognize user emotions in order to improve the quality of user feedback and utilize it to improve the system.

[0346] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0347] In this invention, the server includes means for collecting biodiversity data from domestic and international databases, means for standardizing the format of the collected data and organizing it, means for inputting sensing data from field surveys, means for analyzing the data using a machine learning algorithm and identifying important areas, means for formulating guidelines and generating reports based on the analysis results, means for displaying the generated reports to users and collecting feedback, an emotion engine for analyzing user emotions, and means for updating the system based on the feedback and the analysis results of the emotion engine. This enables efficient data collection and analysis, the formulation of optimal guidelines for each area, improved quality of user feedback, and continuous improvement of the system.

[0348] "Data collection tools" are tools for collecting data on biodiversity from domestic and international databases.

[0349] "Data preparation means" refers to the means for standardizing and preparing the format of collected data.

[0350] The "sensing data input means" is a means for a user to input sensing data acquired during a field survey into the system.

[0351] "Data analysis means" refers to means for analyzing collected data using machine learning algorithms and identifying key areas.

[0352] The "guideline formulation means" is a means for formulating guidelines based on the analysis results and generating reports.

[0353] The "feedback collection means" is a means for displaying the generated report to the user and collecting feedback.

[0354] The "emotion engine" is an engine that has a function for analyzing the user's emotions.

[0355] The "system update means" is a means for updating the system based on the feedback and the analysis results of the emotion engine.

[0356] The present invention relates to a system for efficiently collecting, integrating, and analyzing vast amounts of data in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. Furthermore, by combining it with an emotion engine that recognizes user emotions, the quality of user feedback is improved, contributing to the improvement of the entire system. A specific embodiment of the system of the present invention is described below.

[0357] Data collection methods

[0358] The server collects biodiversity data from public and private databases both in Japan and overseas. The server runs a regularly scheduled task, accessing each database (e.g., databases of domestic and international biodiversity information facilities and the Ministry of the Environment) using an API key to download species lists and habitat data.

[0359] Example: A server downloads the latest species list from the International Biodiversity Information Facility via an API.

[0360] Prompt text: Use the following API key to get the latest species list from the International Biodiversity Information Facility.

[0361] Data preparation methods

[0362] The server aligns and organizes the collected data. It uses the Python Pandas library to read the data and convert it from different formats (CSV, JSON, XML, etc.) into a unified data frame format. It also removes duplicate data and completes missing information.

[0363] Example: The server converts data collected from different databases into a unified format and eliminates duplicate data.

[0364] Prompt: Transform biodiversity data from different data sources into a unified format and eliminate duplicate data.

[0365] Sensing data input means

[0366] Users input data acquired on-site into the system. Using a smartphone or tablet, users upload photos taken during field surveys and location information to a dedicated app. The app includes a photo upload button and an automatic location information acquisition function.

[0367] Example: A user uploads photos of plants taken during a field survey along with their location.

[0368] Prompt: Upload photos of plants taken during field research and their locations to the app.

[0369] Data Analysis Methods

[0370] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots. The server uses the SciKit-Learn library to apply a random forest algorithm to identify biodiversity hotspots from the collected data. The algorithm is pre-trained with training data.

[0371] Example: The server uses a random forest algorithm to identify biodiversity hotspots in rainy regions.

[0372] Prompt: Using the collected plant species and location information, run a random forest algorithm to identify biodiversity hotspots.

[0373] Guideline formulation method

[0374] The server formulates guidelines based on the analysis results and generates a report.The server uses a report generation tool (e.g., LaTeX or a PDF generation library) to create a document containing protection guidelines and measures based on the analysis results.

[0375] Example: The server generates reports of protection measures based on climate zone and season.

[0376] Prompt: Generate a report containing conservation measures for biodiversity hotspots.

[0377] Feedback collection methods

[0378] The terminal displays the generated report to the user and collects feedback. The terminal displays the report through a user interface and provides a form for accepting ratings and comments from the user. The feedback is sent to the server and automatically saved.

[0379] Example: The device provides an interface for users to view reports and collect ratings and comments.

[0380] Prompt: Displays an interface that allows the user to view the report and enter ratings and comments.

[0381] Emotion Engine

[0382] The device uses an emotion engine to analyze the user's emotions when the user enters feedback. The device uses a built-in camera and microphone to record the user's facial expressions and tone of voice, and analyzes them using the emotion engine. The analysis results are used to evaluate the user's satisfaction and interest.

[0383] Example: The device uses a camera and microphone to analyze the user's facial expressions and tone of voice to understand the user's emotional state.

[0384] Prompt text: Uses the camera and microphone to analyze the user's facial expressions and tone of voice to understand the user's emotional state.

[0385] System Update Method

[0386] The server updates the system based on feedback and the results of the sentiment engine analysis, continuously collecting and analyzing data, and optimizing Japan's unique biodiversity conservation standards. The server analyzes the feedback database and sentiment analysis results to identify areas for improvement and update the system's algorithms and data collection methods, thereby adding new items to the next data collection and analysis.

[0387] Example: The server reflects user feedback and sentiment analysis and adds new items to the next data collection and analysis.

[0388] Prompt: We will implement system updates to improve our data collection and analysis processes based on user feedback and sentiment analysis.

[0389] In this way, the system of the present invention will establish biodiversity conservation standards unique to Japan and enable the implementation of optimal conservation measures for each region based on high-quality feedback that also takes user emotions into consideration.

[0390] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0391] Step 1:

[0392] Data collection

[0393] The server collects biodiversity data from public and private databases both domestically and internationally. It runs a regularly scheduled task to access each database using an API key and download species lists and habitat data.

[0394] Input: API key, database URL

[0395] Output: Biodiversity data in JSON and CSV format

[0396] Specific operation: The server uses a Python script to retrieve data from the API, sending an API request and saving the retrieved JSON or CSV data in local storage.

[0397] Step 2:

[0398] Standardization and organization of data formats

[0399] The server aligns and organizes the collected data. It uses the Python Pandas library to read the data and convert it from different formats (CSV, JSON, XML, etc.) into a unified data frame format. It removes duplicate data and completes missing information.

[0400] Input: Biodiversity data (JSON, CSV, XML)

[0401] Output: Cleaned data frame

[0402] Specific operation: The server converts the data collected from each data source into a Pandas data frame, standardizes the format, removes duplicate data, and completes missing data.

[0403] Step 3:

[0404] Sensing data input

[0405] Users input data acquired on-site into the system, using a smartphone or tablet to upload photos taken during field surveys and location information to a dedicated app.

[0406] Input: Plant photo, location information

[0407] Output: Sensing data (photo files, location information) uploaded to the server

[0408] Specific operation: The user presses the photo upload button in the app, selects the photo they took, confirms that the location information is automatically acquired, and uploads it. This sends the data to the server.

[0409] Step 4:

[0410] Data analysis

[0411] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots. It uses the SciKit-Learn library to apply a random forest algorithm to identify biodiversity hotspots from the collected data.

[0412] Input: Prepared data frame, sensing data

[0413] Output: List of hotspots

[0414] How it works: The server trains a random forest algorithm with training data, and then inputs newly collected data into the model to identify hotspots.

[0415] Step 5:

[0416] Guideline formulation and report generation

[0417] The server formulates guidelines based on the analysis results and generates a report. Using the report generation tool, the server creates a document of protection guidelines and measures based on the analysis results.

[0418] Input: List of critical areas, past guidelines

[0419] Output: Report in PDF format

[0420] Specific Actions: The server uses LaTeX and PDF generation libraries to create a report containing specific protection measures based on the identified hotspots and outputs it in PDF format.

[0421] Step 6:

[0422] Collecting feedback

[0423] The terminal displays the generated report to the user and collects feedback. The terminal displays the report through a user interface and provides a form for accepting ratings and comments from the user.

[0424] Input: Report in PDF format

[0425] Output: User feedback (ratings, comments)

[0426] Specific operation: The terminal provides a report display screen, receives user ratings and comments through a feedback input form, and sends them to the server.

[0427] Step 7:

[0428] Emotion analysis using an emotion engine

[0429] The device uses an emotion engine to analyze the user's emotions when they provide feedback, and uses the built-in camera and microphone to record and analyze the user's facial expressions and tone of voice.

[0430] Input: User's facial expression data, voice data

[0431] Output: Analysis of the user's emotional state (satisfaction, dissatisfaction, interest)

[0432] Specific operation: The device uses a camera and microphone to record the user's facial expressions and voice in real time, and analyzes their emotional state using an emotion engine. The analysis results are sent to the server as evaluation data.

[0433] Step 8:

[0434] System Updates

[0435] The server updates the system based on feedback and the results of the sentiment engine analysis, continuously collects and analyzes data, and optimizes Japan's unique biodiversity conservation standards. Feedback data and sentiment analysis results are analyzed to identify areas for improvement, and the system's algorithms and data collection methods are updated.

[0436] Input: User feedback, sentiment analysis results

[0437] Output: Updated data acquisition and analysis system

[0438] Specific operation: Based on the feedback and sentiment analysis, the server adds improvements to be reflected in the next data collection and analysis, and updates the system code.

[0439] (Application example 2)

[0440] 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."

[0441] Biodiversity conservation and management requires the efficient collection, integration, and analysis of vast amounts of data to formulate optimal guidelines for each region. However, it is necessary to collect not only on-site feedback but also high-quality feedback that combines sentiment analysis. It is also necessary to collect environmental data from physical stores, evaluate eco-friendliness based on that data, and develop a system for continuous improvement.

[0442] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0443] In this invention, the server includes means for collecting biodiversity data from domestic and international databases, means for standardizing and organizing the collected data, means for inputting sensing data from field surveys, means for analyzing the data using a machine learning algorithm and identifying priority areas, means for formulating guidelines and generating reports based on the analysis results, means for displaying the generated reports to users and collecting feedback, means for analyzing user emotions using a sentiment analysis engine, means for collecting environmental data at physical stores, means for conducting eco-friendliness evaluations based on the collected environmental data and generating guidelines, means for collecting customer feedback after using the store, means for updating the system based on the collected feedback, and means for analyzing customers' facial expressions and voices to understand their emotional states, thereby enabling the formulation of guidelines for biodiversity protection and the evaluation and improvement of eco-friendly initiatives at physical stores.

[0444] A "database" is a collection of data that is organized to efficiently manage and search large amounts of information.

[0445] "Biodiversity" means the variety of different living species and the ecosystems they use.

[0446] "Data collection methods" are the means by which the necessary information is gathered from various sources.

[0447] "Data preparation means" refers to the means of unifying collected data into a consistent format and making it usable.

[0448] "Sensing data" refers to data about the environment and living organisms obtained through field surveys.

[0449] "Data analysis means" refers to a means of analyzing collected data using machine learning algorithms, etc.

[0450] "Guideline formulation means" refers to the means of creating appropriate guidelines for action based on the results of data analysis.

[0451] The "report generation means" is a means for organizing the analysis results and outputting them as a document.

[0452] "Feedback collection means" refers to the means of collecting evaluations and opinions from system users.

[0453] An "emotion analysis engine" is software or a system that analyzes a user's facial expressions and voice to understand their emotional state.

[0454] "Environmental Data" refers to data about the surrounding environment, such as temperature, humidity, CO2 levels, etc.

[0455] "Eco-friendly evaluation" is a process that evaluates whether a product has a low environmental impact.

[0456] "System update methods" are methods for adapting and improving the entire system based on collected feedback and analysis results.

[0457] The system of this invention is composed of many elements and is realized through the interaction of three entities: a server, a terminal, and a user. The purpose of this system is to support the conservation of biodiversity and the evaluation and improvement of eco-friendly efforts in physical stores.

[0458] Hardware and Software Configuration

[0459] 1. Data Collection

[0460] The server will collect biodiversity data from domestic and international databases, including the International Biodiversity Information Facility, and retrieve data via API.

[0461] 2. Data Preparation

[0462] The server standardizes and organizes the collected data. It performs a data conversion process to standardize the data into a specific format and remove duplicate and missing data.

[0463] 3. Input of sensing data

[0464] Users input sensing data obtained during field surveys into the system, including photos of plants collected during the field survey and their location information.

[0465] 4. Data Analysis

[0466] The server then uses machine learning algorithms to analyze the collected data and identify important biodiversity hotspots, using algorithms such as random forests in the process.

[0467] 5. Guideline formulation and report generation

[0468] Based on the analysis, the server develops guidelines and generates a report detailing protective measures for different climate zones and seasons.

[0469] 6. Gathering Feedback

[0470] The terminal displays the generated report to the user and provides an interface for collecting feedback. The user can view the report and enter their rating and comments.

[0471] 7. Emotion analysis

[0472] The device uses an emotion analysis engine to analyze the user's emotions. It uses a camera (e.g., Logitech C920) and a microphone (e.g., Blue Yeti) to analyze the user's facial expressions and tone of voice to understand their emotional state.

[0473] 8. System Updates

[0474] The server updates the system based on the collected feedback and sentiment analysis results, allowing new items to be added to the next data collection and analysis, enabling continuous improvement of the system.

[0475] Examples:

[0476] For example, the server downloads the latest biodiversity species list from the International Biodiversity Information Facility via API and integrates it with data from other databases. Researchers then upload photos of plants taken in the field and their location information. The server analyzes this data using a random forest algorithm to identify biodiversity hotspots in rainy regions. The generated report is displayed on the device for users to view, and feedback is collected. A sentiment analysis engine analyzes users' facial expressions and tone of voice to determine their level of satisfaction or dissatisfaction. Finally, the server uses the collected feedback and the results of the sentiment analysis to reflect this in the next system update.

[0477] Example prompt sentence:

[0478] "Provide a concrete example of an eco-shop management application that generates optimal guidelines for biodiversity conservation. Include a function that uses a random forest algorithm to evaluate the shop's eco-friendliness based on environmental data collected within the shop (e.g., temperature, humidity, CO2 levels), and report areas for improvement. Also explain how customer feedback and sentiment analysis can be used to improve store operations."

[0479] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0480] Step 1:

[0481] The server collects biodiversity data from domestic and international databases. As input, it uses data obtained from the International Biodiversity Information Facility via API. The data downloaded from the database is in its raw state. As output, it obtains the collected biodiversity dataset.

[0482] Step 2:

[0483] The server standardizes and organizes the collected data. As input, it uses the biodiversity data collected in step 1. To standardize the data, it converts different data structures into a common format and removes duplicates and missing data. As output, it obtains a uniformly formatted dataset.

[0484] Step 3:

[0485] Users input sensing data obtained from field surveys into the system. The input includes photos of the surveyed plants, location information, and environmental data (temperature, humidity, etc.). This data is raw data obtained from the field survey. The output is the field survey data uploaded to the system.

[0486] Step 4:

[0487] The server analyzes the data using machine learning algorithms to identify important biodiversity hotspots. As input, it uses the dataset prepared in step 2 and the sensing data uploaded in step 3. It applies algorithms such as random forests and performs analysis. As output, it obtains a list of important areas.

[0488] Step 5:

[0489] The server formulates guidelines and generates reports based on the analysis results. It uses the list of critical areas obtained in step 4 as input. It sets guidelines including protective measures according to climate zones and seasons, and creates and outputs reports based on them.

[0490] Step 6:

[0491] The terminal displays the generated report to the user and collects feedback. As input, it uses the report generated in step 5. The user enters feedback after viewing the report. As output, it obtains the user's feedback data.

[0492] Step 7:

[0493] The device analyzes the user's emotions using an emotion analysis engine. The input is the user's facial expression data and voice data collected by a camera and microphone. Specifically, the emotion analysis software performs facial expression recognition and voice tone analysis. The output is the user's emotion data.

[0494] Step 8:

[0495] The server updates the system based on the collected feedback and sentiment analysis results. As input, it uses the feedback data obtained in step 6 and the sentiment data obtained in step 7. It performs processing to improve the analysis algorithms and data collection methods of the entire system. As output, it obtains updated data collection and analysis models.

[0496] This will enable the development of guidelines for biodiversity conservation and the evaluation and improvement of eco-friendly initiatives in physical stores.

[0497] 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.

[0498] 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.

[0499] 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.

[0500] [Second embodiment]

[0501] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0502] 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.

[0503] 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).

[0504] 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.

[0505] 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.

[0506] 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).

[0507] 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.

[0508] 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.

[0509] 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.

[0510] 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.

[0511] 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.

[0512] 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."

[0513] The present invention relates to a system for efficiently collecting, integrating, and analyzing huge amounts of data in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. Specific embodiments of the system of the present invention are described below.

[0514] System Overview

[0515] The system includes the following elements:

[0516] 1. Data collection method: The server accesses domestic and international databases to collect data on biodiversity.

[0517] 2. Data preparation method: The server standardizes and organizes the collected data.

[0518] 3. Sensing data input method: The user inputs the sensing data acquired during the field survey into the system.

[0519] 4. Data analysis method: The server uses machine learning algorithms to analyze the data and identify key areas.

[0520] 5. Guideline formulation means: The server formulates guidelines based on the analysis results and generates a report.

[0521] 6. Feedback means: The terminal displays the generated report to the user and collects feedback.

[0522] 7. System update method: The server updates the system based on feedback, continuously collecting and analyzing data to improve it.

[0523] Natural language explanation of program processing

[0524] 1. Data Collection

[0525] The server collects biodiversity data from public and private databases both domestically and internationally.

[0526] Example: A server downloads the latest species list from the International Biodiversity Information Facility via an API.

[0527] 2. Standardization and organization of data formats

[0528] The server compiles and organizes the collected data.

[0529] Example: The server converts data collected from different databases into a unified format and eliminates duplicate data.

[0530] 3. Input of sensing data

[0531] The user inputs the data acquired on-site into the system.

[0532] Example: A user uploads photos and location information of plants collected during a field survey.

[0533] 4. Data Analysis

[0534] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots.

[0535] Example: The server uses a random forest algorithm to identify biodiversity hotspots in rainy regions.

[0536] 5. Guideline formulation and report generation

[0537] The server formulates guidelines based on the analysis results and generates a report.

[0538] Example: The server generates reports of protection measures based on climate zone and season.

[0539] 6. Gathering Feedback

[0540] The terminal displays the generated report to the user and collects feedback.

[0541] Example: The device provides an interface for users to view reports and collect their ratings and comments.

[0542] 7. System Updates

[0543] The server updates the system based on feedback, continuously collecting and analyzing data, and optimizing Japan's unique biodiversity conservation standards.

[0544] Example: The server reflects user feedback and adds new items to the next data collection and analysis.

