system

Abstract

Provide a system. [Solution] A means of collecting data related to politics, economics, education, childcare, culture, science, and law from around the world; A means of analyzing the collected data to discover significant trends and patterns; A means of conducting scenario simulations based on the analysis results and generating optimal policies; A means to check the transparency of the policy proposals generated and compare them with lobby databases of relevant parties, etc. A means to automatically manage legislative and election-related budgets and identify possible cost savings; A means for users to access the cloud server and check the progress and proposals of policies in real time, A system including:

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] In the current political system, policymaking is slow and there is a risk of corruption, such as collusion. Furthermore, the budgets related to lawmakers and elections are large, which leads to a waste of national resources. Furthermore, the lack of transparency often leads to public distrust of the policymaking process. It is necessary to resolve these issues and create an efficient and fair political system. [Means for solving the problem]

[0005] The present invention provides a system that includes means for collecting data related to politics, economics, education, childcare, culture, science, and law from around the world, means for analyzing the collected data and discovering important trends and patterns, means for conducting scenario simulations based on the analysis results to generate optimal policies, means for verifying the transparency of the generated policy proposals and comparing them with lobby databases of relevant parties, means for automatically managing budgets related to lawmakers and elections and identifying expenses that can be reduced, and means for users to access a server on the cloud and check the progress of policies and proposals in real time. This will speed up policymaking, prevent corrupt practices such as collusion, enable efficient budget management, and realize a highly transparent political process.

[0006] "Data collection tools" are tools capable of obtaining data related to politics, economics, education, childcare, culture, science, and law from public databases, research papers, news articles, etc. from around the world.

[0007] "Data analysis means" refers to means that have the function of pre-processing collected data and discovering important trends and patterns using machine learning algorithms, etc.

[0008] "Scenario simulation" is a method of creating hypothetical policy scenarios based on analytical results and conducting mock experiments to evaluate their effects and impacts.

[0009] A "policy generation means" is a means that has the function of automatically generating optimal policies based on the results of scenario simulations.

[0010] "Transparency verification measures" are measures that have the function of checking the generated policy proposals against lobby databases of relevant parties, etc., in order to prevent fraudulent activities and collusion.

[0011] A "budget management tool" is a tool that has the function of automatically managing budgets related to legislators and elections, and extracting expenses that can be reduced.

[0012] "Cloud access means" refers to a means by which users can access a server on the cloud via the Internet and check policy progress and proposals in real time. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] This invention provides a comprehensive system for collecting, analyzing, formulating policies, verifying transparency, managing budgets, and accessing data related to politics, economics, education, childcare, culture, science, and law from around the world. This system has the functions to speed up policymaking, prevent fraud, efficiently manage budgets, and improve transparency.

[0035] Each function of the present invention will now be described in detail.

[0036] Data collection methods

[0037] The server periodically collects data related to politics, economics, education, childcare, culture, science, law, and other areas from public databases, research papers, news articles, etc. This information includes a wide range of data, such as economic statistics, educational achievements, and scientific research progress.

[0038] Examples:

[0039] The server uses APIs to retrieve economic statistics from the World Bank database and store them in the system.

[0040] The server uses web crawling technology to download the latest research papers and store them in a database.

[0041] Data Analysis Methods

[0042] The server pre-processes the collected data (e.g., noise removal, data normalization) and analyzes it using machine learning algorithms and statistical analysis to discover significant trends and patterns.

[0043] Examples:

[0044] The server performs a regression analysis to investigate the impact of educational investment on economic growth.

[0045] The server performs clustering to group data that have similar policy effects.

[0046] Policy generation means

[0047] The server then performs scenario simulations based on the analysis results to generate optimal policies. Scenario simulations are mock experiments that create hypothetical policy scenarios and evaluate their effects and impacts.

[0048] Examples:

[0049] The server simulates different education policy scenarios and identifies those that contribute most to economic growth.

[0050] The server compares multiple policy proposals and selects the most appropriate one.

[0051] Transparency confirmation method

[0052] The server checks the transparency of the generated policy proposals and compares them with lobby databases of relevant parties, etc. This measure is intended to prevent fraud and collusion.

[0053] Examples:

[0054] The server investigates the ties between each submitted policy proposal and the relevant companies and organizations.

[0055] The server checks the lobbying records to detect possible fraud.

[0056] Budget Control Tools

[0057] The server automatically manages budgets related to legislators and elections and extracts expenses that can be reduced. This function reduces wasteful spending and enables efficient budget management.

[0058] Examples:

[0059] The server analyzes traditional travel and accommodation expenses for lawmakers and makes proposals for cost reduction through digital meetings.

[0060] The server reviews and reduces unnecessary expense items.

[0061] Cloud Access Methods

[0062] Users can access the cloud server via their devices and check the progress of policies and proposals in real time, which will increase the transparency of the policy-making process and boost public trust.

[0063] Examples:

[0064] Users can view the latest policy proposals using a web browser or a dedicated app.

[0065] Users have the ability to directly input their opinions and feedback on policies.

[0066] This system will dramatically improve the speed and transparency of policy formulation and the accuracy of budget management, enabling efficient policy implementation.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] The server collects data related to politics, economics, education, childcare, culture, science, and law from around the world, from public databases, research papers, news articles, etc. The server uses APIs to access various databases and periodically retrieve the latest information.

[0070] Step 2:

[0071] The server pre-processes the collected data, removing incomplete data and noise and normalizing the data to make it easier to analyze. During this process, necessary data is supplemented and organized as time series data or category data.

[0072] Step 3:

[0073] The server analyzes the pre-processed data, using machine learning algorithms and statistical analysis techniques to uncover important trends and patterns in the data. Specifically, it performs regression analysis to examine the impact of education investment on economic growth, and clusters data to group together data with similar policy effects.

[0074] Step 4:

[0075] The server performs scenario simulations based on the analysis results, creating hypothetical policy scenarios and predicting their effects and impacts. As a result of the simulations, optimal policy proposals are automatically generated.

[0076] Step 5:

[0077] The server checks the generated policy proposals against a lobbying database of relevant parties to ensure transparency. It also checks the relationships between each part of the submitted policy proposal and related companies and organizations, and compares them with lobbying records to detect possible irregularities.

[0078] Step 6:

[0079] The server will keep a detailed record of the decision-making process and generate transparency reports that will be accessible to third parties and stored in an audit-ready format.

[0080] Step 7:

[0081] The server automatically manages budgets related to legislators and elections. It analyzes each expense item and identifies expenses that can be reduced. It reviews traditional legislator travel and accommodation expenses and proposes cost reductions, such as transitioning to digital meetings.

[0082] Step 8:

[0083] Users can access the cloud server via their devices to check the progress of policies and proposals in real time. They can also use a web browser or a dedicated app to view the latest policy proposals and directly enter their opinions and feedback as needed.

[0084] This series of steps will dramatically improve the speed and transparency of policy formulation and the accuracy of budget management, resulting in efficient policy implementation.

[0085] Example 1

[0086] 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."

[0087] In modern society, a huge amount of data is generated in a wide range of fields, including politics, economics, education, childcare, culture, science, and law. However, it is difficult to efficiently collect and analyze this data and generate optimal policies. Ensuring transparency in the policy generation process and efficient budget management are also major challenges. Existing systems have difficulty comprehensively processing these complex processes, resulting in delays in policy implementation and the risk of fraud.

[0088] 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.

[0089] In this invention, the server includes: means for periodically collecting data related to politics, economics, education, childcare, culture, science, and law from around the world; means for pre-processing the collected data, removing noise and normalizing the data, and analyzing it; means for conducting scenario simulations based on the analysis results and automatically generating optimal policies; means for confirming the transparency of the generated policy proposals and comparing them with databases of relevant parties; means for automatically managing budgets related to lawmakers and elections and extracting expenses that can be reduced; and means for users to access the system on the cloud and check policy progress and proposals in real time. This enables efficient data collection and analysis, ensures transparency in policy generation, and improves the efficiency of budget management.

[0090] "Data Collection Tools" refers to a function that periodically collects data related to politics, economics, education, childcare, culture, science, and law from public databases, research papers, news articles, etc. from around the world.

[0091] "Data analysis means" refers to a function that pre-processes collected data, removes noise, normalizes data, and analyzes it using machine learning algorithms and statistical methods.

[0092] "Scenario simulation means" is a function that creates hypothetical policy scenarios based on the results of data analysis and conducts simulation experiments to evaluate their effects and impacts.

[0093] The "policy generation means" is a function that automatically generates optimal policies based on the results of scenario simulations.

[0094] The "transparency verification means" is a function that verifies the transparency of generated policy proposals by comparing them with the databases of relevant parties in order to prevent fraud and collusion.

[0095] "Budget management tools" are functions that automatically manage budgets related to legislators and elections and extract expenses that can be reduced.

[0096] "Cloud access means" is a function that allows users to access a system on the cloud via their terminal and check policy progress and proposals in real time.

[0097] This invention provides a system for collecting, analyzing, policy-making, transparency checks, budget management, and cloud accessing data related to politics, economics, education, childcare, culture, science, and law from around the world. This system aims to speed up policy-making, prevent fraud, efficiently manage budgets, and improve transparency.

[0098] Data collection

[0099] The server periodically collects data related to politics, economics, education, childcare, culture, science, law, etc. from public databases, research papers, news articles, etc. For example, it uses the World Bank's API to obtain economic statistics and stores them in the system. In addition, it uses web crawling technology (e.g., Beautiful Soup) to download the latest research papers and news articles and store them in the database.

[0100] Data analysis

[0101] The server preprocesses the collected data (e.g., noise removal, data normalization) and analyzes it using machine learning algorithms and statistical analysis, using libraries such as Pandas and Scikit-learn. For example, it performs regression analysis to investigate the impact of education investment on economic growth. It also performs clustering to group data with similar policy effects.

[0102] policy generation

[0103] The server performs scenario simulations based on the analysis results and automatically generates optimal policies. Scenario simulations create different policy scenarios and evaluate their effects and impacts. For example, different education policy scenarios can be simulated to identify the one that contributes most to economic growth. The optimal policy proposals are compiled in document form and submitted to the relevant parties.

[0104] Transparency confirmation

[0105] The server checks the transparency of the generated policy proposals and compares them with relevant databases. To prevent corruption and collusion, it checks the relationships with companies and organizations and compares them with lobbying records. For example, it checks the relationships with relevant companies and organizations for each part of the submitted policy proposal.

[0106] Budget Management

[0107] The server automatically manages budgets related to legislators and elections, extracting expenses that can be reduced. This reduces wasteful spending and enables efficient budget management. For example, it analyzes traditional legislators' travel and accommodation expenses and proposes cost reductions through digital meetings.

[0108] Cloud Access

[0109] Users can access the cloud server via their devices and check the progress of policies and proposals in real time. This increases transparency in the policy-making process and strengthens public trust. Users can view the latest policy proposals using a web browser or a dedicated app, and can directly enter their opinions on policies using the feedback function.

[0110] Examples and prompts

[0111] A specific example would be the analysis and transparency check of education policies.

[0112] Examples:

[0113] 1. The server collects economic statistical data on education from the World Bank database via an API.

[0114] 2. The server analyzes the collected data and uses regression analysis to investigate the impact of educational investment on economic growth.

[0115] 3. Based on the analysis results, the server simulates multiple educational policy scenarios and generates the optimal policy.

[0116] 4. The server investigates the relationships of related companies and organizations with the generated educational policy proposals using transparency verification measures.

[0117] Example prompt sentence:

[0118] "How can I conduct a regression analysis using the latest World Bank data on the relationship between education investment and economic growth, create optimal education policy scenarios based on the results, and ensure the transparency of the policy proposals generated to eliminate the possibility of fraud?"

[0119] The system enables efficient data collection and analysis, ensures transparency in policy development, streamlines budget management, and allows users to monitor progress in real time.

[0120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0121] Step 1:

[0122] Data collection

[0123] The server periodically collects data related to politics, economics, education, childcare, culture, science, law, etc. from around the world. Input data is obtained from public databases (e.g., the World Bank), research papers, news articles, etc. Specifically, the server uses an API to request economic statistical data from the World Bank and stores the received data. It also uses web crawling technology (e.g., Beautiful Soup) to download research papers and news articles and store them in a database. As output, the collected raw data is stored in a database.

[0124] Step 2:

[0125] Data Preprocessing

[0126] The server pre-processes the collected data. The input data is the raw data collected in step 1. Specifically, the server converts it into a data frame using the Pandas library, removes inconsistencies and missing values, and normalizes and denoises the data. The output is a clean, normalized dataset.

[0127] Step 3:

[0128] Data analysis

[0129] The server analyzes the preprocessed data. The input data is the dataset preprocessed in step 2. Specifically, the server uses the Scikit-learn library to run machine learning algorithms (e.g., regression analysis and clustering). For example, regression analysis is used to investigate the impact of educational investment on economic growth. The output is trends and patterns as analytical results, as well as the results of various models.

[0130] Step 4:

[0131] policy generation

[0132] The server performs scenario simulations based on the analysis results and generates optimal policies. The input data are the analysis results obtained in step 3. Specifically, the server uses a scenario simulation tool to generate hypothetical policy scenarios and evaluate their effects and impacts. For example, it simulates different education policy scenarios to identify the policy that contributes most to economic growth. The output is an optimal policy proposal.

[0133] Step 5:

[0134] Transparency confirmation

[0135] The server checks the transparency of the generated policy proposal. The input data is the policy proposal generated in step 4. Specifically, the server checks the data against the databases of relevant parties to check for possible fraud or collusion. For example, it investigates relationships with companies and organizations and compares them with lobbying records. The output is the transparency check result, and a warning is issued if there is any possibility of fraud.

[0136] Step 6:

[0137] Budget Management

[0138] The server automatically manages budgets related to legislators and elections, extracting expenses that can be reduced. The input data is past budget data. Specifically, the server analyzes legislators' past travel and accommodation expenses, etc., and proposes cost reductions through digital meetings. It also reviews unnecessary expense items and generates reduction proposals. The output is an efficient budget proposal.

[0139] Step 7:

[0140] Cloud Access

[0141] Users access the cloud server via their devices and check policy progress and proposals in real time. The input data is policy information stored on the cloud. Specifically, users launch a web browser or a dedicated app, enter their authentication information, and log in. They can then check the latest policy information and progress on the dashboard screen. They can also use the feedback function to enter their opinions and proposals regarding policies. The output is policy information and feedback updated in real time.

[0142] (Application example 1)

[0143] 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."

[0144] Modern society requires efficient collection and analysis of data related to politics, economy, education, childcare, culture, science, and law from around the world, ensuring transparency and improving budget management. However, there is a lack of systems that can systematically collect and analyze this data and efficiently generate policies, verify transparency, and manage budgets. Similar challenges exist in factory operations, where it is difficult to utilize data from sensors and equipment to optimize manufacturing processes, prevent fraud, and improve budget management efficiency.

[0145] 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.

[0146] In this invention, the server includes: means for collecting data related to politics, economics, education, childcare, culture, science, and law from around the world; means for analyzing the collected data and discovering important trends and patterns; means for conducting scenario simulations based on the analysis results to generate optimal policies; means for verifying the transparency of the generated policy proposals and comparing them with lobby databases of relevant parties; means for automatically managing budgets related to legislators and elections and extracting cost savings; means for users to access the cloud server and check policy progress and proposals in real time; means for collecting data from sensors and devices in factories and analyzing the data to optimize manufacturing processes; means for conducting simulations of manufacturing processes and generating optimal manufacturing plans; means for verifying the transparency of manufacturing plans and preventing fraud; and means for automatically managing manufacturing-related budgets and proposing cost savings. This enables more efficient and transparent policy generation in all fields, including politics, economics, and education, as well as optimizing manufacturing processes, preventing fraud, and streamlining budget management in factory operations.

[0147] "Collection tools" are devices and processes for systematically collecting data from sensors and equipment in politics, economics, education, childcare, culture, science, law, and factories around the world.

[0148] "Analysis tools" are techniques and methods for analyzing collected data and discovering trends and patterns in the data.

[0149] "Scenario simulation" is a method for generating hypothetical scenarios based on analysis results and evaluating the effects and impacts of those scenarios.

[0150] A "policy generator" is an algorithm and process for creating optimal policies from the results of scenario simulations.

[0151] "Transparency verification means" refers to a mechanism or method for verifying the transparency of generated policy proposals and manufacturing plans and preventing fraudulent activities.

[0152] "Budget control tools" are technologies and processes for automatically managing legislative and election-related budgets and manufacturing-related budgets and extracting expenses and costs that can be reduced.

[0153] "Cloud access means" refers to the technology and process that allows users to access a server on the cloud via the Internet and check policies, the progress of manufacturing processes, and proposals in real time.

[0154] This invention is a comprehensive system that systematically collects and analyzes data related to politics, economics, education, childcare, culture, science, and law from around the world, as well as data from sensors and equipment in factories, to generate policies, optimize manufacturing processes, prevent fraud, and manage budgets.

[0155] The server first uses APIs and web crawling technology to collect a wide range of data from public databases, news articles, research papers, etc. For example, it can retrieve economic statistics from the World Bank database or download the latest research papers. It can also collect real-time data from sensors and equipment in factories.

[0156] The collected data undergoes preprocessing such as noise removal and data normalization, and is then analyzed using machine learning algorithms and statistical analysis. Specifically, regression analysis and clustering are used to discover important trends and patterns. Regression analysis is used to investigate the impact of education investment on economic growth, and data with similar policy effects are grouped together.

[0157] Based on the analysis results, the server runs scenario simulations, which evaluate the effects of different policies and manufacturing scenarios in a virtual environment and generate optimal policies and manufacturing plans. Scenario simulations are conducted to select the optimal training policies, and simulations are conducted to select the optimal manufacturing plans.

[0158] The policy proposals and production plans generated are cross-checked with relevant databases to ensure transparency. Each part of the policy proposal is checked for ties to relevant companies and organizations and cross-checked with lobbying records to prevent corruption and collusion.

[0159] The server also automatically manages budgets related to legislators and elections, as well as manufacturing, and identifies expenses and costs that can be reduced. For example, it can analyze legislators' travel and accommodation expenses and propose cost reductions through digital meetings.

[0160] Users can access the cloud server using a web browser or a dedicated app to check the progress of policies, manufacturing process status, and proposals in real time. They can also directly input their opinions and feedback on policies.

[0161] The implementation of this system uses the following hardware and software:

[0162] Hardware: Factory sensors, smartphones, tablets, robotic arms

[0163] Software: Python environment, Pandas, NumPy, scikit-learn, API access

[0164] For example, an application used on a factory production line collects data in real time from all the equipment in the factory and sends it to a server on the cloud. The data is analyzed, and suggestions for optimizing the manufacturing process and reducing costs are sent to the user's smartphone or tablet.

[0165] Example prompts to input to a generative AI model:

[0166] "Design an integrated manufacturing management application for factory robots. This application will collect data from sensors and robots, perform data analysis, optimize the manufacturing process, ensure transparency, and manage budgets. Specific functions will include data collection and pre-processing, data analysis using machine learning, scenario simulation, transparency, and budget management."

[0167] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0168] Step 1:

[0169] The server collects data from public databases, news articles, and research papers around the world. Specifically, it uses APIs to retrieve economic statistics from the World Bank database and web crawling technology to download the latest research papers. It also collects data in real time from sensors and equipment within the factory. Inputs include data from APIs and sensors, and outputs are the collected data.

[0170] Step 2:

[0171] The server preprocesses the collected data. Preprocessing includes noise removal and data normalization, such as imputing missing values ​​and removing outliers. The input includes the raw data collected in step 1, and the output is the preprocessed, clean data.