[0545] In this way, the system of the present invention will establish biodiversity conservation standards unique to Japan, enabling optimal conservation measures to be implemented in each region.

[0546] The processing flow will be explained below.

[0547] Step 1:

[0548] The server will access domestic and international databases and collect data on biodiversity.

[0549] Specific behavior:

[0550] 1. The server uses the API to send requests to the International Biodiversity Information Facility and other authoritative data sources.

[0551] 2. The server stores the acquired data in a local database for temporary storage.

[0552] Step 2:

[0553] The server standardizes the format of the collected data and organizes it.

[0554] Specific behavior:

[0555] 1. The server detects duplicate data and removes the duplicate records.

[0556] 2. The server converts data from different formats into a unified format, organizing the data into common fields such as species name, region, and publication year.

[0557] Step 3:

[0558] The user inputs sensing data acquired on-site into the system.

[0559] Specific behavior:

[0560] 1. The user logs into the system using a dedicated terminal or a web interface.

[0561] 2. The user enters the collected data (e.g., plant photos, latitude and longitude information, etc.) into the upload form.

[0562] 3. The sensing data is sent to the server and stored in a local database.

[0563] Step 4:

[0564] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots.

[0565] Specific behavior:

[0566] 1. The server pre-processes the data and creates a dataset for analysis.

[0567] 2. The server applies a machine learning model (e.g., random forest algorithm) to detect biodiversity hotspots in a specific area.

[0568] 3. The analysis results will be saved in a specified folder on the server.

[0569] Step 5:

[0570] The server formulates guidelines based on the analysis results and generates a report.

[0571] Specific behavior:

[0572] 1. The server extracts the necessary information from the analysis results.

[0573] 2. The server creates a report according to a standard format and generates it in PDF format.

[0574] 3. The report is saved to the specified folder on the server.

[0575] Step 6:

[0576] The terminal displays the generated report to the user and collects feedback.

[0577] Specific behavior:

[0578] 1. After user authentication, the terminal provides an interface to display the generated report.

[0579] 2. Users view the report and use the feedback form to provide their ratings and comments.

[0580] 3. The feedback is sent to the server.

[0581] Step 7:

[0582] The server updates the system based on feedback collected from users, and continuously collects, analyzes, and improves data.

[0583] Specific behavior:

[0584] 1. The server analyzes user feedback and identifies areas for improvement.

[0585] 2. The server updates the system configuration to incorporate new data collection and analysis algorithms.

[0586] 3. The server periodically re-runs the data collection and analysis and updates the reports.

[0587] Through these steps, the system will establish biodiversity conservation standards unique to Japan, enabling optimal conservation measures to be implemented in each region.

[0588] Example 1

[0589] 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."

[0590] Biodiversity conservation and management requires the collection, organization, and analysis of large amounts of data. However, this process is time-consuming and labor-intensive, making it difficult to carry out efficiently. Furthermore, to formulate optimal conservation guidelines for each region, it is necessary to effectively integrate and analyze field survey data and existing data. Furthermore, it is also necessary to reflect feedback and continuously improve the system. A system that can solve this problem and implement efficient and accurate biodiversity conservation measures is needed.

[0591] 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.

[0592] In this invention, the server includes means for collecting information on biodiversity from domestic and international sources, means for converting the collected information into a unified format and organizing it, means for inputting sensing information from field surveys, means for analyzing the information using a machine learning algorithm and identifying important ecological regions, means for formulating conservation guidelines and generating documents based on the analysis results, means for displaying the generated documents to users and collecting evaluations, and means for updating the system based on the evaluations and continuously collecting, analyzing, and improving the information. This enables the efficient collection, integration, and analysis of data necessary for biodiversity protection and management, and the formulation and implementation of optimal conservation measures for each region.

[0593] "Biodiversity" refers to the variety of mutations, species, individuals, and ecosystems of living organisms on Earth.

[0594] "Sources" refers to national and international databases and institutions that provide biodiversity data.

[0595] "Unified format" refers to converting data provided in different formats into a consistent data format.

[0596] "Maintenance" refers to the process of removing duplicates and missing data and cleansing the collected data.

[0597] "Sensing information" refers to data obtained during field surveys (e.g., photographs, GPS coordinates, observation notes, etc.).

[0598] "Machine learning algorithms" refer to computer algorithms that analyze large amounts of data to generate patterns and make predictions.

[0599] "Critical ecoregions" refer to specific areas that are rich in biodiversity and require protection.

[0600] "Conservation guidelines" refer to specific guidelines and measures for effectively promoting biodiversity conservation.

[0601] "Document" refers to reports and reports containing analytical findings and conservation guidelines.

[0602] "User" refers to the person or organization that uses the system to input, analyze and evaluate biodiversity data.

[0603] "Evaluation" refers to feedback and suggestions for improvement that users give to the generated document.

[0604] "Update" refers to the process of improving the system based on collected evaluations and making improvements to enable more effective data collection and analysis.

[0605] The present invention relates to a system for efficiently collecting, integrating, and analyzing vast amounts of information in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. Specific embodiments of the present invention are described below.

[0606] Data collection

[0607] The server collects biodiversity information from domestic and international sources, including databases from international biodiversity information agencies and local environmental organizations. The server retrieves data from these sources via APIs. For example, the server automatically downloads the latest species list from the international biodiversity information agency.

[0608] Standardization and organization of data formats

[0609] The server converts the collected information into a unified format and organizes it. This includes converting data provided in different formats (e.g., CSV, JSON, XML) into a unified format. The server also performs data cleansing to remove duplicate data and missing values.

[0610] Sensing data input

[0611] Users input sensing information acquired during field surveys into the system. Using a dedicated mobile app, users input photos of plants taken on-site, GPS coordinates, observation notes, etc. This information is sent to the server in real time and stored in a database. As a specific example, a user may upload a photo of a new species of plant during a field survey and input its location information and observation notes.

[0612] Data analysis

[0613] The server uses machine learning algorithms to analyze the collected and organized information and identify important ecological regions. It uses Python machine learning libraries (e.g., scikit-learn and TensorFlow) to analyze areas with high biodiversity based on large amounts of data. For example, the server uses a random forest algorithm to identify biodiversity hotspots in areas with high rainfall.

[0614] Guideline formulation and report generation

[0615] The server formulates protection guidelines based on the analysis results and generates a document. A generative AI model (e.g., OpenAI's GPT-4) is used to automatically generate guidelines outlining specific protection measures. These guidelines are output as a PDF report and can be downloaded by users through a web portal. A specific example would be a scenario in which the server generates a report stating that "in areas with an annual rainfall of 1000 mm or more, protection of certain vegetation types is necessary."

[0616] Collecting feedback

[0617] The terminal displays the generated document to the user and collects their evaluation. The user is provided with an interface that allows them to check the report on the terminal and enter their evaluation of the generated guidelines and suggestions for improvement. For example, the user may submit comments on whether a specific guideline is feasible or what improvements can be made.

[0618] System Updates

[0619] The server updates the system based on the evaluations and continuously collects, analyzes, and improves the information. It analyzes user feedback to improve the machine learning model and data collection process. For example, the next time data is collected, new data items (such as soil pH data) are added and incorporated into the analysis algorithm.

[0620] Examples of prompt statements

[0621] As an example of a specific prompt, the following might be used when a user uploads data collected during a field survey:

[0622] "Please upload photos of plants taken during your field surveys and their locations into the system, along with notes on the habitat and weather conditions in which you observed them."

[0623] And when the server identifies new biodiversity hotspots and generates guidelines, the prompt is:

[0624] "Calculate and identify biodiversity hotspots in high-rainfall areas using up-to-date rainfall and vegetation data. Then, based on the identified hotspots, develop guidelines and generate reports for conservation measures appropriate for the area."

[0625] In this way, the present invention is a system that enables the efficient collection, integration, and analysis of data necessary for the protection and management of biodiversity, and supports the formulation and implementation of optimal conservation measures for each region.

[0626] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0627] Step 1: Data collection

[0628] The server collects information on biodiversity from domestic and international sources.

[0629] Input: API endpoints from databases such as international biodiversity information agencies and local environmental protection agencies.

[0630] Output: Raw datasets (e.g., species lists, meteorological data, vegetation data)

[0631] Specific operation: The server periodically sends API requests to retrieve data and saves it in local storage. For example, a CRON job can be used to automatically retrieve the latest data at 3:00 AM every day.

[0632] Step 2: Standardize and organize data formats

[0633] The server converts the collected information into a unified format and organizes it.

[0634] Input: Raw datasets acquired in different formats (CSV, JSON, XML).

[0635] Output: A uniformly formatted dataset

[0636] What happens: The server uses ETL (Extract, Transform, Load) tools to transform the data into a consistent format. Data cleansing scripts correct and remove duplicate entries and missing values. Data frame manipulations are performed, for example, using the Python Pandas library.

[0637] Step 3: Input of sensing data

[0638] The user inputs sensing information acquired during field surveys into the system.

[0639] Input: Data obtained during field surveys (plant photos, GPS coordinates, observation notes, etc.).

[0640] Output: Sensing information stored on the server

[0641] Specific operation: A user uses a mobile app to input photos and text data, which the app then uploads to a server. For example, a user might upload a photo of a new species discovered in the Southern Alps region and enter its GPS coordinates.

[0642] Step 4: Data analysis

[0643] The server uses machine learning algorithms to analyze the collected and organized information and identify important ecological regions.

[0644] Input: Organized dataset (integrated data of collected data and sensing data)

[0645] Output: List of important ecological regions and their analysis results

[0646] How it works: The server uses Python machine learning libraries (scikit-learn, TensorFlow) to apply algorithms (e.g., random forests) to analyze the data, for example, to identify biodiversity hotspots based on rainfall and local vegetation data.

[0647] Step 5: Guideline development and report generation

[0648] The server formulates protection guidelines based on the analysis results and generates a document.

[0649] Input: List of important ecological regions and their analysis results

[0650] Output: PDF report with protection guidelines

[0651] How it works: The server uses a generative AI model (such as OpenAI's GPT-4) to automatically generate protection guidelines based on the analysis results, and creates a report using a PDF generation library, which users can download from a web portal.

[0652] Step 6: Gather feedback

[0653] The terminal displays the generated document to the user and collects ratings.

[0654] Input: User evaluation of the report and suggestions for improvement

[0655] Output: Feedback information stored in the rating database

[0656] Specific operation: The user views the report on their device and enters comments and suggestions using the feedback form. This information is sent to the server in real time and stored in the database. For example, the user can enter feedback such as "This part is specific and easy to understand" or "This part is unclear and needs improvement."

[0657] Step 7: Update your system

[0658] The server updates the system based on the evaluation and continuously collects and analyzes information to improve it.

[0659] Input: Feedback information stored in the ratings database

[0660] Output: Improved data collection and analysis processes and updated systems

[0661] Specific operation: The server analyzes the feedback and adds new data items or improves the algorithm. For example, the next time data is collected, new soil pH data is collected and incorporated into the analysis algorithm. This improves the accuracy and efficiency of the entire system.

[0662] (Application example 1)

[0663] 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."

[0664] Conventional biodiversity conservation management systems are inefficient because they require a lot of manpower and time for data collection and analysis, and they lack management measures that are particularly adapted to the environment inside and outside factories. As a result, it has been difficult to formulate appropriate guidelines for minimizing the impact on ecosystems. To solve this problem, a system that allows for efficient and accurate data collection and analysis, and that takes into account practicality in the field, is needed.

[0665] 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.

[0666] In this invention, the server includes means for collecting data on biodiversity from domestic and international databases, means for standardizing and organizing the collected data, means for inputting sensing data from field surveys, means for analyzing the data using a machine learning algorithm and identifying important areas, means for formulating guidelines and generating reports based on the analysis results, means for displaying the generated reports to users and collecting feedback, and a robot that collects data from inside and outside a factory and proposes optimal management measures for ecosystem protection, including means for transmitting the data collected by the robot to the server and for the server to analyze the data, and means for generating the analysis results as guidelines and displaying them via the robot. This enables efficient and accurate data collection and analysis, and enables the formulation of appropriate guidelines that take practicality in the field into consideration.

[0667] A "database" is a collection of specific information that is systematically organized and stored so that it can be easily searched and retrieved.

[0668] "Biodiversity" is a term that refers to the variety of living species on Earth, their genetic variations, and the ecosystems they comprise.

[0669] "Data collection methods" are methods for obtaining information on biodiversity from domestic and international databases.

[0670] "Data preparation means" refers to the means of standardizing and organizing the collected data.

[0671] "Sensing data" refers to data on biodiversity obtained through field surveys.

[0672] A "machine learning algorithm" is a program that analyzes data and performs pattern recognition and prediction.

[0673] "Data analysis means" refers to a means of analyzing data using machine learning algorithms and identifying important areas.

[0674] A "guideline" is a document that provides guidelines or standards established to achieve a specific purpose.

[0675] The "report generation means" is a means for creating guidelines based on the analysis results and outputting them in document format.

[0676] The "feedback means" is a means for displaying the generated report to the user and collecting evaluations and comments from the user.

[0677] "Inside and outside the factory" is a term that refers to the inside of the factory and the surrounding area.

[0678] "Robots" are autonomous or semi-autonomous machines that collect biodiversity data from inside and outside factories and suggest management measures.

[0679] A "server" is a computer system that processes, stores, and provides data over a network.

[0680] The "analysis means" is a means for transmitting the collected data to a server and analyzing it using a machine learning algorithm.

[0681] The "display means" is a means for generating the analysis results as guidelines and displaying the information via the robot.

[0682] This invention relates to a system for efficiently collecting, integrating, and analyzing huge amounts of data in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. This system utilizes a robot that collects data from inside and outside factories and proposes optimal management measures for ecosystem protection.

[0683] 1. Data Collection

[0684] The server collects biodiversity information from domestic and international databases, such as the Global Biodiversity Information Facility (GBIF) and retrieves the latest species list via an API. The server also receives data collected by the robots inside and outside the factory and stores it in a database.

[0685] 2. Data Preparation

[0686] The server standardizes and organizes the collected data. This includes converting data collected from different databases into a unified format, eliminating duplicate data, normalizing data, etc. Organized data is important for improving the accuracy of subsequent data analysis.

[0687] 3. Input of sensing data

[0688] The user (or robot) inputs sensing data acquired on-site into the system. For example, they upload photos of plants collected during field surveys along with their location information. This data is then used for analysis on the server.

[0689] 4. Data Analysis

[0690] The server will analyze the data using machine learning algorithms to identify important biodiversity hotspots. For example, it will use a random forest algorithm to identify biodiversity hotspots in areas with high rainfall. The results of this analysis will form the basis for developing guidelines.

[0691] 5. Establishment of guidelines

[0692] The server then formulates guidelines based on the analysis results and generates reports, such as protection measures based on climate zones and seasons, and provides them to users. These guidelines include specific procedures and monitoring methods for ecosystem protection.

[0693] 6. Gathering Feedback

[0694] The terminal displays the generated report to the user and provides an interface for collecting feedback. For example, after viewing the report, the user can enter their rating and comments. This feedback is used to improve the system.

[0695] 7. System Updates

[0696] The server will update the system based on feedback and continuously improve data collection and analysis, thereby establishing Japan's own biodiversity conservation standards and implementing optimal conservation measures for each region.

[0697] Hardware and software used

[0698] Hardware:

[0699] Robots: Devices that collect data from inside and outside the factory.

[0700] Server: A computer system that processes and stores data.

[0701] software:

[0702] requests: A Python package for retrieving data from data collection APIs.

[0703] scikit-learn: A Python library for running machine learning algorithms (e.g., the Random Forest algorithm).

[0704] Use of such a system will enable efficient and accurate data collection and analysis, making it possible to formulate appropriate guidelines that take into account practicality in the field.

[0705] Examples of concrete examples and prompts

[0706] Examples:

[0707] For example, in high humidity areas of a factory, guidelines may be created such as "maintain specific plant habitats and conduct monthly monitoring."

[0708] Example prompt sentence:

[0709] "Implement a detailed ecosystem analysis using data obtained from API and a guideline development program based on the results."

[0710] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0711] Step 1:

[0712] The server collects biodiversity information from domestic and international databases. Specifically, the server uses APIs to send requests to each database and temporarily stores the acquired data. The input is the API endpoint and authentication information, and the output is the acquired biodiversity data.

[0713] Step 2:

[0714] The server standardizes and organizes the collected data. Because the acquired data is in different formats, the server converts it into a standard format and eliminates duplicate data. This process is called data cleaning and is important for improving the accuracy of data analysis. The input is raw biodiversity data, and the output is organized data.

[0715] Step 3:

[0716] Users input sensing data acquired during field surveys into the system. For example, they upload photos of plants taken during field surveys and GPS location information. This data is sent to the server and integrated with other data. The input is sensing data from field surveys, and the output is the integrated data uploaded to the server.

[0717] Step 4:

[0718] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots. For example, a random forest algorithm can be used to analyze the data and identify high biodiversity areas. The server inputs the data into the model and obtains a list of important areas as output. The input is the consolidated data, and the output is the analysis results.

[0719] Step 5:

[0720] The server formulates guidelines based on the analysis results and generates a report. Based on the analysis results, it creates a document detailing ecosystem protection measures for each region and formats it as a report. The input is the results of the data analysis, and the output is the generated report.

[0721] Step 6:

[0722] The terminal provides an interface for displaying the generated report to the user and collecting feedback. The user views the report and enters their evaluation and comments on its contents as feedback. The input is the generated report and the user's evaluation and comments, and the output is the collected feedback.

[0723] Step 7:

[0724] The server updates the system based on the feedback and continuously improves data collection and analysis. It analyzes the feedback information, makes necessary corrections and adds features, and improves the accuracy and usability of the entire system. The input is user feedback, and the output is an updated system.

[0725] 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.

[0726] The present invention relates to a system for efficiently collecting, integrating, and analyzing vast amounts of data in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. Furthermore, by combining it with an emotion engine that recognizes user emotions, the quality of user feedback is improved, contributing to the improvement of the entire system. A specific embodiment of the system of the present invention is described below.

[0727] System Overview

[0728] The system includes the following elements:

[0729] 1. Data collection method: The server accesses domestic and international databases to collect data on biodiversity.

[0730] 2. Data preparation method: The server standardizes and organizes the collected data.

[0731] 3. Sensing data input method: The user inputs the sensing data acquired during the field survey into the system.

[0732] 4. Data analysis method: The server uses machine learning algorithms to analyze the data and identify key areas.

[0733] 5. Guideline formulation means: The server formulates guidelines based on the analysis results and generates a report.