[0172] Step 3:

[0173] The server analyzes the preprocessed data. This analysis uses machine learning algorithms and statistical analysis. For example, regression analysis can be used to investigate the impact of education investment on economic growth, or clustering can be used to group data with similar policy effects. The input includes the preprocessed data, and the output is trends and patterns resulting from the analysis.

[0174] Step 4:

[0175] The server performs scenario simulations based on the analysis results. It virtually generates different policy and manufacturing scenarios and evaluates their effects and impacts. For example, it simulates different education policy scenarios to identify the optimal one. The input includes the analysis results, and the output is the evaluation results of the scenarios.

[0176] Step 5:

[0177] The server checks the transparency of the generated policy proposals and manufacturing plans, checking them against lobby databases and other related parties to check for signs of fraud or collusion. The inputs include the generated scenarios and information from related databases, and the output is a transparency check report.

[0178] Step 6:

[0179] The server automatically manages budgets related to legislators, elections, and manufacturing. It analyzes historical budget data to identify expenses and costs that can be reduced. For example, it analyzes historical legislators' travel and accommodation expenses and proposes cost reductions through digital meetings. The input includes historical budget data, and the output is budget reduction proposals.

[0180] Step 7:

[0181] Users can access the cloud server using a web browser or a dedicated app to check the progress and suggestions of policies and manufacturing processes in real time. They can also directly input their opinions and feedback on policies. The input includes the user's access information and feedback, and the output is a display of real-time progress and suggestions.

[0182] This system will systematically collect and analyze data from around the world in fields such as politics, economics, and education, making it possible to improve the efficiency and transparency of policymaking.It will also help optimize manufacturing processes, prevent fraud, and streamline budget management in factory operations.

[0183] 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.

[0184] This invention provides a comprehensive system that combines data collection and analysis related to politics, economics, education, childcare, culture, science, and law from around the world, policy generation, transparency confirmation, budget management, cloud access, and an emotion engine that recognizes user emotions. This system has the functions to speed up policymaking, prevent fraud, efficiently manage budgets, improve transparency, and generate policies that reflect user emotions.

[0185] Each function of the present invention will now be described in detail.

[0186] Data collection methods

[0187] The server periodically collects data related to politics, economics, education, childcare, culture, science, law, and other areas from public databases, research papers, news articles, etc. This information includes a wide range of data, such as economic statistics, educational achievements, and scientific research progress.

[0188] Examples:

[0189] The server uses APIs to retrieve economic statistics from the World Bank database and store them in the system.

[0190] The server uses web crawling technology to download the latest research papers and store them in a database.

[0191] Data Analysis Methods

[0192] The server pre-processes the collected data (e.g., noise removal, data normalization) and analyzes it using machine learning algorithms and statistical analysis to discover significant trends and patterns.

[0193] Examples:

[0194] The server performs a regression analysis to investigate the impact of educational investment on economic growth.

[0195] The server performs clustering to group data that have similar policy effects.

[0196] Policy generation means

[0197] The server then performs scenario simulations based on the analysis results to generate optimal policies. Scenario simulations are mock experiments that create hypothetical policy scenarios and evaluate their effects and impacts.

[0198] Examples:

[0199] The server simulates different education policy scenarios and identifies those that contribute most to economic growth.

[0200] The server compares multiple policy proposals and selects the most appropriate one.

[0201] Transparency confirmation method

[0202] The server checks the generated policy proposals against a lobbying database of relevant parties to ensure transparency. It also checks the relationships between each part of the submitted policy proposal and related companies and organizations, and compares them with lobbying records to detect possible irregularities.

[0203] Examples:

[0204] The server investigates the ties between each submitted policy proposal and the relevant companies and organizations.

[0205] The server checks the lobbying records to detect possible fraud.

[0206] Budget Control Tools

[0207] The server automatically manages budgets related to legislators and elections and extracts expenses that can be reduced. This function reduces wasteful spending and enables efficient budget management.

[0208] Examples:

[0209] The server analyzes traditional travel and accommodation expenses for lawmakers and makes proposals for cost reduction through digital meetings.

[0210] The server reviews and reduces unnecessary expense items.

[0211] Cloud Access Methods

[0212] Users can access the cloud server via their devices and check the progress of policies and proposals in real time, which will increase the transparency of the policy-making process and boost public trust.

[0213] Examples:

[0214] Users can view the latest policy proposals using a web browser or a dedicated app.

[0215] Users have the ability to directly input their opinions and feedback on policies.

[0216] Emotion Engine

[0217] The server is equipped with an emotion engine that recognizes the user's emotions, analyzes the user's emotions based on the collected data, and reflects them in policy proposals.

[0218] Examples:

[0219] As users view policy proposals, the server collects their reactions using cameras and microphones and performs sentiment analysis.

[0220] The server immediately improves the content and presentation of policy proposals based on the results of user sentiment analysis.

[0221] Utilizing emotional feedback

[0222] The server uses an emotion engine to collect and analyze users' reactions to policy proposals in real time, allowing for a quantitative evaluation of the level of support for the policy.

[0223] Examples:

[0224] The server calculates the support and satisfaction of the policy based on the results of the user's sentiment analysis and stores the results in a database.

[0225] The server will use this data as a reference for future policy planning.

[0226] This system will dramatically improve the speed, transparency, and accuracy of policymaking, and will also enable more realistic and popular policy proposals that reflect the feelings of users.

[0227] The processing flow will be explained below.

[0228] Step 1:

[0229] The server collects data related to politics, economics, education, childcare, culture, science, and law from around the world, from public databases, research papers, news articles, etc. It uses APIs to access various databases and periodically retrieve the latest information.

[0230] Step 2:

[0231] The server pre-processes the collected data, specifically removing incomplete data and noise, normalizing the data, supplementing it with necessary data, and organizing it as time series data or categorical data.

[0232] Step 3:

[0233] The server analyzes the pre-processed data, using machine learning algorithms and statistical analysis techniques to uncover significant trends and patterns in the data. For example, it performs regression analysis to analyze the impact of education investment on economic growth.

[0234] Step 4:

[0235] The server performs scenario simulations based on the analysis results, creates hypothetical policy scenarios, and predicts their effects and impacts. As a result of the simulation, optimal policy proposals are automatically generated.

[0236] Step 5:

[0237] The server checks the generated policy proposals against a lobbying database of relevant parties to ensure transparency. It also checks the relationships between each part of the submitted policy proposal and related companies and organizations, and compares them with lobbying records to detect possible irregularities.

[0238] Step 6:

[0239] The server will keep a detailed record of the decision-making process and generate transparency reports that will be accessible to third parties and stored in an audit-ready format.

[0240] Step 7:

[0241] The server automatically manages budgets related to legislators and elections. It analyzes each expense item and identifies expenses that can be reduced. It reviews traditional legislator travel and accommodation expenses and proposes cost reductions, such as transitioning to digital meetings.

[0242] Step 8:

[0243] Users can access the cloud server via their devices to check the progress of policies and proposals in real time. They can also use a web browser or a dedicated app to view the latest policy proposals and directly enter their opinions and feedback as needed.

[0244] Step 9:

[0245] The server analyzes users' reactions using an emotion engine to recognize their emotions. Specifically, it collects emotional data using a camera and microphone while users are viewing policy proposals, and performs emotion analysis in real time.

[0246] Step 10:

[0247] The server improves policy proposals based on the results of sentiment analysis. It analyzes user reactions and instantly adjusts policy content and presentation methods. The results of this analysis are saved as reference data for future policy formulation.

[0248] This series of steps will improve the speed and transparency of policymaking, the accuracy of budget management, and lead to realistic and supportable policy proposals that reflect user sentiment.

[0249] Example 2

[0250] 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."

[0251] Conventional policymaking systems have limitations in terms of policy transparency, efficient budget management, and the ability to reflect user opinions. Furthermore, they lack centralized data collection and analysis, resulting in a lack of speed and accuracy in policy decisions. Furthermore, they lack the functionality to collect and reflect user sentiment and opinions in real time, making it difficult to generate realistic and popular policies.

[0252] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting data related to politics, economy, education, childcare, culture, science, and law from around the world; means for pre-processing and analyzing the collected data to discover important trends and patterns; means for conducting scenario simulations based on the analysis results to generate optimal policies; means for verifying the transparency of the generated policy proposals and comparing them with lobby databases of relevant parties; means for automatically managing budgets related to legislators and elections and extracting expenses that can be reduced; means for users to access a server on the cloud and check policy progress and proposals in real time; an emotion engine that recognizes and analyzes user emotions and reflects them in policy proposals; and means for collecting and analyzing user emotions regarding policy proposals using the emotion engine and utilizing them in future policy planning. This enables faster policy planning, improved transparency, more efficient budget management, and the generation of realistic and supportable policies that reflect user emotions.

[0253] "Global political, economic, educational, childcare, cultural, scientific and legal data" refers to information on various areas around the world collected from governments, economic institutions, educational institutions, childcare-related organizations, cultural institutions, scientific research institutions, legal institutions, etc.

[0254] "Means of collecting data" refers to a method of automatically obtaining information from public data on the Internet or dedicated databases using web crawling technology or API access.

[0255] "Preprocessing" refers to the process of performing preprocessing such as noise removal, normalization, and missing value completion on collected data, and is a preparatory step to improve the accuracy of analysis.

[0256] "Means of analysis to discover important trends and patterns" refers to techniques that use machine learning algorithms and statistical analysis methods to extract significant information from large amounts of data and reveal trends and patterns.

[0257] "A means of conducting scenario simulations and generating optimal policies" is a technology that sets up multiple hypothetical policy scenarios, evaluates and compares their effects and impacts, and automatically generates the most effective policies.

[0258] "Means to verify transparency and compare with lobbying databases of relevant parties" refers to a method of making the background and impact of generated policy proposals transparent and comparing them with existing lobbying databases to detect irregularities and bias.

[0259] "Means for automatically managing budgets and extracting expenses that can be reduced" refers to technology that centrally manages budgets related to lawmakers and elections through a system, uses digital analysis to identify wasteful spending, and proposes reductions.