[0734] 6. Feedback means: The terminal displays the generated report to the user and collects feedback.

[0735] 7. Emotion engine: The emotion engine built into the device analyzes the user's emotions and understands their emotional state at the time of feedback.

[0736] 8. System update method: The server updates the system based on feedback and analysis results from the emotion engine, and continuously collects, analyzes, and improves data.

[0737] Natural language explanation of program processing

[0738] 1. Data Collection

[0739] The server collects biodiversity data from public and private databases both domestically and internationally.

[0740] Example: A server downloads the latest species list from the International Biodiversity Information Facility via an API.

[0741] 2. Standardization and organization of data formats

[0742] The server compiles and organizes the collected data.

[0743] Example: The server converts data collected from different databases into a unified format and eliminates duplicate data.

[0744] 3. Input of sensing data

[0745] The user inputs the data acquired on-site into the system.

[0746] Example: A user uploads photos and location information of plants collected during a field survey.

[0747] 4. Data Analysis

[0748] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots.

[0749] Example: The server uses a random forest algorithm to identify biodiversity hotspots in rainy regions.

[0750] 5. Guideline formulation and report generation

[0751] The server formulates guidelines based on the analysis results and generates a report.

[0752] Example: The server generates reports of protection measures based on climate zone and season.

[0753] 6. Gathering Feedback

[0754] The terminal displays the generated report to the user and collects feedback.

[0755] Example: The device provides an interface for users to view reports and collect their ratings and comments.

[0756] 7. Emotion Analysis Using an Emotion Engine

[0757] The terminal analyzes the user's emotions using an emotion engine when the user inputs feedback.

[0758] Example: The device uses a camera and microphone to analyze the user's facial expressions and tone of voice to understand the user's emotional state (e.g., satisfaction, dissatisfaction, interest).

[0759] 8. System Updates

[0760] The server updates the system based on feedback and the results of the emotion engine analysis, continuously collecting and analyzing data to optimize Japan's unique biodiversity conservation standards.

[0761] Example: The server reflects user feedback and sentiment analysis and adds new items to the next data collection and analysis.

[0762] In this way, the system of the present invention will establish biodiversity conservation standards unique to Japan and enable the implementation of optimal conservation measures for each region based on high-quality feedback that also takes user emotions into consideration.

[0763] The processing flow will be explained below.

[0764] Step 1:

[0765] The server will access domestic and international databases and collect data on biodiversity.

[0766] Specific behavior:

[0767] 1. The server uses the API to send requests to the International Biodiversity Information Facility and other authoritative data sources.

[0768] 2. The server stores the acquired data in a local database for temporary storage.

[0769] Step 2:

[0770] The server standardizes the format of the collected data and organizes it.

[0771] Specific behavior:

[0772] 1. The server detects duplicate data and removes the duplicate records.

[0773] 2. The server converts data from different formats into a unified format, organizing the data into common fields such as species name, region, and publication year.

[0774] Step 3:

[0775] The user inputs sensing data acquired on-site into the system.

[0776] Specific behavior:

[0777] 1. The user logs into the system using a dedicated terminal or a web interface.

[0778] 2. The user enters the collected data (e.g., plant photos, latitude and longitude information, etc.) into the upload form.

[0779] 3. The sensing data is sent to the server and stored in a local database.

[0780] Step 4:

[0781] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots.

[0782] Specific behavior:

[0783] 1. The server pre-processes the data and creates a dataset for analysis.

[0784] 2. The server applies a machine learning model (e.g., random forest algorithm) to detect biodiversity hotspots in a specific area.

[0785] 3. The analysis results will be saved in a specified folder on the server.

[0786] Step 5:

[0787] The server formulates guidelines based on the analysis results and generates a report.

[0788] Specific behavior:

[0789] 1. The server extracts the necessary information from the analysis results.

[0790] 2. The server creates a report according to a standard format and generates it in PDF format.

[0791] 3. The report is saved to the specified folder on the server.

[0792] Step 6:

[0793] The terminal displays the generated report to the user and collects feedback.

[0794] Specific behavior:

[0795] 1. After user authentication, the terminal provides an interface to display the generated report.

[0796] 2. Users view the report and use the feedback form to provide their ratings and comments.

[0797] 3. The feedback is sent to the server.

[0798] Step 7:

[0799] The terminal analyzes the user's emotions using an emotion engine when the user inputs feedback.

[0800] Specific behavior:

[0801] 1. The device uses a camera and microphone to record the user's facial expressions and tone of voice.

[0802] 2. The device uses an emotion engine to analyze this data and determine the user's emotional state.

[0803] 3. The sentiment analysis results are sent to the server.

[0804] Step 8:

[0805] The server updates the system based on feedback collected from users and the analysis results of the emotion engine, and continuously collects, analyzes, and improves the data.

[0806] Specific behavior:

[0807] 1. The server analyzes user feedback and sentiment analysis results to identify areas for improvement.

[0808] 2. The server updates the system configuration to incorporate new data collection and analysis algorithms.

[0809] 3. The server periodically re-runs the data collection and analysis and updates the reports.

[0810] Through these steps, the system will establish Japan's own biodiversity conservation standards and enable optimal conservation measures to be implemented for each region. Furthermore, by taking user sentiment into account, the system will be able to reflect more effective feedback and improve the accuracy of the system.

[0811] Example 2

[0812] 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."

[0813] The problem that this invention aims to solve is to achieve sustainable conservation of biodiversity by efficiently collecting, integrating, and analyzing huge amounts of data and formulating optimal guidelines for each region based on the results. The invention also aims to provide a system that incorporates a function to recognize user emotions in order to improve the quality of user feedback and utilize it to improve the system.

[0814] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0815] In this invention, the server includes means for collecting biodiversity data from domestic and international databases, means for standardizing the format of the collected data and organizing it, means for inputting sensing data from field surveys, means for analyzing the data using a machine learning algorithm and identifying important areas, means for formulating guidelines and generating reports based on the analysis results, means for displaying the generated reports to users and collecting feedback, an emotion engine for analyzing user emotions, and means for updating the system based on the feedback and the analysis results of the emotion engine. This enables efficient data collection and analysis, the formulation of optimal guidelines for each area, improved quality of user feedback, and continuous improvement of the system.

[0816] "Data collection tools" are tools for collecting data on biodiversity from domestic and international databases.

[0817] "Data preparation means" refers to the means for standardizing and preparing the format of collected data.

[0818] The "sensing data input means" is a means for a user to input sensing data acquired during a field survey into the system.

[0819] "Data analysis means" refers to means for analyzing collected data using machine learning algorithms and identifying key areas.

[0820] The "guideline formulation means" is a means for formulating guidelines based on the analysis results and generating reports.

[0821] The "feedback collection means" is a means for displaying the generated report to the user and collecting feedback.

[0822] The "emotion engine" is an engine that has a function for analyzing the user's emotions.

[0823] The "system update means" is a means for updating the system based on the feedback and the analysis results of the emotion engine.

[0824] The present invention relates to a system for efficiently collecting, integrating, and analyzing vast amounts of data in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. Furthermore, by combining it with an emotion engine that recognizes user emotions, the quality of user feedback is improved, contributing to the improvement of the entire system. A specific embodiment of the system of the present invention is described below.

[0825] Data collection methods

[0826] The server collects biodiversity data from public and private databases both in Japan and overseas. The server runs a regularly scheduled task, accessing each database (e.g., databases of domestic and international biodiversity information facilities and the Ministry of the Environment) using an API key to download species lists and habitat data.

[0827] Example: A server downloads the latest species list from the International Biodiversity Information Facility via an API.

[0828] Prompt text: Use the following API key to get the latest species list from the International Biodiversity Information Facility.

[0829] Data preparation methods

[0830] The server aligns and organizes the collected data. It uses the Python Pandas library to read the data and convert it from different formats (CSV, JSON, XML, etc.) into a unified data frame format. It also removes duplicate data and completes missing information.

[0831] Example: The server converts data collected from different databases into a unified format and eliminates duplicate data.

[0832] Prompt: Transform biodiversity data from different data sources into a unified format and eliminate duplicate data.

[0833] Sensing data input means

[0834] Users input data acquired on-site into the system. Using a smartphone or tablet, users upload photos taken during field surveys and location information to a dedicated app. The app includes a photo upload button and an automatic location information acquisition function.

[0835] Example: A user uploads photos of plants taken during a field survey along with their location.

[0836] Prompt: Upload photos of plants taken during field research and their locations to the app.

[0837] Data Analysis Methods

[0838] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots. The server uses the SciKit-Learn library to apply a random forest algorithm to identify biodiversity hotspots from the collected data. The algorithm is pre-trained with training data.

[0839] Example: The server uses a random forest algorithm to identify biodiversity hotspots in rainy regions.

[0840] Prompt: Using the collected plant species and location information, run a random forest algorithm to identify biodiversity hotspots.

[0841] Guideline formulation method

[0842] The server formulates guidelines based on the analysis results and generates a report.The server uses a report generation tool (e.g., LaTeX or a PDF generation library) to create a document containing protection guidelines and measures based on the analysis results.

[0843] Example: The server generates reports of protection measures based on climate zone and season.

[0844] Prompt: Generate a report containing conservation measures for biodiversity hotspots.

[0845] Feedback collection methods

[0846] The terminal displays the generated report to the user and collects feedback. The terminal displays the report through a user interface and provides a form for accepting ratings and comments from the user. The feedback is sent to the server and automatically saved.

[0847] Example: The device provides an interface for users to view reports and collect ratings and comments.

[0848] Prompt: Displays an interface that allows the user to view the report and enter ratings and comments.

[0849] Emotion Engine

[0850] The device uses an emotion engine to analyze the user's emotions when the user enters feedback. The device uses a built-in camera and microphone to record the user's facial expressions and tone of voice, and analyzes them using the emotion engine. The analysis results are used to evaluate the user's satisfaction and interest.

[0851] Example: The device uses a camera and microphone to analyze the user's facial expressions and tone of voice to understand the user's emotional state.

[0852] Prompt text: Uses the camera and microphone to analyze the user's facial expressions and tone of voice to understand the user's emotional state.

[0853] System Update Method

[0854] The server updates the system based on feedback and the results of the sentiment engine analysis, continuously collecting and analyzing data, and optimizing Japan's unique biodiversity conservation standards. The server analyzes the feedback database and sentiment analysis results to identify areas for improvement and update the system's algorithms and data collection methods, thereby adding new items to the next data collection and analysis.

[0855] Example: The server reflects user feedback and sentiment analysis and adds new items to the next data collection and analysis.

[0856] Prompt: We will implement system updates to improve our data collection and analysis processes based on user feedback and sentiment analysis.

[0857] In this way, the system of the present invention will establish biodiversity conservation standards unique to Japan and enable the implementation of optimal conservation measures for each region based on high-quality feedback that also takes user emotions into consideration.

[0858] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0859] Step 1:

[0860] Data collection

[0861] The server collects biodiversity data from public and private databases both domestically and internationally. It runs a regularly scheduled task to access each database using an API key and download species lists and habitat data.

[0862] Input: API key, database URL

[0863] Output: Biodiversity data in JSON and CSV format

[0864] Specific operation: The server uses a Python script to retrieve data from the API, sending an API request and saving the retrieved JSON or CSV data in local storage.

[0865] Step 2:

[0866] Standardization and organization of data formats

[0867] The server aligns and organizes the collected data. It uses the Python Pandas library to read the data and convert it from different formats (CSV, JSON, XML, etc.) into a unified data frame format. It removes duplicate data and completes missing information.

[0868] Input: Biodiversity data (JSON, CSV, XML)

[0869] Output: Cleaned data frame

[0870] Specific operation: The server converts the data collected from each data source into a Pandas data frame, standardizes the format, removes duplicate data, and completes missing data.

[0871] Step 3:

[0872] Sensing data input

[0873] Users input data acquired on-site into the system, using a smartphone or tablet to upload photos taken during field surveys and location information to a dedicated app.

[0874] Input: Plant photo, location information

[0875] Output: Sensing data (photo files, location information) uploaded to the server

[0876] Specific operation: The user presses the photo upload button in the app, selects the photo they took, confirms that the location information is automatically acquired, and uploads it. This sends the data to the server.

[0877] Step 4:

[0878] Data analysis

[0879] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots. It uses the SciKit-Learn library to apply a random forest algorithm to identify biodiversity hotspots from the collected data.

[0880] Input: Prepared data frame, sensing data

[0881] Output: List of hotspots

[0882] How it works: The server trains a random forest algorithm with training data, and then inputs newly collected data into the model to identify hotspots.

[0883] Step 5:

[0884] Guideline formulation and report generation

[0885] The server formulates guidelines based on the analysis results and generates a report. Using the report generation tool, the server creates a document of protection guidelines and measures based on the analysis results.

[0886] Input: List of critical areas, past guidelines

[0887] Output: Report in PDF format

[0888] Specific Actions: The server uses LaTeX and PDF generation libraries to create a report containing specific protection measures based on the identified hotspots and outputs it in PDF format.

[0889] Step 6:

[0890] Collecting feedback

[0891] The terminal displays the generated report to the user and collects feedback. The terminal displays the report through a user interface and provides a form for accepting ratings and comments from the user.

[0892] Input: Report in PDF format

[0893] Output: User feedback (ratings, comments)

[0894] Specific operation: The terminal provides a report display screen, receives user ratings and comments through a feedback input form, and sends them to the server.

[0895] Step 7:

[0896] Emotion analysis using an emotion engine

[0897] The device uses an emotion engine to analyze the user's emotions when they provide feedback, and uses the built-in camera and microphone to record and analyze the user's facial expressions and tone of voice.

[0898] Input: User's facial expression data, voice data

[0899] Output: Analysis of the user's emotional state (satisfaction, dissatisfaction, interest)

[0900] Specific operation: The device uses a camera and microphone to record the user's facial expressions and voice in real time, and analyzes their emotional state using an emotion engine. The analysis results are sent to the server as evaluation data.

[0901] Step 8:

[0902] System Updates

[0903] The server updates the system based on feedback and the results of the sentiment engine analysis, continuously collects and analyzes data, and optimizes Japan's unique biodiversity conservation standards. Feedback data and sentiment analysis results are analyzed to identify areas for improvement, and the system's algorithms and data collection methods are updated.

[0904] Input: User feedback, sentiment analysis results

[0905] Output: Updated data acquisition and analysis system

[0906] Specific operation: Based on the feedback and sentiment analysis, the server adds improvements to be reflected in the next data collection and analysis, and updates the system code.

[0907] (Application example 2)

[0908] 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."

[0909] Biodiversity conservation and management requires the efficient collection, integration, and analysis of vast amounts of data to formulate optimal guidelines for each region. However, it is necessary to collect not only on-site feedback but also high-quality feedback that combines sentiment analysis. It is also necessary to collect environmental data from physical stores, evaluate eco-friendliness based on that data, and develop a system for continuous improvement.

[0910] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0911] In this invention, the server includes means for collecting biodiversity data from domestic and international databases, means for standardizing and organizing the collected data, means for inputting sensing data from field surveys, means for analyzing the data using a machine learning algorithm and identifying priority areas, means for formulating guidelines and generating reports based on the analysis results, means for displaying the generated reports to users and collecting feedback, means for analyzing user emotions using a sentiment analysis engine, means for collecting environmental data at physical stores, means for conducting eco-friendliness evaluations based on the collected environmental data and generating guidelines, means for collecting customer feedback after using the store, means for updating the system based on the collected feedback, and means for analyzing customers' facial expressions and voices to understand their emotional states, thereby enabling the formulation of guidelines for biodiversity protection and the evaluation and improvement of eco-friendly initiatives at physical stores.

[0912] A "database" is a collection of data that is organized to efficiently manage and search large amounts of information.

[0913] "Biodiversity" means the variety of different living species and the ecosystems they use.

[0914] "Data collection methods" are the means by which the necessary information is gathered from various sources.

[0915] "Data preparation means" refers to the means of unifying collected data into a consistent format and making it usable.

[0916] "Sensing data" refers to data about the environment and living organisms obtained through field surveys.

[0917] "Data analysis means" refers to a means of analyzing collected data using machine learning algorithms, etc.

[0918] "Guideline formulation means" refers to the means of creating appropriate guidelines for action based on the results of data analysis.

[0919] The "report generation means" is a means for organizing the analysis results and outputting them as a document.

[0920] "Feedback collection means" refers to the means of collecting evaluations and opinions from system users.

[0921] An "emotion analysis engine" is software or a system that analyzes a user's facial expressions and voice to understand their emotional state.

[0922] "Environmental Data" refers to data about the surrounding environment, such as temperature, humidity, CO2 levels, etc.

[0923] "Eco-friendly evaluation" is a process that evaluates whether a product has a low environmental impact.

[0924] "System update methods" are methods for adapting and improving the entire system based on collected feedback and analysis results.

[0925] The system of this invention is composed of many elements and is realized through the interaction of three entities: a server, a terminal, and a user. The purpose of this system is to support the conservation of biodiversity and the evaluation and improvement of eco-friendly efforts in physical stores.

[0926] Hardware and Software Configuration

[0927] 1. Data Collection

[0928] The server will collect biodiversity data from domestic and international databases, including the International Biodiversity Information Facility, and retrieve data via API.

[0929] 2. Data Preparation

[0930] The server standardizes and organizes the collected data. It performs a data conversion process to standardize the data into a specific format and remove duplicate and missing data.

[0931] 3. Input of sensing data

[0932] Users input sensing data obtained during field surveys into the system, including photos of plants collected during the field survey and their location information.

[0933] 4. Data Analysis

[0934] The server then uses machine learning algorithms to analyze the collected data and identify important biodiversity hotspots, using algorithms such as random forests in the process.

[0935] 5. Guideline formulation and report generation

[0936] Based on the analysis, the server develops guidelines and generates a report detailing protective measures for different climate zones and seasons.

[0937] 6. Gathering Feedback

[0938] The terminal displays the generated report to the user and provides an interface for collecting feedback. The user can view the report and enter their rating and comments.

[0939] 7. Emotion analysis

[0940] The device uses an emotion analysis engine to analyze the user's emotions. It uses a camera (e.g., Logitech C920) and a microphone (e.g., Blue Yeti) to analyze the user's facial expressions and tone of voice to understand their emotional state.

[0941] 8. System Updates

[0942] The server updates the system based on the collected feedback and sentiment analysis results, allowing new items to be added to the next data collection and analysis, enabling continuous improvement of the system.

[0943] Examples:

[0944] For example, the server downloads the latest biodiversity species list from the International Biodiversity Information Facility via API and integrates it with data from other databases. Researchers then upload photos of plants taken in the field and their location information. The server analyzes this data using a random forest algorithm to identify biodiversity hotspots in rainy regions. The generated report is displayed on the device for users to view, and feedback is collected. A sentiment analysis engine analyzes users' facial expressions and tone of voice to determine their level of satisfaction or dissatisfaction. Finally, the server uses the collected feedback and the results of the sentiment analysis to reflect this in the next system update.

[0945] Example prompt sentence:

[0946] "Provide a concrete example of an eco-shop management application that generates optimal guidelines for biodiversity conservation. Include a function that uses a random forest algorithm to evaluate the shop's eco-friendliness based on environmental data collected within the shop (e.g., temperature, humidity, CO2 levels), and report areas for improvement. Also explain how customer feedback and sentiment analysis can be used to improve store operations."

[0947] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0948] Step 1:

[0949] The server collects biodiversity data from domestic and international databases. As input, it uses data obtained from the International Biodiversity Information Facility via API. The data downloaded from the database is in its raw state. As output, it obtains the collected biodiversity dataset.

[0950] Step 2:

[0951] The server standardizes and organizes the collected data. As input, it uses the biodiversity data collected in step 1. To standardize the data, it converts different data structures into a common format and removes duplicates and missing data. As output, it obtains a uniformly formatted dataset.

[0952] Step 3:

[0953] Users input sensing data obtained from field surveys into the system. The input includes photos of the surveyed plants, location information, and environmental data (temperature, humidity, etc.). This data is raw data obtained from the field survey. The output is the field survey data uploaded to the system.

[0954] Step 4:

[0955] The server analyzes the data using machine learning algorithms to identify important biodiversity hotspots. As input, it uses the dataset prepared in step 2 and the sensing data uploaded in step 3. It applies algorithms such as random forests and performs analysis. As output, it obtains a list of important areas.

[0956] Step 5:

[0957] The server formulates guidelines and generates reports based on the analysis results. It uses the list of critical areas obtained in step 4 as input. It sets guidelines including protective measures according to climate zones and seasons, and creates and outputs reports based on them.

[0958] Step 6:

[0959] The terminal displays the generated report to the user and collects feedback. As input, it uses the report generated in step 5. The user enters feedback after viewing the report. As output, it obtains the user's feedback data.

[0960] Step 7:

[0961] The device analyzes the user's emotions using an emotion analysis engine. The input is the user's facial expression data and voice data collected by a camera and microphone. Specifically, the emotion analysis software performs facial expression recognition and voice tone analysis. The output is the user's emotion data.

[0962] Step 8:

[0963] The server updates the system based on the collected feedback and sentiment analysis results. As input, it uses the feedback data obtained in step 6 and the sentiment data obtained in step 7. It performs processing to improve the analysis algorithms and data collection methods of the entire system. As output, it obtains updated data collection and analysis models.

[0964] This will enable the development of guidelines for biodiversity conservation and the evaluation and improvement of eco-friendly initiatives in physical stores.

[0965] 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.

[0966] 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.

[0967] 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.

[0968] [Third embodiment]

[0969] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0970] 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.

[0971] 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).

[0972] 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.

[0973] 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.

[0974] 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).

[0975] 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.

[0976] 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.

[0977] 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.

[0978] 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.

[0979] 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.

[0980] 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."

[0981] The present invention relates to a system for efficiently collecting, integrating, and analyzing huge amounts of data in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. Specific embodiments of the system of the present invention are described below.

[0982] System Overview

[0983] The system includes the following elements:

[0984] 1. Data collection method: The server accesses domestic and international databases to collect data on biodiversity.

[0985] 2. Data preparation method: The server standardizes and organizes the collected data.

[0986] 3. Sensing data input method: The user inputs the sensing data acquired during the field survey into the system.

[0987] 4. Data analysis method: The server uses machine learning algorithms to analyze the data and identify key areas.

[0988] 5. Guideline formulation means: The server formulates guidelines based on the analysis results and generates a report.

[0989] 6. Feedback means: The terminal displays the generated report to the user and collects feedback.

[0990] 7. System update method: The server updates the system based on feedback, continuously collecting and analyzing data to improve it.

[0991] Natural language explanation of program processing

[0992] 1. Data Collection

[0993] The server collects biodiversity data from public and private databases both domestically and internationally.

[0994] Example: A server downloads the latest species list from the International Biodiversity Information Facility via an API.

[0995] 2. Standardization and organization of data formats

[0996] The server compiles and organizes the collected data.

[0997] Example: The server converts data collected from different databases into a unified format and eliminates duplicate data.

[0998] 3. Input of sensing data

[0999] The user inputs the data acquired on-site into the system.

[1000] Example: A user uploads photos and location information of plants collected during a field survey.

[1001] 4. Data Analysis

[1002] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots.

[1003] Example: The server uses a random forest algorithm to identify biodiversity hotspots in rainy regions.

[1004] 5. Guideline formulation and report generation

[1005] The server formulates guidelines based on the analysis results and generates a report.

[1006] Example: The server generates reports of protection measures based on climate zone and season.

[1007] 6. Gathering Feedback

[1008] The terminal displays the generated report to the user and collects feedback.

[1009] Example: The device provides an interface for users to view reports and collect their ratings and comments.

[1010] 7. System Updates

[1011] The server updates the system based on feedback, continuously collecting and analyzing data, and optimizing Japan's unique biodiversity conservation standards.

[1012] Example: The server reflects user feedback and adds new items to the next data collection and analysis.

[1013] In this way, the system of the present invention will establish biodiversity conservation standards unique to Japan, enabling optimal conservation measures to be implemented in each region.

[1014] The processing flow will be explained below.

[1015] Step 1:

[1016] The server will access domestic and international databases and collect data on biodiversity.

[1017] Specific behavior:

[1018] 1. The server uses the API to send requests to the International Biodiversity Information Facility and other authoritative data sources.

[1019] 2. The server stores the acquired data in a local database for temporary storage.

[1020] Step 2:

[1021] The server standardizes the format of the collected data and organizes it.

[1022] Specific behavior:

[1023] 1. The server detects duplicate data and removes the duplicate records.

[1024] 2. The server converts data from different formats into a unified format, organizing the data into common fields such as species name, region, and publication year.

[1025] Step 3:

[1026] The user inputs sensing data acquired on-site into the system.

[1027] Specific behavior:

[1028] 1. The user logs into the system using a dedicated terminal or a web interface.

[1029] 2. The user enters the collected data (e.g., plant photos, latitude and longitude information, etc.) into the upload form.

[1030] 3. The sensing data is sent to the server and stored in a local database.

[1031] Step 4:

[1032] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots.

[1033] Specific behavior:

[1034] 1. The server pre-processes the data and creates a dataset for analysis.

[1035] 2. The server applies a machine learning model (e.g., random forest algorithm) to detect biodiversity hotspots in a specific area.

[1036] 3. The analysis results will be saved in a specified folder on the server.

[1037] Step 5:

[1038] The server formulates guidelines based on the analysis results and generates a report.

[1039] Specific behavior:

[1040] 1. The server extracts the necessary information from the analysis results.

[1041] 2. The server creates a report according to a standard format and generates it in PDF format.

[1042] 3. The report is saved to the specified folder on the server.

[1043] Step 6:

[1044] The terminal displays the generated report to the user and collects feedback.

[1045] Specific behavior:

[1046] 1. After user authentication, the terminal provides an interface to display the generated report.

[1047] 2. Users view the report and use the feedback form to provide their ratings and comments.

[1048] 3. The feedback is sent to the server.

[1049] Step 7:

[1050] The server updates the system based on feedback collected from users, and continuously collects, analyzes, and improves data.

[1051] Specific behavior:

[1052] 1. The server analyzes user feedback and identifies areas for improvement.

[1053] 2. The server updates the system configuration to incorporate new data collection and analysis algorithms.

[1054] 3. The server periodically re-runs the data collection and analysis and updates the reports.

[1055] Through these steps, the system will establish biodiversity conservation standards unique to Japan, enabling optimal conservation measures to be implemented in each region.

[1056] Example 1

[1057] 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."

[1058] Biodiversity conservation and management requires the collection, organization, and analysis of large amounts of data. However, this process is time-consuming and labor-intensive, making it difficult to carry out efficiently. Furthermore, to formulate optimal conservation guidelines for each region, it is necessary to effectively integrate and analyze field survey data and existing data. Furthermore, it is also necessary to reflect feedback and continuously improve the system. A system that can solve this problem and implement efficient and accurate biodiversity conservation measures is needed.

[1059] 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.

[1060] In this invention, the server includes means for collecting information on biodiversity from domestic and international sources, means for converting the collected information into a unified format and organizing it, means for inputting sensing information from field surveys, means for analyzing the information using a machine learning algorithm and identifying important ecological regions, means for formulating conservation guidelines and generating documents based on the analysis results, means for displaying the generated documents to users and collecting evaluations, and means for updating the system based on the evaluations and continuously collecting, analyzing, and improving the information. This enables the efficient collection, integration, and analysis of data necessary for biodiversity protection and management, and the formulation and implementation of optimal conservation measures for each region.

[1061] "Biodiversity" refers to the variety of mutations, species, individuals, and ecosystems of living organisms on Earth.

[1062] "Sources" refers to national and international databases and institutions that provide biodiversity data.

[1063] "Unified format" refers to converting data provided in different formats into a consistent data format.

[1064] "Maintenance" refers to the process of removing duplicates and missing data and cleansing the collected data.

[1065] "Sensing information" refers to data obtained during field surveys (e.g., photographs, GPS coordinates, observation notes, etc.).

[1066] "Machine learning algorithms" refer to computer algorithms that analyze large amounts of data to generate patterns and make predictions.

[1067] "Critical ecoregions" refer to specific areas that are rich in biodiversity and require protection.

[1068] "Conservation guidelines" refer to specific guidelines and measures for effectively promoting biodiversity conservation.

[1069] "Document" refers to reports and reports containing analytical findings and conservation guidelines.

[1070] "User" refers to the person or organization that uses the system to input, analyze and evaluate biodiversity data.

[1071] "Evaluation" refers to feedback and suggestions for improvement that users give to the generated document.

[1072] "Update" refers to the process of improving the system based on collected evaluations and making improvements to enable more effective data collection and analysis.

[1073] The present invention relates to a system for efficiently collecting, integrating, and analyzing vast amounts of information in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. Specific embodiments of the present invention are described below.

[1074] Data collection

[1075] The server collects biodiversity information from domestic and international sources, including databases from international biodiversity information agencies and local environmental organizations. The server retrieves data from these sources via APIs. For example, the server automatically downloads the latest species list from the international biodiversity information agency.

[1076] Standardization and organization of data formats

[1077] The server converts the collected information into a unified format and organizes it. This includes converting data provided in different formats (e.g., CSV, JSON, XML) into a unified format. The server also performs data cleansing to remove duplicate data and missing values.

[1078] Sensing data input

[1079] Users input sensing information acquired during field surveys into the system. Using a dedicated mobile app, users input photos of plants taken on-site, GPS coordinates, observation notes, etc. This information is sent to the server in real time and stored in a database. As a specific example, a user may upload a photo of a new species of plant during a field survey and input its location information and observation notes.

[1080] Data analysis

[1081] The server uses machine learning algorithms to analyze the collected and organized information and identify important ecological regions. It uses Python machine learning libraries (e.g., scikit-learn and TensorFlow) to analyze areas with high biodiversity based on large amounts of data. For example, the server uses a random forest algorithm to identify biodiversity hotspots in areas with high rainfall.

[1082] Guideline formulation and report generation

[1083] The server formulates protection guidelines based on the analysis results and generates a document. A generative AI model (e.g., OpenAI's GPT-4) is used to automatically generate guidelines outlining specific protection measures. These guidelines are output as a PDF report and can be downloaded by users through a web portal. A specific example would be a scenario in which the server generates a report stating that "in areas with an annual rainfall of 1000 mm or more, protection of certain vegetation types is necessary."

[1084] Collecting feedback

[1085] The terminal displays the generated document to the user and collects their evaluation. The user is provided with an interface that allows them to check the report on the terminal and enter their evaluation of the generated guidelines and suggestions for improvement. For example, the user may submit comments on whether a specific guideline is feasible or what improvements can be made.

[1086] System Updates

[1087] The server updates the system based on the evaluations and continuously collects, analyzes, and improves the information. It analyzes user feedback to improve the machine learning model and data collection process. For example, the next time data is collected, new data items (such as soil pH data) are added and incorporated into the analysis algorithm.

[1088] Examples of prompt statements

[1089] As an example of a specific prompt, the following might be used when a user uploads data collected during a field survey:

[1090] "Please upload photos of plants taken during your field surveys and their locations into the system, along with notes on the habitat and weather conditions in which you observed them."

[1091] And when the server identifies new biodiversity hotspots and generates guidelines, the prompt is:

[1092] "Calculate and identify biodiversity hotspots in high-rainfall areas using up-to-date rainfall and vegetation data. Then, based on the identified hotspots, develop guidelines and generate reports for conservation measures appropriate for the area."

[1093] In this way, the present invention is a system that enables the efficient collection, integration, and analysis of data necessary for the protection and management of biodiversity, and supports the formulation and implementation of optimal conservation measures for each region.

[1094] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1095] Step 1: Data collection

[1096] The server collects information on biodiversity from domestic and international sources.

[1097] Input: API endpoints from databases such as international biodiversity information agencies and local environmental protection agencies.

[1098] Output: Raw datasets (e.g., species lists, meteorological data, vegetation data)

[1099] Specific operation: The server periodically sends API requests to retrieve data and saves it in local storage. For example, a CRON job can be used to automatically retrieve the latest data at 3:00 AM every day.

[1100] Step 2: Standardize and organize data formats

[1101] The server converts the collected information into a unified format and organizes it.

[1102] Input: Raw datasets acquired in different formats (CSV, JSON, XML).

[1103] Output: A uniformly formatted dataset

[1104] What happens: The server uses ETL (Extract, Transform, Load) tools to transform the data into a consistent format. Data cleansing scripts correct and remove duplicate entries and missing values. Data frame manipulations are performed, for example, using the Python Pandas library.

[1105] Step 3: Input of sensing data

[1106] The user inputs sensing information acquired during field surveys into the system.

[1107] Input: Data obtained during field surveys (plant photos, GPS coordinates, observation notes, etc.).

[1108] Output: Sensing information stored on the server

[1109] Specific operation: A user uses a mobile app to input photos and text data, which the app then uploads to a server. For example, a user might upload a photo of a new species discovered in the Southern Alps region and enter its GPS coordinates.

[1110] Step 4: Data analysis

[1111] The server uses machine learning algorithms to analyze the collected and organized information and identify important ecological regions.

[1112] Input: Organized dataset (integrated data of collected data and sensing data)

[1113] Output: List of important ecological regions and their analysis results

[1114] How it works: The server uses Python machine learning libraries (scikit-learn, TensorFlow) to apply algorithms (e.g., random forests) to analyze the data, for example, to identify biodiversity hotspots based on rainfall and local vegetation data.

[1115] Step 5: Guideline development and report generation

[1116] The server formulates protection guidelines based on the analysis results and generates a document.

[1117] Input: List of important ecological regions and their analysis results

[1118] Output: PDF report with protection guidelines

[1119] How it works: The server uses a generative AI model (such as OpenAI's GPT-4) to automatically generate protection guidelines based on the analysis results, and creates a report using a PDF generation library, which users can download from a web portal.

[1120] Step 6: Gather feedback

[1121] The terminal displays the generated document to the user and collects ratings.

[1122] Input: User evaluation of the report and suggestions for improvement

[1123] Output: Feedback information stored in the rating database

[1124] Specific operation: The user views the report on their device and enters comments and suggestions using the feedback form. This information is sent to the server in real time and stored in the database. For example, the user can enter feedback such as "This part is specific and easy to understand" or "This part is unclear and needs improvement."

[1125] Step 7: Update your system

[1126] The server updates the system based on the evaluation and continuously collects and analyzes information to improve it.

[1127] Input: Feedback information stored in the ratings database

[1128] Output: Improved data collection and analysis processes and updated systems

[1129] Specific operation: The server analyzes the feedback and adds new data items or improves the algorithm. For example, the next time data is collected, new soil pH data is collected and incorporated into the analysis algorithm. This improves the accuracy and efficiency of the entire system.

[1130] (Application example 1)

[1131] 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."

[1132] Conventional biodiversity conservation management systems are inefficient because they require a lot of manpower and time for data collection and analysis, and they lack management measures that are particularly adapted to the environment inside and outside factories. As a result, it has been difficult to formulate appropriate guidelines for minimizing the impact on ecosystems. To solve this problem, a system that allows for efficient and accurate data collection and analysis, and that takes into account practicality in the field, is needed.

[1133] 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.

[1134] In this invention, the server includes means for collecting data on biodiversity from domestic and international databases, means for standardizing and organizing the collected data, means for inputting sensing data from field surveys, means for analyzing the data using a machine learning algorithm and identifying important areas, means for formulating guidelines and generating reports based on the analysis results, means for displaying the generated reports to users and collecting feedback, and a robot that collects data from inside and outside a factory and proposes optimal management measures for ecosystem protection, including means for transmitting the data collected by the robot to the server and for the server to analyze the data, and means for generating the analysis results as guidelines and displaying them via the robot. This enables efficient and accurate data collection and analysis, and enables the formulation of appropriate guidelines that take practicality in the field into consideration.

[1135] A "database" is a collection of specific information that is systematically organized and stored so that it can be easily searched and retrieved.

[1136] "Biodiversity" is a term that refers to the variety of living species on Earth, their genetic variations, and the ecosystems they comprise.

[1137] "Data collection methods" are methods for obtaining information on biodiversity from domestic and international databases.

[1138] "Data preparation means" refers to the means of standardizing and organizing the collected data.

[1139] "Sensing data" refers to data on biodiversity obtained through field surveys.

[1140] A "machine learning algorithm" is a program that analyzes data and performs pattern recognition and prediction.

[1141] "Data analysis means" refers to a means of analyzing data using machine learning algorithms and identifying important areas.

[1142] A "guideline" is a document that provides guidelines or standards established to achieve a specific purpose.

[1143] The "report generation means" is a means for creating guidelines based on the analysis results and outputting them in document format.

[1144] The "feedback means" is a means for displaying the generated report to the user and collecting evaluations and comments from the user.

[1145] "Inside and outside the factory" is a term that refers to the inside of the factory and the surrounding area.