[0260] "A means of accessing a server on the cloud and checking policy progress and proposals in real time" is a function that allows users to connect to the cloud via the Internet and instantly view and check the latest policy information and proposals.

[0261] The "emotion engine" is a system that analyzes a user's facial expressions and voice data to assess their emotional state and incorporates this information into the policy generation process.

[0262] "Means of collecting and analyzing emotions and utilizing them in future policy formulation" refers to a technology that uses an emotion engine to collect and analyze users' reactions to policy proposals, stores the results in a database, and reflects them in the generation of future policies.

[0263] This invention is a comprehensive system that collects and analyzes data related to politics, economy, education, childcare, culture, science, and law from around the world, generates optimal policies, checks transparency, streamlines budget management, and reflects user sentiment. The system of this invention is composed of three main entities: a server, a terminal, and a user.

[0264] Data collection methods

[0265] The server periodically collects data related to politics, economy, education, childcare, culture, science, and law from around the world using web crawlers, API access technologies (e.g., World Bank API), and downloads from databases. Examples of data collected include economic statistics, educational achievements, and scientific research progress.

[0266] Examples:

[0267] The server uses the World Bank API to obtain GDP data for each country.

[0268] The server uses web crawling techniques to download new research papers and store them in a database.

[0269] Data Analysis Methods

[0270] The server pre-processes the collected data (denoising, normalising the data, etc.) and then analyses it using machine learning algorithms and statistical analysis techniques, including regression analysis and clustering, to discover significant trends and patterns.

[0271] Examples:

[0272] The server uses regression analysis to investigate the impact of educational investment on economic growth.

[0273] The server performs clustering to group data that have similar policy effects.

[0274] Policy generation means

[0275] The server performs scenario simulation based on the analysis results to generate optimal policies. Scenario simulation is a method of setting up multiple hypothetical scenarios and evaluating and comparing their effects.

[0276] Examples:

[0277] The server hypothesizes different scenarios for education policy and evaluates the impact of each scenario on economic growth.

[0278] The server generates the most effective policy based on the simulation results.

[0279] Transparency confirmation method

[0280] The server checks the transparency of the generated policy proposals and checks them against a lobby database to detect possible fraud.

[0281] Examples:

[0282] The server checks whether the policy proposals are biased toward any particular company or organization.

[0283] The server checks the lobbying records to detect possible fraud.

[0284] Budget Control Tools

[0285] The server automatically manages budgets related to legislators and elections and identifies expenses that can be reduced.

[0286] Examples:

[0287] The server analyzes past travel and accommodation expenses for lawmakers and makes proposals for cost reduction through digital meetings.

[0288] The server reviews and reduces unnecessary expense items.

[0289] Cloud Access Methods

[0290] Users can access the cloud server using their devices and check policy progress and proposals in real time.

[0291] Examples:

[0292] Users can view the latest policy proposals using a web browser or a dedicated app.

[0293] Users can directly input their opinions and feedback on policies.

[0294] Emotion Engine

[0295] The server is equipped with an emotion engine that recognizes the user's emotions by analyzing the user's facial expressions and voice data to evaluate their emotional state.

[0296] Examples:

[0297] The server uses cameras and microphones to collect users' reactions as they view policy proposals.

[0298] The server refines the content of policy proposals based on the results of the sentiment analysis.

[0299] Utilizing emotional feedback

[0300] The server uses an emotion engine to collect and analyze users' emotions regarding policy proposals and use them in future policy planning.

[0301] Examples:

[0302] The server aggregates users' emotional data and evaluates the support and satisfaction of the policy.

[0303] The server uses the evaluation results in the next policy generation.

[0304] Prompt Sentence Examples

[0305] "Analyze the effects of education policies and see the correlation with economic growth."

[0306] "Perform user sentiment analysis on the latest policy proposals and refine them based on their feedback."

[0307] In this way, the system of the present invention realizes rapid policy formulation, improved transparency, more efficient budget management, and the generation of realistic and supportable policies that reflect the feelings of users.

[0308] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0309] Step 1:

[0310] The server collects data related to politics, economics, education, childcare, culture, science, and law from around the world using web crawling technology and API access (e.g., World Bank API). The input is various public databases and news sites, and the output is the collected dataset. Specifically, it periodically sends API requests to retrieve and store the latest data.

[0311] Step 2:

[0312] The server pre-processes the collected data. The input is the raw data collected in step 1, and the output is clean data that has been normalized and noise removed. Specific operations include detecting and removing outliers, filling in missing values, and standardizing the data format.

[0313] Step 3:

[0314] The server uses the cleaned data to run machine learning algorithms and statistical analysis. The input is the cleaned data processed in step 2, and the output is the analysis results (discovery of trends and patterns). As a specific example, regression analysis is used to analyze the impact of education investment on economic growth, and clustering is used to group data with similar policy effects.

[0315] Step 4:

[0316] The server runs a scenario simulation based on the analysis results and generates optimal policies. The input is the analysis results from Step 3, and the output is an optimal policy proposal. Specifically, multiple hypothetical scenarios are set up, and the most effective policy is selected by evaluating and comparing their effects.

[0317] Step 5:

[0318] The server compares the generated policy proposal with the lobby database to verify its transparency. The input is the generated policy proposal, and the output is the transparency evaluation result. Specifically, it checks whether the proposed policy proposal is biased toward specific companies or organizations.

[0319] Step 6:

[0320] The server automatically manages budgets related to legislators and elections and extracts expenses that can be reduced. The input is budget data related to legislators and elections, and the output is reduction proposals. As a specific example, it analyzes data on past legislator travel and accommodation expenses and proposes cost reductions through digital meetings.

[0321] Step 7:

[0322] Users use their devices to access the cloud server and check the progress and proposals of policies in real time. The input is a request to access the cloud database, and the output is the latest policy information. Specifically, users use a web browser or a dedicated app to view the latest policy proposals and provide feedback.

[0323] Step 8:

[0324] The server activates an emotion engine that recognizes the user's emotions and analyzes the user's facial expressions and voice data. The input is the user's reaction data, and the output is the emotion analysis results. As a specific example, the server analyzes the user's facial expressions and voice tone in real time while viewing policy proposals to evaluate their emotional state.

[0325] Step 9:

[0326] The server improves policy proposals based on the results of user sentiment analysis and uses them in future policy planning. The input is the sentiment analysis results, and the output is improved policy proposals and data to be used in the next policy generation. Specifically, it automatically adjusts the content and presentation method to elicit a positive response from users, and stores the evaluation results in a database.

[0327] Through these processing steps, rapid policy formulation, increased transparency, efficient budget management, and the generation of realistic and supportable policies that reflect user sentiment are realized.

[0328] (Application example 2)

[0329] 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."

[0330] In conventional policymaking systems, data collection and analysis are localized, making it difficult to comprehensively analyze a wide range of data from around the world. Furthermore, the transparency of the policies generated and the efficiency of budget management are insufficient, and there are also insufficient mechanisms to prevent fraud and reflect public sentiment in the actual policy generation. Furthermore, there is no optimized method for generating policies in terms of security measures, and improvements in overall reliability and transparency are needed.

[0331] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting data related to politics, economics, education, childcare, culture, science, and law from around the world; means for analyzing the collected data and discovering important trends and patterns; means for conducting scenario simulations based on the analysis results to generate optimal policies; means for verifying the transparency of the generated policy proposals and comparing them with lobby databases of relevant parties; means for automatically managing budgets related to legislators and elections and extracting cost savings; means for users to access a cloud-based server and check policy progress and proposals in real time; means for recognizing user sentiment and improving policy proposals based on the analysis results; and means for collecting security-related data and generating security policies. This dramatically improves the speed, transparency, and accuracy of policymaking, enabling more realistic and supportable policy proposals that reflect user sentiment. Furthermore, generating optimal policies for security measures also improves overall reliability and transparency.

[0332] "Political, economic, educational, childcare, cultural, scientific and legal data" refers to the totality of information collected from governments, economic institutions, educational institutions, childcare support groups, cultural institutions, scientific research institutions, legal organizations and other sources around the world.

[0333] "Means of collecting data" refers to systems that automatically collect data from specific sources using APIs, web crawling, scraping technologies, etc.

[0334] "Methods for discovering significant trends and patterns" refers to techniques that use machine learning algorithms and statistical analysis to extract meaningful trends and patterns from large amounts of data.

[0335] "Means for performing scenario simulation" refers to algorithms and software that quantitatively evaluate hypothetical policy scenarios based on collected data and analysis results and simulate their effects.

[0336] "Means to confirm the transparency of the generated policy proposals" refers to the process of comparing the generated policy proposals with lobby databases, etc., to verify the possibility of fraud.

[0337] "Automated budget management tools" refers to software and algorithms that digitally manage legislative and election-related budgets, calculate efficiency, and reduce waste.

[0338] "Means for accessing servers in the cloud" refers to the interfaces and protocols for accessing and using data and functions stored in the cloud through an Internet connection.

[0339] "Means for recognizing user emotions" refers to an emotion engine that uses sensors such as cameras and microphones to analyze emotions from the user's facial expressions and voice.

[0340] "Means of collecting security-related data" refers to systems that collect security-related information such as cyberattack trends, vulnerability information, and changes in laws.

[0341] "Means for generating security policies" refers to a system that analyzes collected security-related data and automatically proposes optimal security measures and policies.

[0342] "Means for improving policy proposals that reflect user emotions" refers to a function for dynamically modifying and improving the content of policy proposals based on user emotion data analyzed by the emotion engine.

[0343] This is a comprehensive system that collects and analyzes data related to politics, economy, education, childcare, culture, science, and law from around the world, generates optimal policies, ensures transparency, and efficiently manages budgets. It also has the ability to recognize users' emotions and improve policy proposals based on the analysis results.

[0344] The server contains the following main means:

[0345] 1. Data Collection Methods

[0346] The server uses APIs and web crawling technology to collect data related to politics, economics, education, childcare, culture, science, and law from around the world. For example, you can use APIs to obtain economic statistics or web crawling technology to download the latest research papers.