[1146] "Robots" are autonomous or semi-autonomous machines that collect biodiversity data from inside and outside factories and suggest management measures.

[1147] A "server" is a computer system that processes, stores, and provides data over a network.

[1148] The "analysis means" is a means for transmitting the collected data to a server and analyzing it using a machine learning algorithm.

[1149] The "display means" is a means for generating the analysis results as guidelines and displaying the information via the robot.

[1150] This invention relates to a system for efficiently collecting, integrating, and analyzing huge amounts of data in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. This system utilizes a robot that collects data from inside and outside factories and proposes optimal management measures for ecosystem protection.

[1151] 1. Data Collection

[1152] The server collects biodiversity information from domestic and international databases, such as the Global Biodiversity Information Facility (GBIF) and retrieves the latest species list via an API. The server also receives data collected by the robots inside and outside the factory and stores it in a database.

[1153] 2. Data Preparation

[1154] The server standardizes and organizes the collected data. This includes converting data collected from different databases into a unified format, eliminating duplicate data, normalizing data, etc. Organized data is important for improving the accuracy of subsequent data analysis.

[1155] 3. Input of sensing data

[1156] The user (or robot) inputs sensing data acquired on-site into the system. For example, they upload photos of plants collected during field surveys along with their location information. This data is then used for analysis on the server.

[1157] 4. Data Analysis

[1158] The server will analyze the data using machine learning algorithms to identify important biodiversity hotspots. For example, it will use a random forest algorithm to identify biodiversity hotspots in areas with high rainfall. The results of this analysis will form the basis for developing guidelines.

[1159] 5. Establishment of guidelines

[1160] The server then formulates guidelines based on the analysis results and generates reports, such as protection measures based on climate zones and seasons, and provides them to users. These guidelines include specific procedures and monitoring methods for ecosystem protection.

[1161] 6. Gathering Feedback

[1162] The terminal displays the generated report to the user and provides an interface for collecting feedback. For example, after viewing the report, the user can enter their rating and comments. This feedback is used to improve the system.

[1163] 7. System Updates

[1164] The server will update the system based on feedback and continuously improve data collection and analysis, thereby establishing Japan's own biodiversity conservation standards and implementing optimal conservation measures for each region.

[1165] Hardware and software used

[1166] Hardware:

[1167] Robots: Devices that collect data from inside and outside the factory.

[1168] Server: A computer system that processes and stores data.

[1169] software:

[1170] requests: A Python package for retrieving data from data collection APIs.

[1171] scikit-learn: A Python library for running machine learning algorithms (e.g., the Random Forest algorithm).

[1172] Use of such a system will enable efficient and accurate data collection and analysis, making it possible to formulate appropriate guidelines that take into account practicality in the field.

[1173] Examples of concrete examples and prompts

[1174] Examples:

[1175] For example, in high humidity areas of a factory, guidelines may be created such as "maintain specific plant habitats and conduct monthly monitoring."

[1176] Example prompt sentence:

[1177] "Implement a detailed ecosystem analysis using data obtained from API and a guideline development program based on the results."

[1178] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1179] Step 1:

[1180] The server collects biodiversity information from domestic and international databases. Specifically, the server uses APIs to send requests to each database and temporarily stores the acquired data. The input is the API endpoint and authentication information, and the output is the acquired biodiversity data.

[1181] Step 2:

[1182] The server standardizes and organizes the collected data. Because the acquired data is in different formats, the server converts it into a standard format and eliminates duplicate data. This process is called data cleaning and is important for improving the accuracy of data analysis. The input is raw biodiversity data, and the output is organized data.

[1183] Step 3:

[1184] Users input sensing data acquired during field surveys into the system. For example, they upload photos of plants taken during field surveys and GPS location information. This data is sent to the server and integrated with other data. The input is sensing data from field surveys, and the output is the integrated data uploaded to the server.

[1185] Step 4:

[1186] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots. For example, a random forest algorithm can be used to analyze the data and identify high biodiversity areas. The server inputs the data into the model and obtains a list of important areas as output. The input is the consolidated data, and the output is the analysis results.

[1187] Step 5:

[1188] The server formulates guidelines based on the analysis results and generates a report. Based on the analysis results, it creates a document detailing ecosystem protection measures for each region and formats it as a report. The input is the results of the data analysis, and the output is the generated report.

[1189] Step 6:

[1190] The terminal provides an interface for displaying the generated report to the user and collecting feedback. The user views the report and enters their evaluation and comments on its contents as feedback. The input is the generated report and the user's evaluation and comments, and the output is the collected feedback.

[1191] Step 7:

[1192] The server updates the system based on the feedback and continuously improves data collection and analysis. It analyzes the feedback information, makes necessary corrections and adds features, and improves the accuracy and usability of the entire system. The input is user feedback, and the output is an updated system.

[1193] 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.

[1194] The present invention relates to a system for efficiently collecting, integrating, and analyzing vast amounts of data in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. Furthermore, by combining it with an emotion engine that recognizes user emotions, the quality of user feedback is improved, contributing to the improvement of the entire system. A specific embodiment of the system of the present invention is described below.

[1195] System Overview

[1196] The system includes the following elements:

[1197] 1. Data collection method: The server accesses domestic and international databases to collect data on biodiversity.

[1198] 2. Data preparation method: The server standardizes and organizes the collected data.

[1199] 3. Sensing data input method: The user inputs the sensing data acquired during the field survey into the system.

[1200] 4. Data analysis method: The server uses machine learning algorithms to analyze the data and identify key areas.

[1201] 5. Guideline formulation means: The server formulates guidelines based on the analysis results and generates a report.

[1202] 6. Feedback means: The terminal displays the generated report to the user and collects feedback.

[1203] 7. Emotion engine: The emotion engine built into the device analyzes the user's emotions and understands their emotional state at the time of feedback.

[1204] 8. System update method: The server updates the system based on feedback and analysis results from the emotion engine, and continuously collects, analyzes, and improves data.

[1205] Natural language explanation of program processing

[1206] 1. Data Collection

[1207] The server collects biodiversity data from public and private databases both domestically and internationally.

[1208] Example: A server downloads the latest species list from the International Biodiversity Information Facility via an API.

[1209] 2. Standardization and organization of data formats

[1210] The server compiles and organizes the collected data.

[1211] Example: The server converts data collected from different databases into a unified format and eliminates duplicate data.

[1212] 3. Input of sensing data

[1213] The user inputs the data acquired on-site into the system.

[1214] Example: A user uploads photos and location information of plants collected during a field survey.

[1215] 4. Data Analysis

[1216] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots.

[1217] Example: The server uses a random forest algorithm to identify biodiversity hotspots in rainy regions.

[1218] 5. Guideline formulation and report generation

[1219] The server formulates guidelines based on the analysis results and generates a report.

[1220] Example: The server generates reports of protection measures based on climate zone and season.

[1221] 6. Gathering Feedback

[1222] The terminal displays the generated report to the user and collects feedback.

[1223] Example: The device provides an interface for users to view reports and collect their ratings and comments.

[1224] 7. Emotion Analysis Using an Emotion Engine

[1225] The terminal analyzes the user's emotions using an emotion engine when the user inputs feedback.

[1226] Example: The device uses a camera and microphone to analyze the user's facial expressions and tone of voice to understand the user's emotional state (e.g., satisfaction, dissatisfaction, interest).

[1227] 8. System Updates

[1228] The server updates the system based on feedback and the results of the emotion engine analysis, continuously collecting and analyzing data to optimize Japan's unique biodiversity conservation standards.

[1229] Example: The server reflects user feedback and sentiment analysis and adds new items to the next data collection and analysis.

[1230] In this way, the system of the present invention will establish biodiversity conservation standards unique to Japan and enable the implementation of optimal conservation measures for each region based on high-quality feedback that also takes user emotions into consideration.

[1231] The processing flow will be explained below.

[1232] Step 1:

[1233] The server will access domestic and international databases and collect data on biodiversity.

[1234] Specific behavior:

[1235] 1. The server uses the API to send requests to the International Biodiversity Information Facility and other authoritative data sources.

[1236] 2. The server stores the acquired data in a local database for temporary storage.

[1237] Step 2:

[1238] The server standardizes the format of the collected data and organizes it.

[1239] Specific behavior:

[1240] 1. The server detects duplicate data and removes the duplicate records.

[1241] 2. The server converts data from different formats into a unified format, organizing the data into common fields such as species name, region, and publication year.

[1242] Step 3:

[1243] The user inputs sensing data acquired on-site into the system.

[1244] Specific behavior:

[1245] 1. The user logs into the system using a dedicated terminal or a web interface.

[1246] 2. The user enters the collected data (e.g., plant photos, latitude and longitude information, etc.) into the upload form.

[1247] 3. The sensing data is sent to the server and stored in a local database.

[1248] Step 4:

[1249] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots.

[1250] Specific behavior:

[1251] 1. The server pre-processes the data and creates a dataset for analysis.

[1252] 2. The server applies a machine learning model (e.g., random forest algorithm) to detect biodiversity hotspots in a specific area.

[1253] 3. The analysis results will be saved in a specified folder on the server.

[1254] Step 5:

[1255] The server formulates guidelines based on the analysis results and generates a report.

[1256] Specific behavior:

[1257] 1. The server extracts the necessary information from the analysis results.

[1258] 2. The server creates a report according to a standard format and generates it in PDF format.

[1259] 3. The report is saved to the specified folder on the server.

[1260] Step 6:

[1261] The terminal displays the generated report to the user and collects feedback.

[1262] Specific behavior:

[1263] 1. After user authentication, the terminal provides an interface to display the generated report.

[1264] 2. Users view the report and use the feedback form to provide their ratings and comments.

[1265] 3. The feedback is sent to the server.

[1266] Step 7:

[1267] The terminal analyzes the user's emotions using an emotion engine when the user inputs feedback.

[1268] Specific behavior:

[1269] 1. The device uses a camera and microphone to record the user's facial expressions and tone of voice.

[1270] 2. The device uses an emotion engine to analyze this data and determine the user's emotional state.

[1271] 3. The sentiment analysis results are sent to the server.

[1272] Step 8:

[1273] The server updates the system based on feedback collected from users and the analysis results of the emotion engine, and continuously collects, analyzes, and improves the data.

[1274] Specific behavior:

[1275] 1. The server analyzes user feedback and sentiment analysis results to identify areas for improvement.

[1276] 2. The server updates the system configuration to incorporate new data collection and analysis algorithms.

[1277] 3. The server periodically re-runs the data collection and analysis and updates the reports.

[1278] Through these steps, the system will establish Japan's own biodiversity conservation standards and enable optimal conservation measures to be implemented for each region. Furthermore, by taking user sentiment into account, the system will be able to reflect more effective feedback and improve the accuracy of the system.

[1279] Example 2

[1280] 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."

[1281] The problem that this invention aims to solve is to achieve sustainable conservation of biodiversity by efficiently collecting, integrating, and analyzing huge amounts of data and formulating optimal guidelines for each region based on the results. The invention also aims to provide a system that incorporates a function to recognize user emotions in order to improve the quality of user feedback and utilize it to improve the system.

[1282] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1283] In this invention, the server includes means for collecting biodiversity data from domestic and international databases, means for standardizing the format of the collected data and organizing it, means for inputting sensing data from field surveys, means for analyzing the data using a machine learning algorithm and identifying important areas, means for formulating guidelines and generating reports based on the analysis results, means for displaying the generated reports to users and collecting feedback, an emotion engine for analyzing user emotions, and means for updating the system based on the feedback and the analysis results of the emotion engine. This enables efficient data collection and analysis, the formulation of optimal guidelines for each area, improved quality of user feedback, and continuous improvement of the system.

[1284] "Data collection tools" are tools for collecting data on biodiversity from domestic and international databases.

[1285] "Data preparation means" refers to the means for standardizing and preparing the format of collected data.

[1286] The "sensing data input means" is a means for a user to input sensing data acquired during a field survey into the system.

[1287] "Data analysis means" refers to means for analyzing collected data using machine learning algorithms and identifying key areas.

[1288] The "guideline formulation means" is a means for formulating guidelines based on the analysis results and generating reports.

[1289] The "feedback collection means" is a means for displaying the generated report to the user and collecting feedback.

[1290] The "emotion engine" is an engine that has a function for analyzing the user's emotions.

[1291] The "system update means" is a means for updating the system based on the feedback and the analysis results of the emotion engine.

[1292] The present invention relates to a system for efficiently collecting, integrating, and analyzing vast amounts of data in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. Furthermore, by combining it with an emotion engine that recognizes user emotions, the quality of user feedback is improved, contributing to the improvement of the entire system. A specific embodiment of the system of the present invention is described below.

[1293] Data collection methods

[1294] The server collects biodiversity data from public and private databases both in Japan and overseas. The server runs a regularly scheduled task, accessing each database (e.g., databases of domestic and international biodiversity information facilities and the Ministry of the Environment) using an API key to download species lists and habitat data.

[1295] Example: A server downloads the latest species list from the International Biodiversity Information Facility via an API.

[1296] Prompt text: Use the following API key to get the latest species list from the International Biodiversity Information Facility.

[1297] Data preparation methods

[1298] The server aligns and organizes the collected data. It uses the Python Pandas library to read the data and convert it from different formats (CSV, JSON, XML, etc.) into a unified data frame format. It also removes duplicate data and completes missing information.

[1299] Example: The server converts data collected from different databases into a unified format and eliminates duplicate data.

[1300] Prompt: Transform biodiversity data from different data sources into a unified format and eliminate duplicate data.

[1301] Sensing data input means

[1302] Users input data acquired on-site into the system. Using a smartphone or tablet, users upload photos taken during field surveys and location information to a dedicated app. The app includes a photo upload button and an automatic location information acquisition function.

[1303] Example: A user uploads photos of plants taken during a field survey along with their location.

[1304] Prompt: Upload photos of plants taken during field research and their locations to the app.

[1305] Data Analysis Methods

[1306] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots. The server uses the SciKit-Learn library to apply a random forest algorithm to identify biodiversity hotspots from the collected data. The algorithm is pre-trained with training data.

[1307] Example: The server uses a random forest algorithm to identify biodiversity hotspots in rainy regions.

[1308] Prompt: Using the collected plant species and location information, run a random forest algorithm to identify biodiversity hotspots.

[1309] Guideline formulation method

[1310] The server formulates guidelines based on the analysis results and generates a report.The server uses a report generation tool (e.g., LaTeX or a PDF generation library) to create a document containing protection guidelines and measures based on the analysis results.

[1311] Example: The server generates reports of protection measures based on climate zone and season.

[1312] Prompt: Generate a report containing conservation measures for biodiversity hotspots.

[1313] Feedback collection methods

[1314] The terminal displays the generated report to the user and collects feedback. The terminal displays the report through a user interface and provides a form for accepting ratings and comments from the user. The feedback is sent to the server and automatically saved.

[1315] Example: The device provides an interface for users to view reports and collect ratings and comments.

[1316] Prompt: Displays an interface that allows the user to view the report and enter ratings and comments.

[1317] Emotion Engine

[1318] The device uses an emotion engine to analyze the user's emotions when the user enters feedback. The device uses a built-in camera and microphone to record the user's facial expressions and tone of voice, and analyzes them using the emotion engine. The analysis results are used to evaluate the user's satisfaction and interest.

[1319] Example: The device uses a camera and microphone to analyze the user's facial expressions and tone of voice to understand the user's emotional state.

[1320] Prompt text: Uses the camera and microphone to analyze the user's facial expressions and tone of voice to understand the user's emotional state.

[1321] System Update Method

[1322] The server updates the system based on feedback and the results of the sentiment engine analysis, continuously collecting and analyzing data, and optimizing Japan's unique biodiversity conservation standards. The server analyzes the feedback database and sentiment analysis results to identify areas for improvement and update the system's algorithms and data collection methods, thereby adding new items to the next data collection and analysis.

[1323] Example: The server reflects user feedback and sentiment analysis and adds new items to the next data collection and analysis.

[1324] Prompt: We will implement system updates to improve our data collection and analysis processes based on user feedback and sentiment analysis.

[1325] In this way, the system of the present invention will establish biodiversity conservation standards unique to Japan and enable the implementation of optimal conservation measures for each region based on high-quality feedback that also takes user emotions into consideration.

[1326] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1327] Step 1:

[1328] Data collection

[1329] The server collects biodiversity data from public and private databases both domestically and internationally. It runs a regularly scheduled task to access each database using an API key and download species lists and habitat data.

[1330] Input: API key, database URL

[1331] Output: Biodiversity data in JSON and CSV format

[1332] Specific operation: The server uses a Python script to retrieve data from the API, sending an API request and saving the retrieved JSON or CSV data in local storage.

[1333] Step 2:

[1334] Standardization and organization of data formats

[1335] The server aligns and organizes the collected data. It uses the Python Pandas library to read the data and convert it from different formats (CSV, JSON, XML, etc.) into a unified data frame format. It removes duplicate data and completes missing information.

[1336] Input: Biodiversity data (JSON, CSV, XML)

[1337] Output: Cleaned data frame

[1338] Specific operation: The server converts the data collected from each data source into a Pandas data frame, standardizes the format, removes duplicate data, and completes missing data.

[1339] Step 3:

[1340] Sensing data input

[1341] Users input data acquired on-site into the system, using a smartphone or tablet to upload photos taken during field surveys and location information to a dedicated app.

[1342] Input: Plant photo, location information

[1343] Output: Sensing data (photo files, location information) uploaded to the server

[1344] Specific operation: The user presses the photo upload button in the app, selects the photo they took, confirms that the location information is automatically acquired, and uploads it. This sends the data to the server.

[1345] Step 4:

[1346] Data analysis

[1347] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots. It uses the SciKit-Learn library to apply a random forest algorithm to identify biodiversity hotspots from the collected data.

[1348] Input: Prepared data frame, sensing data

[1349] Output: List of hotspots

[1350] How it works: The server trains a random forest algorithm with training data, and then inputs newly collected data into the model to identify hotspots.

[1351] Step 5:

[1352] Guideline formulation and report generation

[1353] The server formulates guidelines based on the analysis results and generates a report. Using the report generation tool, the server creates a document of protection guidelines and measures based on the analysis results.

[1354] Input: List of critical areas, past guidelines

[1355] Output: Report in PDF format

[1356] Specific Actions: The server uses LaTeX and PDF generation libraries to create a report containing specific protection measures based on the identified hotspots and outputs it in PDF format.

[1357] Step 6:

[1358] Collecting feedback

[1359] The terminal displays the generated report to the user and collects feedback. The terminal displays the report through a user interface and provides a form for accepting ratings and comments from the user.