[0347] 2. Data analysis methods

[0348] The collected data is then standardized and analyzed using machine learning algorithms and statistical analysis to uncover important trends and patterns, allowing, for example, regression analysis to explore the impact of education investment on economic growth.

[0349] 3. Policy generation instruments

[0350] The server then performs scenario simulations based on the analysis results to generate optimal policies. This involves using an algorithm to generate multiple policy proposals and simulate their effects. For example, it simulates different education policy scenarios to identify the one that contributes most to economic growth.

[0351] 4. Transparency confirmation method

[0352] The generated policy proposals are checked against a lobby database to ensure transparency about the policy proposals, investigate ties with relevant companies and organizations, and detect any potential irregularities.

[0353] 5. Budgetary Control Tools

[0354] The server automatically manages budgets related to legislators and elections, using algorithms that analyze past expenditure data and identify potential cost savings, such as travel and accommodation costs that can be reduced by implementing digital meetings.

[0355] 6. Cloud Access Methods

[0356] Users can access the cloud server through a web browser or a dedicated app to check the progress and proposals of policies in real time, and can also directly enter their opinions and feedback on policies.

[0357] 7. Emotion recognition means

[0358] It is equipped with an emotion engine that recognizes the user's emotions, and uses a camera and microphone to analyze the user's facial expressions and voice, allowing it to dynamically modify and improve policy proposals based on the user's emotions.

[0359] 8. Security Policy Generation Method

[0360] The server collects and analyzes security-related data (e.g., cyberattack trends, vulnerability information, legal changes, etc.), and generates optimal security policies based on the results, ensuring transparency.

[0361] Specific examples of the hardware and software used include machine learning algorithms implemented in the Python language, RESTful APIs used for data collection, Pandas and Scikit-learn used for data analysis, and AWS (registered trademark), Azure (registered trademark), and Google (registered trademark) Cloud Platform as cloud servers.

[0362] As a concrete example, the following prompt sentence could be input to a generative AI model:

[0363] "Please build a system that collects and analyzes security data from around the world, generates optimal security policies, ensures transparency, and manages budgets. Also, please add a function to analyze user sentiment and reflect it in policy proposals."

[0364] This will dramatically improve the speed, transparency, and accuracy of policymaking, and will enable more realistic and supportable policy proposals that reflect user sentiment. It will also improve overall reliability and transparency by generating optimal policies for security measures.

[0365] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0366] Step 1:

[0367] The server uses APIs and web crawling technology to collect data related to politics, economics, education, childcare, culture, science, and law from around the world. Specifically, it uses APIs to obtain economic statistics and web crawling technology to collect the latest research papers. The input data is public data and web pages related to each field, and the output data is structured data containing that information. Data collection is carried out regularly to keep the content up to date.

[0368] Step 2:

[0369] The server pre-processes the collected data. This involves standardizing the data format and removing missing values ​​and noise. For example, data provided in different formats is standardized into CSV format and missing values ​​are imputed. The input data is the raw data collected in step 1, and the output data is the normalized, clean data.

[0370] Step 3:

[0371] The server analyzes the pre-processed data. This analysis uses machine learning algorithms and statistical analysis to discover important trends and patterns. Specifically, it performs regression analysis and clustering to examine the impact of education investment on economic growth. The input data is the pre-processed data, and the output data is the analysis results and trend information.

[0372] Step 4:

[0373] The server performs a scenario simulation based on the analysis results. In the scenario simulation, different policy scenarios are generated and their effects are evaluated. For example, multiple education policy scenarios are generated and it is determined which scenario will contribute most to economic growth. The input data are the analysis results obtained in Step 3, and the output data are the results of the simulation.

[0374] Step 5:

[0375] The server checks the transparency of the generated policy proposals. Specifically, it checks the policy proposals against the lobby database to detect possible fraud. The input data is the policy proposals resulting from the simulation, and the output data is the policy proposals whose transparency has been confirmed.

[0376] Step 6:

[0377] The server automatically manages budgets related to legislators and elections. It analyzes past expenditure data and extracts cost items that can be reduced. Specifically, it suggests travel and accommodation costs that can be reduced by introducing digital meetings. The input data is budget data related to legislators and elections, and the output data is a list of cost items that can be reduced.

[0378] Step 7:

[0379] Users with devices can access the cloud server using a web browser or a dedicated app to check policy progress and proposals in real time. They can also directly input their opinions and feedback on policies. The input data is the user's access information and feedback, and the output data is real-time policy progress information.

[0380] Step 8:

[0381] The server uses an emotion engine to recognize the user's emotions. It analyzes emotions using a camera and microphone and reflects the feedback in policy proposals. Specifically, it analyzes the user's facial expressions and voice to calculate the policy's support and satisfaction. The input data is the user's facial expressions and voice data, and the output data is feedback information based on the emotion analysis.

[0382] Step 9:

[0383] The server collects and analyzes security-related data to generate optimal security policies. Specifically, it collects cyber-attack trends and vulnerability information, and formulates security policies based on the results. The input data is public security-related data, and the output data is the generated security policy.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] [Second embodiment]

[0388] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0389] 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.

[0390] 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).

[0391] 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.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] In the smart glasses 214, 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.

[0399] 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."

[0400] This invention provides a comprehensive system for collecting, analyzing, formulating policies, verifying transparency, managing budgets, and accessing data related to politics, economics, education, childcare, culture, science, and law from around the world. This system has the functions to speed up policymaking, prevent fraud, efficiently manage budgets, and improve transparency.

[0401] Each function of the present invention will now be described in detail.

[0402] Data collection methods

[0403] The server periodically collects data related to politics, economics, education, childcare, culture, science, law, and other areas from public databases, research papers, news articles, etc. This information includes a wide range of data, such as economic statistics, educational achievements, and scientific research progress.

[0404] Examples:

[0405] The server uses APIs to retrieve economic statistics from the World Bank database and store them in the system.

[0406] The server uses web crawling technology to download the latest research papers and store them in a database.

[0407] Data Analysis Methods

[0408] The server pre-processes the collected data (e.g., noise removal, data normalization) and analyzes it using machine learning algorithms and statistical analysis to discover significant trends and patterns.

[0409] Examples:

[0410] The server performs a regression analysis to investigate the impact of educational investment on economic growth.

[0411] The server performs clustering to group data that have similar policy effects.

[0412] Policy generation means

[0413] The server then performs scenario simulations based on the analysis results to generate optimal policies. Scenario simulations are mock experiments that create hypothetical policy scenarios and evaluate their effects and impacts.

[0414] Examples:

[0415] The server simulates different education policy scenarios and identifies those that contribute most to economic growth.

[0416] The server compares multiple policy proposals and selects the most appropriate one.

[0417] Transparency confirmation method

[0418] The server checks the transparency of the generated policy proposals and compares them with lobby databases of relevant parties, etc. This measure is intended to prevent fraud and collusion.

[0419] Examples:

[0420] The server investigates the ties between each submitted policy proposal and the relevant companies and organizations.

[0421] The server checks the lobbying records to detect possible fraud.

[0422] Budget Control Tools

[0423] The server automatically manages budgets related to legislators and elections and extracts expenses that can be reduced. This function reduces wasteful spending and enables efficient budget management.

[0424] Examples:

[0425] The server analyzes traditional travel and accommodation expenses for lawmakers and makes proposals for cost reduction through digital meetings.

[0426] The server reviews and reduces unnecessary expense items.

[0427] Cloud Access Methods

[0428] Users can access the cloud server via their devices and check the progress of policies and proposals in real time, which will increase the transparency of the policy-making process and boost public trust.

[0429] Examples:

[0430] Users can view the latest policy proposals using a web browser or a dedicated app.

[0431] Users have the ability to directly input their opinions and feedback on policies.

[0432] This system will dramatically improve the speed and transparency of policy formulation and the accuracy of budget management, enabling efficient policy implementation.

[0433] The processing flow will be explained below.

[0434] Step 1:

[0435] The server collects data related to politics, economics, education, childcare, culture, science, and law from around the world, from public databases, research papers, news articles, etc. The server uses APIs to access various databases and periodically retrieve the latest information.

[0436] Step 2:

[0437] The server pre-processes the collected data, removing incomplete data and noise and normalizing the data to make it easier to analyze. During this process, necessary data is supplemented and organized as time series data or category data.

[0438] Step 3:

[0439] The server analyzes the pre-processed data, using machine learning algorithms and statistical analysis techniques to uncover important trends and patterns in the data. Specifically, it performs regression analysis to examine the impact of education investment on economic growth, and clusters data to group together data with similar policy effects.

[0440] Step 4:

[0441] The server performs scenario simulations based on the analysis results, creating hypothetical policy scenarios and predicting their effects and impacts. As a result of the simulations, optimal policy proposals are automatically generated.

[0442] Step 5:

[0443] The server checks the generated policy proposals against a lobbying database of relevant parties to ensure transparency. It also checks the relationships between each part of the submitted policy proposal and related companies and organizations, and compares them with lobbying records to detect possible irregularities.

[0444] Step 6:

[0445] The server will keep a detailed record of the decision-making process and generate transparency reports that will be accessible to third parties and stored in an audit-ready format.

[0446] Step 7:

[0447] The server automatically manages budgets related to legislators and elections. It analyzes each expense item and identifies expenses that can be reduced. It reviews traditional legislator travel and accommodation expenses and proposes cost reductions, such as transitioning to digital meetings.

[0448] Step 8:

[0449] Users can access the cloud server via their devices to check the progress of policies and proposals in real time. They can also use a web browser or a dedicated app to view the latest policy proposals and directly enter their opinions and feedback as needed.

[0450] This series of steps will dramatically improve the speed and transparency of policy formulation and the accuracy of budget management, resulting in efficient policy implementation.

[0451] Example 1

[0452] 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."