[1360] Input: Report in PDF format

[1361] Output: User feedback (ratings, comments)

[1362] Specific operation: The terminal provides a report display screen, receives user ratings and comments through a feedback input form, and sends them to the server.

[1363] Step 7:

[1364] Emotion analysis using an emotion engine

[1365] The device uses an emotion engine to analyze the user's emotions when they provide feedback, and uses the built-in camera and microphone to record and analyze the user's facial expressions and tone of voice.

[1366] Input: User's facial expression data, voice data

[1367] Output: Analysis of the user's emotional state (satisfaction, dissatisfaction, interest)

[1368] Specific operation: The device uses a camera and microphone to record the user's facial expressions and voice in real time, and analyzes their emotional state using an emotion engine. The analysis results are sent to the server as evaluation data.

[1369] Step 8:

[1370] System Updates

[1371] The server updates the system based on feedback and the results of the sentiment engine analysis, continuously collects and analyzes data, and optimizes Japan's unique biodiversity conservation standards. Feedback data and sentiment analysis results are analyzed to identify areas for improvement, and the system's algorithms and data collection methods are updated.

[1372] Input: User feedback, sentiment analysis results

[1373] Output: Updated data acquisition and analysis system

[1374] Specific operation: Based on the feedback and sentiment analysis, the server adds improvements to be reflected in the next data collection and analysis, and updates the system code.

[1375] (Application example 2)

[1376] 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."

[1377] Biodiversity conservation and management requires the efficient collection, integration, and analysis of vast amounts of data to formulate optimal guidelines for each region. However, it is necessary to collect not only on-site feedback but also high-quality feedback that combines sentiment analysis. It is also necessary to collect environmental data from physical stores, evaluate eco-friendliness based on that data, and develop a system for continuous improvement.

[1378] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1379] In this invention, the server includes means for collecting biodiversity data from domestic and international databases, means for standardizing and organizing the collected data, means for inputting sensing data from field surveys, means for analyzing the data using a machine learning algorithm and identifying priority areas, means for formulating guidelines and generating reports based on the analysis results, means for displaying the generated reports to users and collecting feedback, means for analyzing user emotions using a sentiment analysis engine, means for collecting environmental data at physical stores, means for conducting eco-friendliness evaluations based on the collected environmental data and generating guidelines, means for collecting customer feedback after using the store, means for updating the system based on the collected feedback, and means for analyzing customers' facial expressions and voices to understand their emotional states, thereby enabling the formulation of guidelines for biodiversity protection and the evaluation and improvement of eco-friendly initiatives at physical stores.

[1380] A "database" is a collection of data that is organized to efficiently manage and search large amounts of information.

[1381] "Biodiversity" means the variety of different living species and the ecosystems they use.

[1382] "Data collection methods" are the means by which the necessary information is gathered from various sources.

[1383] "Data preparation means" refers to the means of unifying collected data into a consistent format and making it usable.

[1384] "Sensing data" refers to data about the environment and living organisms obtained through field surveys.

[1385] "Data analysis means" refers to a means of analyzing collected data using machine learning algorithms, etc.

[1386] "Guideline formulation means" refers to the means of creating appropriate guidelines for action based on the results of data analysis.

[1387] The "report generation means" is a means for organizing the analysis results and outputting them as a document.

[1388] "Feedback collection means" refers to the means of collecting evaluations and opinions from system users.

[1389] An "emotion analysis engine" is software or a system that analyzes a user's facial expressions and voice to understand their emotional state.

[1390] "Environmental Data" refers to data about the surrounding environment, such as temperature, humidity, CO2 levels, etc.

[1391] "Eco-friendly evaluation" is a process that evaluates whether a product has a low environmental impact.

[1392] "System update methods" are methods for adapting and improving the entire system based on collected feedback and analysis results.

[1393] The system of this invention is composed of many elements and is realized through the interaction of three entities: a server, a terminal, and a user. The purpose of this system is to support the conservation of biodiversity and the evaluation and improvement of eco-friendly efforts in physical stores.

[1394] Hardware and Software Configuration

[1395] 1. Data Collection

[1396] The server will collect biodiversity data from domestic and international databases, including the International Biodiversity Information Facility, and retrieve data via API.

[1397] 2. Data Preparation

[1398] The server standardizes and organizes the collected data. It performs a data conversion process to standardize the data into a specific format and remove duplicate and missing data.

[1399] 3. Input of sensing data

[1400] Users input sensing data obtained during field surveys into the system, including photos of plants collected during the field survey and their location information.

[1401] 4. Data Analysis

[1402] The server then uses machine learning algorithms to analyze the collected data and identify important biodiversity hotspots, using algorithms such as random forests in the process.

[1403] 5. Guideline formulation and report generation

[1404] Based on the analysis, the server develops guidelines and generates a report detailing protective measures for different climate zones and seasons.

[1405] 6. Gathering Feedback

[1406] The terminal displays the generated report to the user and provides an interface for collecting feedback. The user can view the report and enter their rating and comments.

[1407] 7. Emotion analysis

[1408] The device uses an emotion analysis engine to analyze the user's emotions. It uses a camera (e.g., Logitech C920) and a microphone (e.g., Blue Yeti) to analyze the user's facial expressions and tone of voice to understand their emotional state.

[1409] 8. System Updates

[1410] The server updates the system based on the collected feedback and sentiment analysis results, allowing new items to be added to the next data collection and analysis, enabling continuous improvement of the system.

[1411] Examples:

[1412] For example, the server downloads the latest biodiversity species list from the International Biodiversity Information Facility via API and integrates it with data from other databases. Researchers then upload photos of plants taken in the field and their location information. The server analyzes this data using a random forest algorithm to identify biodiversity hotspots in rainy regions. The generated report is displayed on the device for users to view, and feedback is collected. A sentiment analysis engine analyzes users' facial expressions and tone of voice to determine their level of satisfaction or dissatisfaction. Finally, the server uses the collected feedback and the results of the sentiment analysis to reflect this in the next system update.

[1413] Example prompt sentence:

[1414] "Provide a concrete example of an eco-shop management application that generates optimal guidelines for biodiversity conservation. Include a function that uses a random forest algorithm to evaluate the shop's eco-friendliness based on environmental data collected within the shop (e.g., temperature, humidity, CO2 levels), and report areas for improvement. Also explain how customer feedback and sentiment analysis can be used to improve store operations."

[1415] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1416] Step 1:

[1417] The server collects biodiversity data from domestic and international databases. As input, it uses data obtained from the International Biodiversity Information Facility via API. The data downloaded from the database is in its raw state. As output, it obtains the collected biodiversity dataset.

[1418] Step 2:

[1419] The server standardizes and organizes the collected data. As input, it uses the biodiversity data collected in step 1. To standardize the data, it converts different data structures into a common format and removes duplicates and missing data. As output, it obtains a uniformly formatted dataset.

[1420] Step 3:

[1421] Users input sensing data obtained from field surveys into the system. The input includes photos of the surveyed plants, location information, and environmental data (temperature, humidity, etc.). This data is raw data obtained from the field survey. The output is the field survey data uploaded to the system.

[1422] Step 4:

[1423] The server analyzes the data using machine learning algorithms to identify important biodiversity hotspots. As input, it uses the dataset prepared in step 2 and the sensing data uploaded in step 3. It applies algorithms such as random forests and performs analysis. As output, it obtains a list of important areas.

[1424] Step 5:

[1425] The server formulates guidelines and generates reports based on the analysis results. It uses the list of critical areas obtained in step 4 as input. It sets guidelines including protective measures according to climate zones and seasons, and creates and outputs reports based on them.

[1426] Step 6:

[1427] The terminal displays the generated report to the user and collects feedback. As input, it uses the report generated in step 5. The user enters feedback after viewing the report. As output, it obtains the user's feedback data.

[1428] Step 7:

[1429] The device analyzes the user's emotions using an emotion analysis engine. The input is the user's facial expression data and voice data collected by a camera and microphone. Specifically, the emotion analysis software performs facial expression recognition and voice tone analysis. The output is the user's emotion data.

[1430] Step 8:

[1431] The server updates the system based on the collected feedback and sentiment analysis results. As input, it uses the feedback data obtained in step 6 and the sentiment data obtained in step 7. It performs processing to improve the analysis algorithms and data collection methods of the entire system. As output, it obtains updated data collection and analysis models.

[1432] This will enable the development of guidelines for biodiversity conservation and the evaluation and improvement of eco-friendly initiatives in physical stores.

[1433] 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.

[1434] 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.

[1435] 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.

[1436] [Fourth embodiment]

[1437] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1438] 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.

[1439] 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).

[1440] 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.

[1441] 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.

[1442] 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).

[1443] 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.

[1444] 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.

[1445] 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.

[1446] 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.

[1447] 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.

[1448] 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.

[1449] 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."

[1450] The present invention relates to a system for efficiently collecting, integrating, and analyzing huge amounts of data in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. Specific embodiments of the system of the present invention are described below.

[1451] System Overview

[1452] The system includes the following elements:

[1453] 1. Data collection method: The server accesses domestic and international databases to collect data on biodiversity.

[1454] 2. Data preparation method: The server standardizes and organizes the collected data.

[1455] 3. Sensing data input method: The user inputs the sensing data acquired during the field survey into the system.

[1456] 4. Data analysis method: The server uses machine learning algorithms to analyze the data and identify key areas.

[1457] 5. Guideline formulation means: The server formulates guidelines based on the analysis results and generates a report.

[1458] 6. Feedback means: The terminal displays the generated report to the user and collects feedback.

[1459] 7. System update method: The server updates the system based on feedback, continuously collecting and analyzing data to improve it.

[1460] Natural language explanation of program processing

[1461] 1. Data Collection

[1462] The server collects biodiversity data from public and private databases both domestically and internationally.

[1463] Example: A server downloads the latest species list from the International Biodiversity Information Facility via an API.

[1464] 2. Standardization and organization of data formats

[1465] The server compiles and organizes the collected data.

[1466] Example: The server converts data collected from different databases into a unified format and eliminates duplicate data.

[1467] 3. Input of sensing data

[1468] The user inputs the data acquired on-site into the system.

[1469] Example: A user uploads photos and location information of plants collected during a field survey.

[1470] 4. Data Analysis

[1471] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots.

[1472] Example: The server uses a random forest algorithm to identify biodiversity hotspots in rainy regions.

[1473] 5. Guideline formulation and report generation

[1474] The server formulates guidelines based on the analysis results and generates a report.

[1475] Example: The server generates reports of protection measures based on climate zone and season.

[1476] 6. Gathering Feedback

[1477] The terminal displays the generated report to the user and collects feedback.

[1478] Example: The device provides an interface for users to view reports and collect their ratings and comments.

[1479] 7. System Updates

[1480] The server updates the system based on feedback, continuously collecting and analyzing data, and optimizing Japan's unique biodiversity conservation standards.

[1481] Example: The server reflects user feedback and adds new items to the next data collection and analysis.

[1482] In this way, the system of the present invention will establish biodiversity conservation standards unique to Japan, enabling optimal conservation measures to be implemented in each region.

[1483] The processing flow will be explained below.

[1484] Step 1:

[1485] The server will access domestic and international databases and collect data on biodiversity.

[1486] Specific behavior:

[1487] 1. The server uses the API to send requests to the International Biodiversity Information Facility and other authoritative data sources.

[1488] 2. The server stores the acquired data in a local database for temporary storage.

[1489] Step 2:

[1490] The server standardizes the format of the collected data and organizes it.

[1491] Specific behavior:

[1492] 1. The server detects duplicate data and removes the duplicate records.

[1493] 2. The server converts data from different formats into a unified format, organizing the data into common fields such as species name, region, and publication year.

[1494] Step 3:

[1495] The user inputs sensing data acquired on-site into the system.

[1496] Specific behavior:

[1497] 1. The user logs into the system using a dedicated terminal or a web interface.

[1498] 2. The user enters the collected data (e.g., plant photos, latitude and longitude information, etc.) into the upload form.

[1499] 3. The sensing data is sent to the server and stored in a local database.

[1500] Step 4:

[1501] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots.

[1502] Specific behavior:

[1503] 1. The server pre-processes the data and creates a dataset for analysis.

[1504] 2. The server applies a machine learning model (e.g., random forest algorithm) to detect biodiversity hotspots in a specific area.

[1505] 3. The analysis results will be saved in a specified folder on the server.

[1506] Step 5:

[1507] The server formulates guidelines based on the analysis results and generates a report.

[1508] Specific behavior:

[1509] 1. The server extracts the necessary information from the analysis results.

[1510] 2. The server creates a report according to a standard format and generates it in PDF format.

[1511] 3. The report is saved to the specified folder on the server.

[1512] Step 6:

[1513] The terminal displays the generated report to the user and collects feedback.

[1514] Specific behavior:

[1515] 1. After user authentication, the terminal provides an interface to display the generated report.

[1516] 2. Users view the report and use the feedback form to provide their ratings and comments.

[1517] 3. The feedback is sent to the server.

[1518] Step 7:

[1519] The server updates the system based on feedback collected from users, and continuously collects, analyzes, and improves data.

[1520] Specific behavior:

[1521] 1. The server analyzes user feedback and identifies areas for improvement.

[1522] 2. The server updates the system configuration to incorporate new data collection and analysis algorithms.

[1523] 3. The server periodically re-runs the data collection and analysis and updates the reports.

[1524] Through these steps, the system will establish biodiversity conservation standards unique to Japan, enabling optimal conservation measures to be implemented in each region.

[1525] Example 1

[1526] 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."

[1527] Biodiversity conservation and management requires the collection, organization, and analysis of large amounts of data. However, this process is time-consuming and labor-intensive, making it difficult to carry out efficiently. Furthermore, to formulate optimal conservation guidelines for each region, it is necessary to effectively integrate and analyze field survey data and existing data. Furthermore, it is also necessary to reflect feedback and continuously improve the system. A system that can solve this problem and implement efficient and accurate biodiversity conservation measures is needed.

[1528] 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.

[1529] In this invention, the server includes means for collecting information on biodiversity from domestic and international sources, means for converting the collected information into a unified format and organizing it, means for inputting sensing information from field surveys, means for analyzing the information using a machine learning algorithm and identifying important ecological regions, means for formulating conservation guidelines and generating documents based on the analysis results, means for displaying the generated documents to users and collecting evaluations, and means for updating the system based on the evaluations and continuously collecting, analyzing, and improving the information. This enables the efficient collection, integration, and analysis of data necessary for biodiversity protection and management, and the formulation and implementation of optimal conservation measures for each region.

[1530] "Biodiversity" refers to the variety of mutations, species, individuals, and ecosystems of living organisms on Earth.

[1531] "Sources" refers to national and international databases and institutions that provide biodiversity data.

[1532] "Unified format" refers to converting data provided in different formats into a consistent data format.

[1533] "Maintenance" refers to the process of removing duplicates and missing data and cleansing the collected data.

[1534] "Sensing information" refers to data obtained during field surveys (e.g., photographs, GPS coordinates, observation notes, etc.).

[1535] "Machine learning algorithms" refer to computer algorithms that analyze large amounts of data to generate patterns and make predictions.

[1536] "Critical ecoregions" refer to specific areas that are rich in biodiversity and require protection.

[1537] "Conservation guidelines" refer to specific guidelines and measures for effectively promoting biodiversity conservation.

[1538] "Document" refers to reports and reports containing analytical findings and conservation guidelines.

[1539] "User" refers to the person or organization that uses the system to input, analyze and evaluate biodiversity data.

[1540] "Evaluation" refers to feedback and suggestions for improvement that users give to the generated document.

[1541] "Update" refers to the process of improving the system based on collected evaluations and making improvements to enable more effective data collection and analysis.

[1542] The present invention relates to a system for efficiently collecting, integrating, and analyzing vast amounts of information in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. Specific embodiments of the present invention are described below.

[1543] Data collection

[1544] The server collects biodiversity information from domestic and international sources, including databases from international biodiversity information agencies and local environmental organizations. The server retrieves data from these sources via APIs. For example, the server automatically downloads the latest species list from the international biodiversity information agency.

[1545] Standardization and organization of data formats

[1546] The server converts the collected information into a unified format and organizes it. This includes converting data provided in different formats (e.g., CSV, JSON, XML) into a unified format. The server also performs data cleansing to remove duplicate data and missing values.

[1547] Sensing data input

[1548] Users input sensing information acquired during field surveys into the system. Using a dedicated mobile app, users input photos of plants taken on-site, GPS coordinates, observation notes, etc. This information is sent to the server in real time and stored in a database. As a specific example, a user may upload a photo of a new species of plant during a field survey and input its location information and observation notes.

[1549] Data analysis

[1550] The server uses machine learning algorithms to analyze the collected and organized information and identify important ecological regions. It uses Python machine learning libraries (e.g., scikit-learn and TensorFlow) to analyze areas with high biodiversity based on large amounts of data. For example, the server uses a random forest algorithm to identify biodiversity hotspots in areas with high rainfall.

[1551] Guideline formulation and report generation

[1552] The server formulates protection guidelines based on the analysis results and generates a document. A generative AI model (e.g., OpenAI's GPT-4) is used to automatically generate guidelines outlining specific protection measures. These guidelines are output as a PDF report and can be downloaded by users through a web portal. A specific example would be a scenario in which the server generates a report stating that "in areas with an annual rainfall of 1000 mm or more, protection of certain vegetation types is necessary."

[1553] Collecting feedback

[1554] The terminal displays the generated document to the user and collects their evaluation. The user is provided with an interface that allows them to check the report on the terminal and enter their evaluation of the generated guidelines and suggestions for improvement. For example, the user may submit comments on whether a specific guideline is feasible or what improvements can be made.

[1555] System Updates

[1556] The server updates the system based on the evaluations and continuously collects, analyzes, and improves the information. It analyzes user feedback to improve the machine learning model and data collection process. For example, the next time data is collected, new data items (such as soil pH data) are added and incorporated into the analysis algorithm.

[1557] Examples of prompt statements

[1558] As an example of a specific prompt, the following might be used when a user uploads data collected during a field survey:

[1559] "Please upload photos of plants taken during your field surveys and their locations into the system, along with notes on the habitat and weather conditions in which you observed them."

[1560] And when the server identifies new biodiversity hotspots and generates guidelines, the prompt is:

[1561] "Calculate and identify biodiversity hotspots in high-rainfall areas using up-to-date rainfall and vegetation data. Then, based on the identified hotspots, develop guidelines and generate reports for conservation measures appropriate for the area."

[1562] In this way, the present invention is a system that enables the efficient collection, integration, and analysis of data necessary for the protection and management of biodiversity, and supports the formulation and implementation of optimal conservation measures for each region.

[1563] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1564] Step 1: Data collection

[1565] The server collects information on biodiversity from domestic and international sources.

[1566] Input: API endpoints from databases such as international biodiversity information agencies and local environmental protection agencies.

[1567] Output: Raw datasets (e.g., species lists, meteorological data, vegetation data)

[1568] Specific operation: The server periodically sends API requests to retrieve data and saves it in local storage. For example, a CRON job can be used to automatically retrieve the latest data at 3:00 AM every day.

[1569] Step 2: Standardize and organize data formats

[1570] The server converts the collected information into a unified format and organizes it.

[1571] Input: Raw datasets acquired in different formats (CSV, JSON, XML).

[1572] Output: A uniformly formatted dataset

[1573] What happens: The server uses ETL (Extract, Transform, Load) tools to transform the data into a consistent format. Data cleansing scripts correct and remove duplicate entries and missing values. Data frame manipulations are performed, for example, using the Python Pandas library.

[1574] Step 3: Input of sensing data

[1575] The user inputs sensing information acquired during field surveys into the system.

[1576] Input: Data obtained during field surveys (plant photos, GPS coordinates, observation notes, etc.).

[1577] Output: Sensing information stored on the server

[1578] Specific operation: A user uses a mobile app to input photos and text data, which the app then uploads to a server. For example, a user might upload a photo of a new species discovered in the Southern Alps region and enter its GPS coordinates.

[1579] Step 4: Data analysis

[1580] The server uses machine learning algorithms to analyze the collected and organized information and identify important ecological regions.

[1581] Input: Organized dataset (integrated data of collected data and sensing data)

[1582] Output: List of important ecological regions and their analysis results

[1583] How it works: The server uses Python machine learning libraries (scikit-learn, TensorFlow) to apply algorithms (e.g., random forests) to analyze the data, for example, to identify biodiversity hotspots based on rainfall and local vegetation data.

[1584] Step 5: Guideline development and report generation

[1585] The server formulates protection guidelines based on the analysis results and generates a document.

[1586] Input: List of important ecological regions and their analysis results

[1587] Output: PDF report with protection guidelines

[1588] How it works: The server uses a generative AI model (such as OpenAI's GPT-4) to automatically generate protection guidelines based on the analysis results, and creates a report using a PDF generation library, which users can download from a web portal.

[1589] Step 6: Gather feedback

[1590] The terminal displays the generated document to the user and collects ratings.

[1591] Input: User evaluation of the report and suggestions for improvement

[1592] Output: Feedback information stored in the rating database

[1593] Specific operation: The user views the report on their device and enters comments and suggestions using the feedback form. This information is sent to the server in real time and stored in the database. For example, the user can enter feedback such as "This part is specific and easy to understand" or "This part is unclear and needs improvement."

[1594] Step 7: Update your system

[1595] The server updates the system based on the evaluation and continuously collects and analyzes information to improve it.

[1596] Input: Feedback information stored in the ratings database

[1597] Output: Improved data collection and analysis processes and updated systems

[1598] Specific operation: The server analyzes the feedback and adds new data items or improves the algorithm. For example, the next time data is collected, new soil pH data is collected and incorporated into the analysis algorithm. This improves the accuracy and efficiency of the entire system.

[1599] (Application example 1)

[1600] 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."

[1601] Conventional biodiversity conservation management systems are inefficient because they require a lot of manpower and time for data collection and analysis, and they lack management measures that are particularly adapted to the environment inside and outside factories. As a result, it has been difficult to formulate appropriate guidelines for minimizing the impact on ecosystems. To solve this problem, a system that allows for efficient and accurate data collection and analysis, and that takes into account practicality in the field, is needed.

[1602] 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.

[1603] In this invention, the server includes means for collecting data on biodiversity from domestic and international databases, means for standardizing and organizing the collected data, means for inputting sensing data from field surveys, means for analyzing the data using a machine learning algorithm and identifying important areas, means for formulating guidelines and generating reports based on the analysis results, means for displaying the generated reports to users and collecting feedback, and a robot that collects data from inside and outside a factory and proposes optimal management measures for ecosystem protection, including means for transmitting the data collected by the robot to the server and for the server to analyze the data, and means for generating the analysis results as guidelines and displaying them via the robot. This enables efficient and accurate data collection and analysis, and enables the formulation of appropriate guidelines that take practicality in the field into consideration.

[1604] A "database" is a collection of specific information that is systematically organized and stored so that it can be easily searched and retrieved.

[1605] "Biodiversity" is a term that refers to the variety of living species on Earth, their genetic variations, and the ecosystems they comprise.

[1606] "Data collection methods" are methods for obtaining information on biodiversity from domestic and international databases.

[1607] "Data preparation means" refers to the means of standardizing and organizing the collected data.

[1608] "Sensing data" refers to data on biodiversity obtained through field surveys.

[1609] A "machine learning algorithm" is a program that analyzes data and performs pattern recognition and prediction.

[1610] "Data analysis means" refers to a means of analyzing data using machine learning algorithms and identifying important areas.

[1611] A "guideline" is a document that provides guidelines or standards established to achieve a specific purpose.

[1612] The "report generation means" is a means for creating guidelines based on the analysis results and outputting them in document format.

[1613] The "feedback means" is a means for displaying the generated report to the user and collecting evaluations and comments from the user.

[1614] "Inside and outside the factory" is a term that refers to the inside of the factory and the surrounding area.

[1615] "Robots" are autonomous or semi-autonomous machines that collect biodiversity data from inside and outside factories and suggest management measures.

[1616] A "server" is a computer system that processes, stores, and provides data over a network.

[1617] The "analysis means" is a means for transmitting the collected data to a server and analyzing it using a machine learning algorithm.

[1618] The "display means" is a means for generating the analysis results as guidelines and displaying the information via the robot.

[1619] This invention relates to a system for efficiently collecting, integrating, and analyzing huge amounts of data in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. This system utilizes a robot that collects data from inside and outside factories and proposes optimal management measures for ecosystem protection.

[1620] 1. Data Collection

[1621] The server collects biodiversity information from domestic and international databases, such as the Global Biodiversity Information Facility (GBIF) and retrieves the latest species list via an API. The server also receives data collected by the robots inside and outside the factory and stores it in a database.

[1622] 2. Data Preparation

[1623] The server standardizes and organizes the collected data. This includes converting data collected from different databases into a unified format, eliminating duplicate data, normalizing data, etc. Organized data is important for improving the accuracy of subsequent data analysis.

[1624] 3. Input of sensing data

[1625] The user (or robot) inputs sensing data acquired on-site into the system. For example, they upload photos of plants collected during field surveys along with their location information. This data is then used for analysis on the server.

[1626] 4. Data Analysis

[1627] The server will analyze the data using machine learning algorithms to identify important biodiversity hotspots. For example, it will use a random forest algorithm to identify biodiversity hotspots in areas with high rainfall. The results of this analysis will form the basis for developing guidelines.

[1628] 5. Establishment of guidelines

[1629] The server then formulates guidelines based on the analysis results and generates reports, such as protection measures based on climate zones and seasons, and provides them to users. These guidelines include specific procedures and monitoring methods for ecosystem protection.

[1630] 6. Gathering Feedback

[1631] The terminal displays the generated report to the user and provides an interface for collecting feedback. For example, after viewing the report, the user can enter their rating and comments. This feedback is used to improve the system.

[1632] 7. System Updates

[1633] The server will update the system based on feedback and continuously improve data collection and analysis, thereby establishing Japan's own biodiversity conservation standards and implementing optimal conservation measures for each region.

[1634] Hardware and software used

[1635] Hardware:

[1636] Robots: Devices that collect data from inside and outside the factory.

[1637] Server: A computer system that processes and stores data.

[1638] software:

[1639] requests: A Python package for retrieving data from data collection APIs.

[1640] scikit-learn: A Python library for running machine learning algorithms (e.g., the Random Forest algorithm).

[1641] Use of such a system will enable efficient and accurate data collection and analysis, making it possible to formulate appropriate guidelines that take into account practicality in the field.

[1642] Examples of concrete examples and prompts

[1643] Examples:

[1644] For example, in high humidity areas of a factory, guidelines may be created such as "maintain specific plant habitats and conduct monthly monitoring."

[1645] Example prompt sentence:

[1646] "Implement a detailed ecosystem analysis using data obtained from API and a guideline development program based on the results."

[1647] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1648] Step 1:

[1649] The server collects biodiversity information from domestic and international databases. Specifically, the server uses APIs to send requests to each database and temporarily stores the acquired data. The input is the API endpoint and authentication information, and the output is the acquired biodiversity data.

[1650] Step 2:

[1651] The server standardizes and organizes the collected data. Because the acquired data is in different formats, the server converts it into a standard format and eliminates duplicate data. This process is called data cleaning and is important for improving the accuracy of data analysis. The input is raw biodiversity data, and the output is organized data.

[1652] Step 3:

[1653] Users input sensing data acquired during field surveys into the system. For example, they upload photos of plants taken during field surveys and GPS location information. This data is sent to the server and integrated with other data. The input is sensing data from field surveys, and the output is the integrated data uploaded to the server.

[1654] Step 4:

[1655] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots. For example, a random forest algorithm can be used to analyze the data and identify high biodiversity areas. The server inputs the data into the model and obtains a list of important areas as output. The input is the consolidated data, and the output is the analysis results.

[1656] Step 5:

[1657] The server formulates guidelines based on the analysis results and generates a report. Based on the analysis results, it creates a document detailing ecosystem protection measures for each region and formats it as a report. The input is the results of the data analysis, and the output is the generated report.

[1658] Step 6:

[1659] The terminal provides an interface for displaying the generated report to the user and collecting feedback. The user views the report and enters their evaluation and comments on its contents as feedback. The input is the generated report and the user's evaluation and comments, and the output is the collected feedback.

[1660] Step 7:

[1661] The server updates the system based on the feedback and continuously improves data collection and analysis. It analyzes the feedback information, makes necessary corrections and adds features, and improves the accuracy and usability of the entire system. The input is user feedback, and the output is an updated system.

[1662] 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.

[1663] The present invention relates to a system for efficiently collecting, integrating, and analyzing vast amounts of data in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. Furthermore, by combining it with an emotion engine that recognizes user emotions, the quality of user feedback is improved, contributing to the improvement of the entire system. A specific embodiment of the system of the present invention is described below.

[1664] System Overview

[1665] The system includes the following elements:

[1666] 1. Data collection method: The server accesses domestic and international databases to collect data on biodiversity.

[1667] 2. Data preparation method: The server standardizes and organizes the collected data.

[1668] 3. Sensing data input method: The user inputs the sensing data acquired during the field survey into the system.

[1669] 4. Data analysis method: The server uses machine learning algorithms to analyze the data and identify key areas.

[1670] 5. Guideline formulation means: The server formulates guidelines based on the analysis results and generates a report.

[1671] 6. Feedback means: The terminal displays the generated report to the user and collects feedback.

[1672] 7. Emotion engine: The emotion engine built into the device analyzes the user's emotions and understands their emotional state at the time of feedback.

[1673] 8. System update method: The server updates the system based on feedback and analysis results from the emotion engine, and continuously collects, analyzes, and improves data.

[1674] Natural language explanation of program processing

[1675] 1. Data Collection

[1676] The server collects biodiversity data from public and private databases both domestically and internationally.

[1677] Example: A server downloads the latest species list from the International Biodiversity Information Facility via an API.

[1678] 2. Standardization and organization of data formats

[1679] The server compiles and organizes the collected data.

[1680] Example: The server converts data collected from different databases into a unified format and eliminates duplicate data.

[1681] 3. Input of sensing data

[1682] The user inputs the data acquired on-site into the system.

[1683] Example: A user uploads photos and location information of plants collected during a field survey.

[1684] 4. Data Analysis

[1685] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots.

[1686] Example: The server uses a random forest algorithm to identify biodiversity hotspots in rainy regions.

[1687] 5. Guideline formulation and report generation

[1688] The server formulates guidelines based on the analysis results and generates a report.

[1689] Example: The server generates reports of protection measures based on climate zone and season.

[1690] 6. Gathering Feedback

[1691] The terminal displays the generated report to the user and collects feedback.

[1692] Example: The device provides an interface for users to view reports and collect their ratings and comments.

[1693] 7. Emotion Analysis Using an Emotion Engine

[1694] The terminal analyzes the user's emotions using an emotion engine when the user inputs feedback.

[1695] Example: The device uses a camera and microphone to analyze the user's facial expressions and tone of voice to understand the user's emotional state (e.g., satisfaction, dissatisfaction, interest).

[1696] 8. System Updates

[1697] The server updates the system based on feedback and the results of the emotion engine analysis, continuously collecting and analyzing data to optimize Japan's unique biodiversity conservation standards.

[1698] Example: The server reflects user feedback and sentiment analysis and adds new items to the next data collection and analysis.

[1699] In this way, the system of the present invention will establish biodiversity conservation standards unique to Japan and enable the implementation of optimal conservation measures for each region based on high-quality feedback that also takes user emotions into consideration.

[1700] The processing flow will be explained below.

[1701] Step 1:

[1702] The server will access domestic and international databases and collect data on biodiversity.

[1703] Specific behavior:

[1704] 1. The server uses the API to send requests to the International Biodiversity Information Facility and other authoritative data sources.

[1705] 2. The server stores the acquired data in a local database for temporary storage.

[1706] Step 2:

[1707] The server standardizes the format of the collected data and organizes it.

[1708] Specific behavior:

[1709] 1. The server detects duplicate data and removes the duplicate records.

[1710] 2. The server converts data from different formats into a unified format, organizing the data into common fields such as species name, region, and publication year.

[1711] Step 3:

[1712] The user inputs sensing data acquired on-site into the system.

[1713] Specific behavior:

[1714] 1. The user logs into the system using a dedicated terminal or a web interface.

[1715] 2. The user enters the collected data (e.g., plant photos, latitude and longitude information, etc.) into the upload form.

[1716] 3. The sensing data is sent to the server and stored in a local database.

[1717] Step 4:

[1718] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots.

[1719] Specific behavior:

[1720] 1. The server pre-processes the data and creates a dataset for analysis.

[1721] 2. The server applies a machine learning model (e.g., random forest algorithm) to detect biodiversity hotspots in a specific area.

[1722] 3. The analysis results will be saved in a specified folder on the server.

[1723] Step 5:

[1724] The server formulates guidelines based on the analysis results and generates a report.

[1725] Specific behavior:

[1726] 1. The server extracts the necessary information from the analysis results.

[1727] 2. The server creates a report according to a standard format and generates it in PDF format.

[1728] 3. The report is saved to the specified folder on the server.

[1729] Step 6:

[1730] The terminal displays the generated report to the user and collects feedback.

[1731] Specific behavior:

[1732] 1. After user authentication, the terminal provides an interface to display the generated report.

[1733] 2. Users view the report and use the feedback form to provide their ratings and comments.

[1734] 3. The feedback is sent to the server.

[1735] Step 7:

[1736] The terminal analyzes the user's emotions using an emotion engine when the user inputs feedback.

[1737] Specific behavior:

[1738] 1. The device uses a camera and microphone to record the user's facial expressions and tone of voice.

[1739] 2. The device uses an emotion engine to analyze this data and determine the user's emotional state.

[1740] 3. The sentiment analysis results are sent to the server.

[1741] Step 8:

[1742] The server updates the system based on feedback collected from users and the analysis results of the emotion engine, and continuously collects, analyzes, and improves the data.

[1743] Specific behavior:

[1744] 1. The server analyzes user feedback and sentiment analysis results to identify areas for improvement.

[1745] 2. The server updates the system configuration to incorporate new data collection and analysis algorithms.

[1746] 3. The server periodically re-runs the data collection and analysis and updates the reports.

[1747] Through these steps, the system will establish Japan's own biodiversity conservation standards and enable optimal conservation measures to be implemented for each region. Furthermore, by taking user sentiment into account, the system will be able to reflect more effective feedback and improve the accuracy of the system.

[1748] Example 2

[1749] 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."

[1750] The problem that this invention aims to solve is to achieve sustainable conservation of biodiversity by efficiently collecting, integrating, and analyzing huge amounts of data and formulating optimal guidelines for each region based on the results. The invention also aims to provide a system that incorporates a function to recognize user emotions in order to improve the quality of user feedback and utilize it to improve the system.

[1751] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1752] In this invention, the server includes means for collecting biodiversity data from domestic and international databases, means for standardizing the format of the collected data and organizing it, means for inputting sensing data from field surveys, means for analyzing the data using a machine learning algorithm and identifying important areas, means for formulating guidelines and generating reports based on the analysis results, means for displaying the generated reports to users and collecting feedback, an emotion engine for analyzing user emotions, and means for updating the system based on the feedback and the analysis results of the emotion engine. This enables efficient data collection and analysis, the formulation of optimal guidelines for each area, improved quality of user feedback, and continuous improvement of the system.

[1753] "Data collection tools" are tools for collecting data on biodiversity from domestic and international databases.

[1754] "Data preparation means" refers to the means for standardizing and preparing the format of collected data.

[1755] The "sensing data input means" is a means for a user to input sensing data acquired during a field survey into the system.

[1756] "Data analysis means" refers to means for analyzing collected data using machine learning algorithms and identifying key areas.

[1757] The "guideline formulation means" is a means for formulating guidelines based on the analysis results and generating reports.

[1758] The "feedback collection means" is a means for displaying the generated report to the user and collecting feedback.

[1759] The "emotion engine" is an engine that has a function for analyzing the user's emotions.

[1760] The "system update means" is a means for updating the system based on the feedback and the analysis results of the emotion engine.

[1761] The present invention relates to a system for efficiently collecting, integrating, and analyzing vast amounts of data in the protection and management of biodiversity, and formulating optimal guidelines for each region based on the results. Furthermore, by combining it with an emotion engine that recognizes user emotions, the quality of user feedback is improved, contributing to the improvement of the entire system. A specific embodiment of the system of the present invention is described below.

[1762] Data collection methods

[1763] The server collects biodiversity data from public and private databases both in Japan and overseas. The server runs a regularly scheduled task, accessing each database (e.g., databases of domestic and international biodiversity information facilities and the Ministry of the Environment) using an API key to download species lists and habitat data.

[1764] Example: A server downloads the latest species list from the International Biodiversity Information Facility via an API.

[1765] Prompt text: Use the following API key to get the latest species list from the International Biodiversity Information Facility.