[0453] In modern society, a huge amount of data is generated in a wide range of fields, including politics, economics, education, childcare, culture, science, and law. However, it is difficult to efficiently collect and analyze this data and generate optimal policies. Ensuring transparency in the policy generation process and efficient budget management are also major challenges. Existing systems have difficulty comprehensively processing these complex processes, resulting in delays in policy implementation and the risk of fraud.

[0454] 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.

[0455] In this invention, the server includes: means for periodically collecting data related to politics, economics, education, childcare, culture, science, and law from around the world; means for pre-processing the collected data, removing noise and normalizing the data, and analyzing it; means for conducting scenario simulations based on the analysis results and automatically generating optimal policies; means for confirming the transparency of the generated policy proposals and comparing them with databases of relevant parties; means for automatically managing budgets related to lawmakers and elections and extracting expenses that can be reduced; and means for users to access the system on the cloud and check policy progress and proposals in real time. This enables efficient data collection and analysis, ensures transparency in policy generation, and improves the efficiency of budget management.

[0456] "Data Collection Tools" refers to a function that periodically collects data related to politics, economics, education, childcare, culture, science, and law from public databases, research papers, news articles, etc. from around the world.

[0457] "Data analysis means" refers to a function that pre-processes collected data, removes noise, normalizes data, and analyzes it using machine learning algorithms and statistical methods.

[0458] "Scenario simulation means" is a function that creates hypothetical policy scenarios based on the results of data analysis and conducts simulation experiments to evaluate their effects and impacts.

[0459] The "policy generation means" is a function that automatically generates optimal policies based on the results of scenario simulations.

[0460] The "transparency verification means" is a function that verifies the transparency of generated policy proposals by comparing them with the databases of relevant parties in order to prevent fraud and collusion.

[0461] "Budget management tools" are functions that automatically manage budgets related to legislators and elections and extract expenses that can be reduced.

[0462] "Cloud access means" is a function that allows users to access a system on the cloud via their terminal and check policy progress and proposals in real time.

[0463] This invention provides a system for collecting, analyzing, policy-making, transparency checks, budget management, and cloud accessing data related to politics, economics, education, childcare, culture, science, and law from around the world. This system aims to speed up policy-making, prevent fraud, efficiently manage budgets, and improve transparency.

[0464] Data collection

[0465] The server periodically collects data related to politics, economics, education, childcare, culture, science, law, etc. from public databases, research papers, news articles, etc. For example, it uses the World Bank's API to obtain economic statistics and stores them in the system. In addition, it uses web crawling technology (e.g., Beautiful Soup) to download the latest research papers and news articles and store them in the database.

[0466] Data analysis

[0467] The server preprocesses the collected data (e.g., noise removal, data normalization) and analyzes it using machine learning algorithms and statistical analysis, using libraries such as Pandas and Scikit-learn. For example, it performs regression analysis to investigate the impact of education investment on economic growth. It also performs clustering to group data with similar policy effects.

[0468] policy generation

[0469] The server performs scenario simulations based on the analysis results and automatically generates optimal policies. Scenario simulations create different policy scenarios and evaluate their effects and impacts. For example, different education policy scenarios can be simulated to identify the one that contributes most to economic growth. The optimal policy proposals are compiled in document form and submitted to the relevant parties.

[0470] Transparency confirmation

[0471] The server checks the transparency of the generated policy proposals and compares them with relevant databases. To prevent corruption and collusion, it checks the relationships with companies and organizations and compares them with lobbying records. For example, it checks the relationships with relevant companies and organizations for each part of the submitted policy proposal.

[0472] Budget Management

[0473] The server automatically manages budgets related to legislators and elections, extracting expenses that can be reduced. This reduces wasteful spending and enables efficient budget management. For example, it analyzes traditional legislators' travel and accommodation expenses and proposes cost reductions through digital meetings.

[0474] Cloud Access

[0475] Users can access the cloud server via their devices and check the progress of policies and proposals in real time. This increases transparency in the policy-making process and strengthens public trust. Users can view the latest policy proposals using a web browser or a dedicated app, and can directly enter their opinions on policies using the feedback function.

[0476] Examples and prompts

[0477] A specific example would be the analysis and transparency check of education policies.

[0478] Examples:

[0479] 1. The server collects economic statistical data on education from the World Bank database via an API.

[0480] 2. The server analyzes the collected data and uses regression analysis to investigate the impact of educational investment on economic growth.

[0481] 3. Based on the analysis results, the server simulates multiple educational policy scenarios and generates the optimal policy.

[0482] 4. The server investigates the relationships of related companies and organizations with the generated educational policy proposals using transparency verification measures.

[0483] Example prompt sentence:

[0484] "How can I conduct a regression analysis using the latest World Bank data on the relationship between education investment and economic growth, create optimal education policy scenarios based on the results, and ensure the transparency of the policy proposals generated to eliminate the possibility of fraud?"

[0485] The system enables efficient data collection and analysis, ensures transparency in policy development, streamlines budget management, and allows users to monitor progress in real time.

[0486] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0487] Step 1:

[0488] Data collection

[0489] The server periodically collects data related to politics, economics, education, childcare, culture, science, law, etc. from around the world. Input data is obtained from public databases (e.g., the World Bank), research papers, news articles, etc. Specifically, the server uses an API to request economic statistical data from the World Bank and stores the received data. It also uses web crawling technology (e.g., Beautiful Soup) to download research papers and news articles and store them in a database. As output, the collected raw data is stored in a database.

[0490] Step 2:

[0491] Data Preprocessing

[0492] The server pre-processes the collected data. The input data is the raw data collected in step 1. Specifically, the server converts it into a data frame using the Pandas library, removes inconsistencies and missing values, and normalizes and denoises the data. The output is a clean, normalized dataset.

[0493] Step 3:

[0494] Data analysis

[0495] The server analyzes the preprocessed data. The input data is the dataset preprocessed in step 2. Specifically, the server uses the Scikit-learn library to run machine learning algorithms (e.g., regression analysis and clustering). For example, regression analysis is used to investigate the impact of educational investment on economic growth. The output is trends and patterns as analytical results, as well as the results of various models.

[0496] Step 4:

[0497] policy generation

[0498] The server performs scenario simulations based on the analysis results and generates optimal policies. The input data are the analysis results obtained in step 3. Specifically, the server uses a scenario simulation tool to generate hypothetical policy scenarios and evaluate their effects and impacts. For example, it simulates different education policy scenarios to identify the policy that contributes most to economic growth. The output is an optimal policy proposal.

[0499] Step 5:

[0500] Transparency confirmation

[0501] The server checks the transparency of the generated policy proposal. The input data is the policy proposal generated in step 4. Specifically, the server checks the data against the databases of relevant parties to check for possible fraud or collusion. For example, it investigates relationships with companies and organizations and compares them with lobbying records. The output is the transparency check result, and a warning is issued if there is any possibility of fraud.

[0502] Step 6:

[0503] Budget Management

[0504] The server automatically manages budgets related to legislators and elections, extracting expenses that can be reduced. The input data is past budget data. Specifically, the server analyzes legislators' past travel and accommodation expenses, etc., and proposes cost reductions through digital meetings. It also reviews unnecessary expense items and generates reduction proposals. The output is an efficient budget proposal.

[0505] Step 7:

[0506] Cloud Access

[0507] Users access the cloud server via their devices and check policy progress and proposals in real time. The input data is policy information stored on the cloud. Specifically, users launch a web browser or a dedicated app, enter their authentication information, and log in. They can then check the latest policy information and progress on the dashboard screen. They can also use the feedback function to enter their opinions and proposals regarding policies. The output is policy information and feedback updated in real time.

[0508] (Application example 1)

[0509] 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."

[0510] Modern society requires efficient collection and analysis of data related to politics, economy, education, childcare, culture, science, and law from around the world, ensuring transparency and improving budget management. However, there is a lack of systems that can systematically collect and analyze this data and efficiently generate policies, verify transparency, and manage budgets. Similar challenges exist in factory operations, where it is difficult to utilize data from sensors and equipment to optimize manufacturing processes, prevent fraud, and improve budget management efficiency.

[0511] 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.

[0512] In this invention, the server includes: means for collecting data related to politics, economics, education, childcare, culture, science, and law from around the world; means for analyzing the collected data and discovering important trends and patterns; means for conducting scenario simulations based on the analysis results to generate optimal policies; means for verifying the transparency of the generated policy proposals and comparing them with lobby databases of relevant parties; means for automatically managing budgets related to legislators and elections and extracting cost savings; means for users to access the cloud server and check policy progress and proposals in real time; means for collecting data from sensors and devices in factories and analyzing the data to optimize manufacturing processes; means for conducting simulations of manufacturing processes and generating optimal manufacturing plans; means for verifying the transparency of manufacturing plans and preventing fraud; and means for automatically managing manufacturing-related budgets and proposing cost savings. This enables more efficient and transparent policy generation in all fields, including politics, economics, and education, as well as optimizing manufacturing processes, preventing fraud, and streamlining budget management in factory operations.

[0513] "Collection tools" are devices and processes for systematically collecting data from sensors and equipment in politics, economics, education, childcare, culture, science, law, and factories around the world.

[0514] "Analysis tools" are techniques and methods for analyzing collected data and discovering trends and patterns in the data.

[0515] "Scenario simulation" is a method for generating hypothetical scenarios based on analysis results and evaluating the effects and impacts of those scenarios.

[0516] A "policy generator" is an algorithm and process for creating optimal policies from the results of scenario simulations.

[0517] "Transparency verification means" refers to a mechanism or method for verifying the transparency of generated policy proposals and manufacturing plans and preventing fraudulent activities.

[0518] "Budget control tools" are technologies and processes for automatically managing legislative and election-related budgets and manufacturing-related budgets and extracting expenses and costs that can be reduced.

[0519] "Cloud access means" refers to the technology and process that allows users to access a server on the cloud via the Internet and check policies, the progress of manufacturing processes, and proposals in real time.

[0520] This invention is a comprehensive system that systematically collects and analyzes data related to politics, economics, education, childcare, culture, science, and law from around the world, as well as data from sensors and equipment in factories, to generate policies, optimize manufacturing processes, prevent fraud, and manage budgets.

[0521] The server first uses APIs and web crawling technology to collect a wide range of data from public databases, news articles, research papers, etc. For example, it can retrieve economic statistics from the World Bank database or download the latest research papers. It can also collect real-time data from sensors and equipment in factories.

[0522] The collected data undergoes preprocessing such as noise removal and data normalization, and is then analyzed using machine learning algorithms and statistical analysis. Specifically, regression analysis and clustering are used to discover important trends and patterns. Regression analysis is used to investigate the impact of education investment on economic growth, and data with similar policy effects are grouped together.

[0523] Based on the analysis results, the server runs scenario simulations, which evaluate the effects of different policies and manufacturing scenarios in a virtual environment and generate optimal policies and manufacturing plans. Scenario simulations are conducted to select the optimal training policies, and simulations are conducted to select the optimal manufacturing plans.

[0524] The policy proposals and production plans generated are cross-checked with relevant databases to ensure transparency. Each part of the policy proposal is checked for ties to relevant companies and organizations and cross-checked with lobbying records to prevent corruption and collusion.

[0525] The server also automatically manages budgets related to legislators and elections, as well as manufacturing, and identifies expenses and costs that can be reduced. For example, it can analyze legislators' travel and accommodation expenses and propose cost reductions through digital meetings.

[0526] Users can access the cloud server using a web browser or a dedicated app to check the progress of policies, manufacturing process status, and proposals in real time. They can also directly input their opinions and feedback on policies.

[0527] The implementation of this system uses the following hardware and software:

[0528] Hardware: Factory sensors, smartphones, tablets, robotic arms

[0529] Software: Python environment, Pandas, NumPy, scikit-learn, API access

[0530] For example, an application used on a factory production line collects data in real time from all the equipment in the factory and sends it to a server on the cloud. The data is analyzed, and suggestions for optimizing the manufacturing process and reducing costs are sent to the user's smartphone or tablet.

[0531] Example prompts to input to a generative AI model:

[0532] "Design an integrated manufacturing management application for factory robots. This application will collect data from sensors and robots, perform data analysis, optimize the manufacturing process, ensure transparency, and manage budgets. Specific functions will include data collection and pre-processing, data analysis using machine learning, scenario simulation, transparency, and budget management."

[0533] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0534] Step 1:

[0535] The server collects data from public databases, news articles, and research papers around the world. Specifically, it uses APIs to retrieve economic statistics from the World Bank database and web crawling technology to download the latest research papers. It also collects data in real time from sensors and equipment within the factory. Inputs include data from APIs and sensors, and outputs are the collected data.

[0536] Step 2:

[0537] The server preprocesses the collected data. Preprocessing includes noise removal and data normalization, such as imputing missing values ​​and removing outliers. The input includes the raw data collected in step 1, and the output is the preprocessed, clean data.

[0538] Step 3:

[0539] The server analyzes the preprocessed data. This analysis uses machine learning algorithms and statistical analysis. For example, regression analysis can be used to investigate the impact of education investment on economic growth, or clustering can be used to group data with similar policy effects. The input includes the preprocessed data, and the output is trends and patterns resulting from the analysis.

[0540] Step 4:

[0541] The server performs scenario simulations based on the analysis results. It virtually generates different policy and manufacturing scenarios and evaluates their effects and impacts. For example, it simulates different education policy scenarios to identify the optimal one. The input includes the analysis results, and the output is the evaluation results of the scenarios.

[0542] Step 5:

[0543] The server checks the transparency of the generated policy proposals and manufacturing plans, checking them against lobby databases and other related parties to check for signs of fraud or collusion. The inputs include the generated scenarios and information from related databases, and the output is a transparency check report.

[0544] Step 6:

[0545] The server automatically manages budgets related to legislators, elections, and manufacturing. It analyzes historical budget data to identify expenses and costs that can be reduced. For example, it analyzes historical legislators' travel and accommodation expenses and proposes cost reductions through digital meetings. The input includes historical budget data, and the output is budget reduction proposals.

[0546] Step 7:

[0547] Users can access the cloud server using a web browser or a dedicated app to check the progress and suggestions of policies and manufacturing processes in real time. They can also directly input their opinions and feedback on policies. The input includes the user's access information and feedback, and the output is a display of real-time progress and suggestions.

[0548] This system will systematically collect and analyze data from around the world in fields such as politics, economics, and education, making it possible to improve the efficiency and transparency of policymaking.It will also help optimize manufacturing processes, prevent fraud, and streamline budget management in factory operations.

[0549] 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.

[0550] This invention provides a comprehensive system that combines data collection and analysis related to politics, economics, education, childcare, culture, science, and law from around the world, policy generation, transparency confirmation, budget management, cloud access, and an emotion engine that recognizes user emotions. This system has the functions to speed up policymaking, prevent fraud, efficiently manage budgets, improve transparency, and generate policies that reflect user emotions.

[0551] Each function of the present invention will now be described in detail.

[0552] Data collection methods

[0553] The server periodically collects data related to politics, economics, education, childcare, culture, science, law, and other areas from public databases, research papers, news articles, etc. This information includes a wide range of data, such as economic statistics, educational achievements, and scientific research progress.

[0554] Examples:

[0555] The server uses APIs to retrieve economic statistics from the World Bank database and store them in the system.

[0556] The server uses web crawling technology to download the latest research papers and store them in a database.

[0557] Data Analysis Methods

[0558] The server pre-processes the collected data (e.g., noise removal, data normalization) and analyzes it using machine learning algorithms and statistical analysis to discover significant trends and patterns.

[0559] Examples:

[0560] The server performs a regression analysis to investigate the impact of educational investment on economic growth.

[0561] The server performs clustering to group data that have similar policy effects.

[0562] Policy generation means

[0563] The server then performs scenario simulations based on the analysis results to generate optimal policies. Scenario simulations are mock experiments that create hypothetical policy scenarios and evaluate their effects and impacts.

[0564] Examples:

[0565] The server simulates different education policy scenarios and identifies those that contribute most to economic growth.

[0566] The server compares multiple policy proposals and selects the most appropriate one.

[0567] Transparency confirmation method

[0568] The server checks the generated policy proposals against a lobbying database of relevant parties to ensure transparency. It also checks the relationships between each part of the submitted policy proposal and related companies and organizations, and compares them with lobbying records to detect possible irregularities.

[0569] Examples:

[0570] The server investigates the ties between each submitted policy proposal and the relevant companies and organizations.

[0571] The server checks the lobbying records to detect possible fraud.

[0572] Budget Control Tools

[0573] The server automatically manages budgets related to legislators and elections and extracts expenses that can be reduced. This function reduces wasteful spending and enables efficient budget management.

[0574] Examples:

[0575] The server analyzes traditional travel and accommodation expenses for lawmakers and makes proposals for cost reduction through digital meetings.

[0576] The server reviews and reduces unnecessary expense items.

[0577] Cloud Access Methods

[0578] Users can access the cloud server via their devices and check the progress of policies and proposals in real time, which will increase the transparency of the policy-making process and boost public trust.

[0579] Examples:

[0580] Users can view the latest policy proposals using a web browser or a dedicated app.

[0581] Users have the ability to directly input their opinions and feedback on policies.

[0582] Emotion Engine

[0583] The server is equipped with an emotion engine that recognizes the user's emotions, analyzes the user's emotions based on the collected data, and reflects them in policy proposals.

[0584] Examples:

[0585] As users view policy proposals, the server collects their reactions using cameras and microphones and performs sentiment analysis.

[0586] The server immediately improves the content and presentation of policy proposals based on the results of user sentiment analysis.

[0587] Utilizing emotional feedback

[0588] The server uses an emotion engine to collect and analyze users' reactions to policy proposals in real time, allowing for a quantitative evaluation of the level of support for the policy.

[0589] Examples:

[0590] The server calculates the support and satisfaction of the policy based on the results of the user's sentiment analysis and stores the results in a database.

[0591] The server will use this data as a reference for future policy planning.

[0592] This system will dramatically improve the speed, transparency, and accuracy of policymaking, and will also enable more realistic and popular policy proposals that reflect the feelings of users.

[0593] The processing flow will be explained below.

[0594] Step 1:

[0595] The server collects data related to politics, economics, education, childcare, culture, science, and law from around the world, from public databases, research papers, news articles, etc. It uses APIs to access various databases and periodically retrieve the latest information.

[0596] Step 2:

[0597] The server pre-processes the collected data, specifically removing incomplete data and noise, normalizing the data, supplementing it with necessary data, and organizing it as time series data or categorical data.

[0598] Step 3:

[0599] The server analyzes the pre-processed data, using machine learning algorithms and statistical analysis techniques to uncover significant trends and patterns in the data. For example, it performs regression analysis to analyze the impact of education investment on economic growth.

[0600] Step 4:

[0601] The server performs scenario simulations based on the analysis results, creates hypothetical policy scenarios, and predicts their effects and impacts. As a result of the simulation, optimal policy proposals are automatically generated.

[0602] Step 5:

[0603] The server checks the generated policy proposals against a lobbying database of relevant parties to ensure transparency. It also checks the relationships between each part of the submitted policy proposal and related companies and organizations, and compares them with lobbying records to detect possible irregularities.

[0604] Step 6:

[0605] The server will keep a detailed record of the decision-making process and generate transparency reports that will be accessible to third parties and stored in an audit-ready format.

[0606] Step 7:

[0607] The server automatically manages budgets related to legislators and elections. It analyzes each expense item and identifies expenses that can be reduced. It reviews traditional legislator travel and accommodation expenses and proposes cost reductions, such as transitioning to digital meetings.

[0608] Step 8:

[0609] Users can access the cloud server via their devices to check the progress of policies and proposals in real time. They can also use a web browser or a dedicated app to view the latest policy proposals and directly enter their opinions and feedback as needed.

[0610] Step 9:

[0611] The server analyzes users' reactions using an emotion engine to recognize their emotions. Specifically, it collects emotional data using a camera and microphone while users are viewing policy proposals, and performs emotion analysis in real time.