[1766] Data preparation methods

[1767] The server aligns and organizes the collected data. It uses the Python Pandas library to read the data and convert it from different formats (CSV, JSON, XML, etc.) into a unified data frame format. It also removes duplicate data and completes missing information.

[1768] Example: The server converts data collected from different databases into a unified format and eliminates duplicate data.

[1769] Prompt: Transform biodiversity data from different data sources into a unified format and eliminate duplicate data.

[1770] Sensing data input means

[1771] Users input data acquired on-site into the system. Using a smartphone or tablet, users upload photos taken during field surveys and location information to a dedicated app. The app includes a photo upload button and an automatic location information acquisition function.

[1772] Example: A user uploads photos of plants taken during a field survey along with their location.

[1773] Prompt: Upload photos of plants taken during field research and their locations to the app.

[1774] Data Analysis Methods

[1775] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots. The server uses the SciKit-Learn library to apply a random forest algorithm to identify biodiversity hotspots from the collected data. The algorithm is pre-trained with training data.

[1776] Example: The server uses a random forest algorithm to identify biodiversity hotspots in rainy regions.

[1777] Prompt: Using the collected plant species and location information, run a random forest algorithm to identify biodiversity hotspots.

[1778] Guideline formulation method

[1779] The server formulates guidelines based on the analysis results and generates a report.The server uses a report generation tool (e.g., LaTeX or a PDF generation library) to create a document containing protection guidelines and measures based on the analysis results.

[1780] Example: The server generates reports of protection measures based on climate zone and season.

[1781] Prompt: Generate a report containing conservation measures for biodiversity hotspots.

[1782] Feedback collection methods

[1783] The terminal displays the generated report to the user and collects feedback. The terminal displays the report through a user interface and provides a form for accepting ratings and comments from the user. The feedback is sent to the server and automatically saved.

[1784] Example: The device provides an interface for users to view reports and collect ratings and comments.

[1785] Prompt: Displays an interface that allows the user to view the report and enter ratings and comments.

[1786] Emotion Engine

[1787] The device uses an emotion engine to analyze the user's emotions when the user enters feedback. The device uses a built-in camera and microphone to record the user's facial expressions and tone of voice, and analyzes them using the emotion engine. The analysis results are used to evaluate the user's satisfaction and interest.

[1788] Example: The device uses a camera and microphone to analyze the user's facial expressions and tone of voice to understand the user's emotional state.

[1789] Prompt text: Uses the camera and microphone to analyze the user's facial expressions and tone of voice to understand the user's emotional state.

[1790] System Update Method

[1791] The server updates the system based on feedback and the results of the sentiment engine analysis, continuously collecting and analyzing data, and optimizing Japan's unique biodiversity conservation standards. The server analyzes the feedback database and sentiment analysis results to identify areas for improvement and update the system's algorithms and data collection methods, thereby adding new items to the next data collection and analysis.

[1792] Example: The server reflects user feedback and sentiment analysis and adds new items to the next data collection and analysis.

[1793] Prompt: We will implement system updates to improve our data collection and analysis processes based on user feedback and sentiment analysis.

[1794] In this way, the system of the present invention will establish biodiversity conservation standards unique to Japan and enable the implementation of optimal conservation measures for each region based on high-quality feedback that also takes user emotions into consideration.

[1795] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1796] Step 1:

[1797] Data collection

[1798] The server collects biodiversity data from public and private databases both domestically and internationally. It runs a regularly scheduled task to access each database using an API key and download species lists and habitat data.

[1799] Input: API key, database URL

[1800] Output: Biodiversity data in JSON and CSV format

[1801] Specific operation: The server uses a Python script to retrieve data from the API, sending an API request and saving the retrieved JSON or CSV data in local storage.

[1802] Step 2:

[1803] Standardization and organization of data formats

[1804] The server aligns and organizes the collected data. It uses the Python Pandas library to read the data and convert it from different formats (CSV, JSON, XML, etc.) into a unified data frame format. It removes duplicate data and completes missing information.

[1805] Input: Biodiversity data (JSON, CSV, XML)

[1806] Output: Cleaned data frame

[1807] Specific operation: The server converts the data collected from each data source into a Pandas data frame, standardizes the format, removes duplicate data, and completes missing data.

[1808] Step 3:

[1809] Sensing data input

[1810] Users input data acquired on-site into the system, using a smartphone or tablet to upload photos taken during field surveys and location information to a dedicated app.

[1811] Input: Plant photo, location information

[1812] Output: Sensing data (photo files, location information) uploaded to the server

[1813] Specific operation: The user presses the photo upload button in the app, selects the photo they took, confirms that the location information is automatically acquired, and uploads it. This sends the data to the server.

[1814] Step 4:

[1815] Data analysis

[1816] The server uses machine learning algorithms to analyze the data and identify important biodiversity hotspots. It uses the SciKit-Learn library to apply a random forest algorithm to identify biodiversity hotspots from the collected data.

[1817] Input: Prepared data frame, sensing data

[1818] Output: List of hotspots

[1819] How it works: The server trains a random forest algorithm with training data, and then inputs newly collected data into the model to identify hotspots.

[1820] Step 5:

[1821] Guideline formulation and report generation

[1822] The server formulates guidelines based on the analysis results and generates a report. Using the report generation tool, the server creates a document of protection guidelines and measures based on the analysis results.

[1823] Input: List of critical areas, past guidelines

[1824] Output: Report in PDF format

[1825] Specific Actions: The server uses LaTeX and PDF generation libraries to create a report containing specific protection measures based on the identified hotspots and outputs it in PDF format.

[1826] Step 6:

[1827] Collecting feedback

[1828] The terminal displays the generated report to the user and collects feedback. The terminal displays the report through a user interface and provides a form for accepting ratings and comments from the user.

[1829] Input: Report in PDF format

[1830] Output: User feedback (ratings, comments)

[1831] Specific operation: The terminal provides a report display screen, receives user ratings and comments through a feedback input form, and sends them to the server.

[1832] Step 7:

[1833] Emotion analysis using an emotion engine

[1834] The device uses an emotion engine to analyze the user's emotions when they provide feedback, and uses the built-in camera and microphone to record and analyze the user's facial expressions and tone of voice.

[1835] Input: User's facial expression data, voice data

[1836] Output: Analysis of the user's emotional state (satisfaction, dissatisfaction, interest)

[1837] Specific operation: The device uses a camera and microphone to record the user's facial expressions and voice in real time, and analyzes their emotional state using an emotion engine. The analysis results are sent to the server as evaluation data.

[1838] Step 8:

[1839] System Updates

[1840] The server updates the system based on feedback and the results of the sentiment engine analysis, continuously collects and analyzes data, and optimizes Japan's unique biodiversity conservation standards. Feedback data and sentiment analysis results are analyzed to identify areas for improvement, and the system's algorithms and data collection methods are updated.

[1841] Input: User feedback, sentiment analysis results

[1842] Output: Updated data acquisition and analysis system

[1843] Specific operation: Based on the feedback and sentiment analysis, the server adds improvements to be reflected in the next data collection and analysis, and updates the system code.

[1844] (Application example 2)

[1845] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1846] Biodiversity conservation and management requires the efficient collection, integration, and analysis of vast amounts of data to formulate optimal guidelines for each region. However, it is necessary to collect not only on-site feedback but also high-quality feedback that combines sentiment analysis. It is also necessary to collect environmental data from physical stores, evaluate eco-friendliness based on that data, and develop a system for continuous improvement.

[1847] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1848] In this invention, the server includes means for collecting biodiversity data from domestic and international databases, means for standardizing and organizing the collected data, means for inputting sensing data from field surveys, means for analyzing the data using a machine learning algorithm and identifying priority areas, means for formulating guidelines and generating reports based on the analysis results, means for displaying the generated reports to users and collecting feedback, means for analyzing user emotions using a sentiment analysis engine, means for collecting environmental data at physical stores, means for conducting eco-friendliness evaluations based on the collected environmental data and generating guidelines, means for collecting customer feedback after using the store, means for updating the system based on the collected feedback, and means for analyzing customers' facial expressions and voices to understand their emotional states, thereby enabling the formulation of guidelines for biodiversity protection and the evaluation and improvement of eco-friendly initiatives at physical stores.

[1849] A "database" is a collection of data that is organized to efficiently manage and search large amounts of information.

[1850] "Biodiversity" means the variety of different living species and the ecosystems they use.

[1851] "Data collection methods" are the means by which the necessary information is gathered from various sources.

[1852] "Data preparation means" refers to the means of unifying collected data into a consistent format and making it usable.

[1853] "Sensing data" refers to data about the environment and living organisms obtained through field surveys.

[1854] "Data analysis means" refers to a means of analyzing collected data using machine learning algorithms, etc.

[1855] "Guideline formulation means" refers to the means of creating appropriate guidelines for action based on the results of data analysis.

[1856] The "report generation means" is a means for organizing the analysis results and outputting them as a document.

[1857] "Feedback collection means" refers to the means of collecting evaluations and opinions from system users.

[1858] An "emotion analysis engine" is software or a system that analyzes a user's facial expressions and voice to understand their emotional state.

[1859] "Environmental Data" refers to data about the surrounding environment, such as temperature, humidity, CO2 levels, etc.

[1860] "Eco-friendly evaluation" is a process that evaluates whether a product has a low environmental impact.

[1861] "System update methods" are methods for adapting and improving the entire system based on collected feedback and analysis results.

[1862] The system of this invention is composed of many elements and is realized through the interaction of three entities: a server, a terminal, and a user. The purpose of this system is to support the conservation of biodiversity and the evaluation and improvement of eco-friendly efforts in physical stores.

[1863] Hardware and Software Configuration

[1864] 1. Data Collection

[1865] The server will collect biodiversity data from domestic and international databases, including the International Biodiversity Information Facility, and retrieve data via API.

[1866] 2. Data Preparation

[1867] The server standardizes and organizes the collected data. It performs a data conversion process to standardize the data into a specific format and remove duplicate and missing data.

[1868] 3. Input of sensing data

[1869] Users input sensing data obtained during field surveys into the system, including photos of plants collected during the field survey and their location information.

[1870] 4. Data Analysis

[1871] The server then uses machine learning algorithms to analyze the collected data and identify important biodiversity hotspots, using algorithms such as random forests in the process.

[1872] 5. Guideline formulation and report generation

[1873] Based on the analysis, the server develops guidelines and generates a report detailing protective measures for different climate zones and seasons.

[1874] 6. Gathering Feedback

[1875] The terminal displays the generated report to the user and provides an interface for collecting feedback. The user can view the report and enter their rating and comments.

[1876] 7. Emotion analysis

[1877] The device uses an emotion analysis engine to analyze the user's emotions. It uses a camera (e.g., Logitech C920) and a microphone (e.g., Blue Yeti) to analyze the user's facial expressions and tone of voice to understand their emotional state.

[1878] 8. System Updates

[1879] The server updates the system based on the collected feedback and sentiment analysis results, allowing new items to be added to the next data collection and analysis, enabling continuous improvement of the system.

[1880] Examples:

[1881] For example, the server downloads the latest biodiversity species list from the International Biodiversity Information Facility via API and integrates it with data from other databases. Researchers then upload photos of plants taken in the field and their location information. The server analyzes this data using a random forest algorithm to identify biodiversity hotspots in rainy regions. The generated report is displayed on the device for users to view, and feedback is collected. A sentiment analysis engine analyzes users' facial expressions and tone of voice to determine their level of satisfaction or dissatisfaction. Finally, the server uses the collected feedback and the results of the sentiment analysis to reflect this in the next system update.

[1882] Example prompt sentence:

[1883] "Provide a concrete example of an eco-shop management application that generates optimal guidelines for biodiversity conservation. Include a function that uses a random forest algorithm to evaluate the shop's eco-friendliness based on environmental data collected within the shop (e.g., temperature, humidity, CO2 levels), and report areas for improvement. Also explain how customer feedback and sentiment analysis can be used to improve store operations."

[1884] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1885] Step 1:

[1886] The server collects biodiversity data from domestic and international databases. As input, it uses data obtained from the International Biodiversity Information Facility via API. The data downloaded from the database is in its raw state. As output, it obtains the collected biodiversity dataset.

[1887] Step 2:

[1888] The server standardizes and organizes the collected data. As input, it uses the biodiversity data collected in step 1. To standardize the data, it converts different data structures into a common format and removes duplicates and missing data. As output, it obtains a uniformly formatted dataset.

[1889] Step 3:

[1890] Users input sensing data obtained from field surveys into the system. The input includes photos of the surveyed plants, location information, and environmental data (temperature, humidity, etc.). This data is raw data obtained from the field survey. The output is the field survey data uploaded to the system.

[1891] Step 4:

[1892] The server analyzes the data using machine learning algorithms to identify important biodiversity hotspots. As input, it uses the dataset prepared in step 2 and the sensing data uploaded in step 3. It applies algorithms such as random forests and performs analysis. As output, it obtains a list of important areas.

[1893] Step 5:

[1894] The server formulates guidelines and generates reports based on the analysis results. It uses the list of critical areas obtained in step 4 as input. It sets guidelines including protective measures according to climate zones and seasons, and creates and outputs reports based on them.

[1895] Step 6:

[1896] The terminal displays the generated report to the user and collects feedback. As input, it uses the report generated in step 5. The user enters feedback after viewing the report. As output, it obtains the user's feedback data.

[1897] Step 7:

[1898] The device analyzes the user's emotions using an emotion analysis engine. The input is the user's facial expression data and voice data collected by a camera and microphone. Specifically, the emotion analysis software performs facial expression recognition and voice tone analysis. The output is the user's emotion data.

[1899] Step 8:

[1900] The server updates the system based on the collected feedback and sentiment analysis results. As input, it uses the feedback data obtained in step 6 and the sentiment data obtained in step 7. It performs processing to improve the analysis algorithms and data collection methods of the entire system. As output, it obtains updated data collection and analysis models.

[1901] This will enable the development of guidelines for biodiversity conservation and the evaluation and improvement of eco-friendly initiatives in physical stores.

[1902] 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.

[1903] 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.

[1904] 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.

[1905] 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.

[1906] 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.

[1907] 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.

[1908] 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).

[1909] 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.

[1910] 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."

[1911] 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.

[1912] 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).

[1913] 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.

[1914] 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.

[1915] 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.

[1916] 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.

[1917] 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.

[1918] 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.

[1919] 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.

[1920] 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.

[1921] 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.

[1922] 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.

[1923] The following is further disclosed regarding the above embodiment.

[1924] (Claim 1)

[1925] means of collecting biodiversity data from national and international databases;

[1926] A means to standardize and organize the collected data,

[1927] A means for inputting sensing data from field surveys;

[1928] A means of analyzing data using machine learning algorithms to identify key areas;

[1929] a means for formulating guidelines and generating reports based on the analysis results;

[1930] and means for displaying the generated report to a user and collecting feedback.

[1931] (Claim 2)

[1932] 10. The system of claim 1, further comprising means for updating the system based on said feedback and for continuously collecting, analyzing and improving data.

[1933] (Claim 3)

[1934] 2. The system of claim 1, wherein the data collection means obtains data from the International Biodiversity Information Facility.

[1935] (Claim 4)

[1936] 2. The system of claim 1, wherein the sensing data input means includes field survey data obtained using a drone.

[1937] (Claim 5)

[1938] The system of claim 1 , wherein the machine learning algorithm comprises a random forest algorithm.

[1939] "Example 1"

[1940] (Claim 1)

[1941] means of collecting information on biodiversity from national and international sources;

[1942] A means of converting and organizing the collected information into a unified format;

[1943] A means for inputting sensing information from field surveys;

[1944] a means of analyzing the information using machine learning algorithms to identify important ecological regions;

[1945] A means for formulating protection guidelines and generating documentation based on the analysis results;

[1946] and means for displaying the generated document to users and collecting ratings.

[1947] (Claim 2)

[1948] The system of claim 1, further comprising means for updating the system based on said evaluation, and continuously collecting and analyzing information for improvement.

[1949] (Claim 3)

[1950] 2. The system according to claim 1, wherein the information gathering means acquires information from the International Biodiversity Information Center.

[1951] "Application Example 1"

[1952] (Claim 1)

[1953] means of collecting biodiversity data from national and international databases;

[1954] A means to standardize and organize the collected data,

[1955] A means for inputting sensing data from field surveys;

[1956] A means of analyzing data using machine learning algorithms to identify key areas;

[1957] a means for formulating guidelines and generating reports based on the analysis results;

[1958] a means for displaying the generated report to a user and collecting feedback;

[1959] It is about a robot that collects data from inside and outside the factory and proposes optimal management measures for protecting the ecosystem.

[1960] a means for transmitting the data collected by the robot to a server, and for the server to analyze the data;

[1961] The system includes a means for generating analysis results as guidelines and displaying them via the robot.

[1962] (Claim 2)

[1963] 10. The system of claim 1, further comprising means for updating the system based on feedback and for continually collecting, analyzing, and improving data.

[1964] (Claim 3)

[1965] 10. The system of claim 1, which obtains data from the International Biodiversity Information Facility, and further includes a robot for environmental monitoring and management to protect biodiversity inside and outside the factory.

[1966] "Example 2: Combining Emotion Engines"

[1967] (Claim 1)

[1968] means of collecting biodiversity data from national and international databases;

[1969] A means to standardize and organize the collected data,

[1970] A means for inputting sensing data from field surveys;

[1971] A means of analyzing data using machine learning algorithms to identify key areas;

[1972] a means for formulating guidelines and generating reports based on the analysis results;

[1973] a means for displaying the generated report to a user and collecting feedback;

[1974] an emotion engine that analyzes the user's emotions;

[1975] a means for updating the system based on the feedback and analysis of the emotion engine;

[1976] A system including:

[1977] (Claim 2)

[1978] 10. The system of claim 1, further comprising means for updating the system based on said feedback and for continuously collect...

Claims

1. means of collecting biodiversity data from national and international databases; A means to standardize and organize the collected data, A means for inputting sensing data from field surveys; A means of analyzing data using machine learning algorithms to identify key areas; a means for formulating guidelines and generating reports based on the analysis results; and means for displaying the generated report to a user and collecting feedback.

2. 10. The system of claim 1, further comprising means for updating the system based on said feedback and for continually collecting, analyzing and improving data.

3. 2. The system of claim 1, wherein the data collection means obtains data from the International Biodiversity Information Facility.

4. The system of claim 1 , wherein the sensing data input means includes field survey data obtained using a drone.

5. The system of claim 1 , wherein the machine learning algorithm comprises a random forest algorithm.

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