[0612] Step 10:

[0613] The server improves policy proposals based on the results of sentiment analysis. It analyzes user reactions and instantly adjusts policy content and presentation methods. The results of this analysis are saved as reference data for future policy formulation.

[0614] This series of steps will improve the speed and transparency of policymaking, the accuracy of budget management, and lead to realistic and supportable policy proposals that reflect user sentiment.

[0615] Example 2

[0616] 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."

[0617] Conventional policymaking systems have limitations in terms of policy transparency, efficient budget management, and the ability to reflect user opinions. Furthermore, they lack centralized data collection and analysis, resulting in a lack of speed and accuracy in policy decisions. Furthermore, they lack the functionality to collect and reflect user sentiment and opinions in real time, making it difficult to generate realistic and popular policies.

[0618] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting data related to politics, economy, education, childcare, culture, science, and law from around the world; means for pre-processing and analyzing the collected data to discover important trends and patterns; means for conducting scenario simulations based on the analysis results to generate optimal policies; means for verifying the transparency of the generated policy proposals and comparing them with lobby databases of relevant parties; means for automatically managing budgets related to legislators and elections and extracting expenses that can be reduced; means for users to access a server on the cloud and check policy progress and proposals in real time; an emotion engine that recognizes and analyzes user emotions and reflects them in policy proposals; and means for collecting and analyzing user emotions regarding policy proposals using the emotion engine and utilizing them in future policy planning. This enables faster policy planning, improved transparency, more efficient budget management, and the generation of realistic and supportable policies that reflect user emotions.

[0619] "Global political, economic, educational, childcare, cultural, scientific and legal data" refers to information on various areas around the world collected from governments, economic institutions, educational institutions, childcare-related organizations, cultural institutions, scientific research institutions, legal institutions, etc.

[0620] "Means of collecting data" refers to a method of automatically obtaining information from public data on the Internet or dedicated databases using web crawling technology or API access.

[0621] "Preprocessing" refers to the process of performing preprocessing such as noise removal, normalization, and missing value completion on collected data, and is a preparatory step to improve the accuracy of analysis.

[0622] "Means of analysis to discover important trends and patterns" refers to techniques that use machine learning algorithms and statistical analysis methods to extract significant information from large amounts of data and reveal trends and patterns.

[0623] "A means of conducting scenario simulations and generating optimal policies" is a technology that sets up multiple hypothetical policy scenarios, evaluates and compares their effects and impacts, and automatically generates the most effective policies.

[0624] "Means to verify transparency and compare with lobbying databases of relevant parties" refers to a method of making the background and impact of generated policy proposals transparent and comparing them with existing lobbying databases to detect irregularities and bias.

[0625] "Means for automatically managing budgets and extracting expenses that can be reduced" refers to technology that centrally manages budgets related to lawmakers and elections through a system, uses digital analysis to identify wasteful spending, and proposes reductions.

[0626] "A means of accessing a server on the cloud and checking policy progress and proposals in real time" is a function that allows users to connect to the cloud via the Internet and instantly view and check the latest policy information and proposals.

[0627] The "emotion engine" is a system that analyzes a user's facial expressions and voice data to assess their emotional state and incorporates this information into the policy generation process.

[0628] "Means of collecting and analyzing emotions and utilizing them in future policy formulation" refers to a technology that uses an emotion engine to collect and analyze users' reactions to policy proposals, stores the results in a database, and reflects them in the generation of future policies.

[0629] This invention is a comprehensive system that collects and analyzes data related to politics, economy, education, childcare, culture, science, and law from around the world, generates optimal policies, checks transparency, streamlines budget management, and reflects user sentiment. The system of this invention is composed of three main entities: a server, a terminal, and a user.

[0630] Data collection methods

[0631] The server periodically collects data related to politics, economy, education, childcare, culture, science, and law from around the world using web crawlers, API access technologies (e.g., World Bank API), and downloads from databases. Examples of data collected include economic statistics, educational achievements, and scientific research progress.

[0632] Examples:

[0633] The server uses the World Bank API to obtain GDP data for each country.

[0634] The server uses web crawling techniques to download new research papers and store them in a database.

[0635] Data Analysis Methods

[0636] The server pre-processes the collected data (denoising, normalising the data, etc.) and then analyses it using machine learning algorithms and statistical analysis techniques, including regression analysis and clustering, to discover significant trends and patterns.

[0637] Examples:

[0638] The server uses regression analysis to investigate the impact of educational investment on economic growth.

[0639] The server performs clustering to group data that have similar policy effects.

[0640] Policy generation means

[0641] The server performs scenario simulation based on the analysis results to generate optimal policies. Scenario simulation is a method of setting up multiple hypothetical scenarios and evaluating and comparing their effects.

[0642] Examples:

[0643] The server hypothesizes different scenarios for education policy and evaluates the impact of each scenario on economic growth.

[0644] The server generates the most effective policy based on the simulation results.

[0645] Transparency confirmation method

[0646] The server checks the transparency of the generated policy proposals and checks them against a lobby database to detect possible fraud.

[0647] Examples:

[0648] The server checks whether the policy proposals are biased toward any particular company or organization.

[0649] The server checks the lobbying records to detect possible fraud.

[0650] Budget Control Tools

[0651] The server automatically manages budgets related to legislators and elections and identifies expenses that can be reduced.

[0652] Examples:

[0653] The server analyzes past travel and accommodation expenses for lawmakers and makes proposals for cost reduction through digital meetings.

[0654] The server reviews and reduces unnecessary expense items.

[0655] Cloud Access Methods

[0656] Users can access the cloud server using their devices and check policy progress and proposals in real time.

[0657] Examples:

[0658] Users can view the latest policy proposals using a web browser or a dedicated app.

[0659] Users can directly input their opinions and feedback on policies.

[0660] Emotion Engine

[0661] The server is equipped with an emotion engine that recognizes the user's emotions by analyzing the user's facial expressions and voice data to evaluate their emotional state.

[0662] Examples:

[0663] The server uses cameras and microphones to collect users' reactions as they view policy proposals.

[0664] The server refines the content of policy proposals based on the results of the sentiment analysis.

[0665] Utilizing emotional feedback

[0666] The server uses an emotion engine to collect and analyze users' emotions regarding policy proposals and use them in future policy planning.

[0667] Examples:

[0668] The server aggregates users' emotional data and evaluates the support and satisfaction of the policy.

[0669] The server uses the evaluation results in the next policy generation.

[0670] Prompt Sentence Examples

[0671] "Analyze the effects of education policies and see the correlation with economic growth."

[0672] "Perform user sentiment analysis on the latest policy proposals and refine them based on their feedback."

[0673] In this way, the system of the present invention realizes rapid policy formulation, improved transparency, more efficient budget management, and the generation of realistic and supportable policies that reflect the feelings of users.

[0674] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0675] Step 1:

[0676] The server collects data related to politics, economics, education, childcare, culture, science, and law from around the world using web crawling technology and API access (e.g., World Bank API). The input is various public databases and news sites, and the output is the collected dataset. Specifically, it periodically sends API requests to retrieve and store the latest data.

[0677] Step 2:

[0678] The server pre-processes the collected data. The input is the raw data collected in step 1, and the output is clean data that has been normalized and noise removed. Specific operations include detecting and removing outliers, filling in missing values, and standardizing the data format.

[0679] Step 3:

[0680] The server uses the cleaned data to run machine learning algorithms and statistical analysis. The input is the cleaned data processed in step 2, and the output is the analysis results (discovery of trends and patterns). As a specific example, regression analysis is used to analyze the impact of education investment on economic growth, and clustering is used to group data with similar policy effects.

[0681] Step 4:

[0682] The server runs a scenario simulation based on the analysis results and generates optimal policies. The input is the analysis results from Step 3, and the output is an optimal policy proposal. Specifically, multiple hypothetical scenarios are set up, and the most effective policy is selected by evaluating and comparing their effects.

[0683] Step 5:

[0684] The server compares the generated policy proposal with the lobby database to verify its transparency. The input is the generated policy proposal, and the output is the transparency evaluation result. Specifically, it checks whether the proposed policy proposal is biased toward specific companies or organizations.

[0685] Step 6:

[0686] The server automatically manages budgets related to legislators and elections and extracts expenses that can be reduced. The input is budget data related to legislators and elections, and the output is reduction proposals. As a specific example, it analyzes data on past legislator travel and accommodation expenses and proposes cost reductions through digital meetings.

[0687] Step 7:

[0688] Users use their devices to access the cloud server and check the progress and proposals of policies in real time. The input is a request to access the cloud database, and the output is the latest policy information. Specifically, users use a web browser or a dedicated app to view the latest policy proposals and provide feedback.

[0689] Step 8:

[0690] The server activates an emotion engine that recognizes the user's emotions and analyzes the user's facial expressions and voice data. The input is the user's reaction data, and the output is the emotion analysis results. As a specific example, the server analyzes the user's facial expressions and voice tone in real time while viewing policy proposals to evaluate their emotional state.

[0691] Step 9:

[0692] The server improves policy proposals based on the results of user sentiment analysis and uses them in future policy planning. The input is the sentiment analysis results, and the output is improved policy proposals and data to be used in the next policy generation. Specifically, it automatically adjusts the content and presentation method to elicit a positive response from users, and stores the evaluation results in a database.

[0693] Through these processing steps, rapid policy formulation, increased transparency, efficient budget management, and the generation of realistic and supportable policies that reflect user sentiment are realized.

[0694] (Application example 2)

[0695] 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."

[0696] In conventional policymaking systems, data collection and analysis are localized, making it difficult to comprehensively analyze a wide range of data from around the world. Furthermore, the transparency of the policies generated and the efficiency of budget management are insufficient, and there are also insufficient mechanisms to prevent fraud and reflect public sentiment in the actual policy generation. Furthermore, there is no optimized method for generating policies in terms of security measures, and improvements in overall reliability and transparency are needed.

[0697] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting data related to politics, economics, education, childcare, culture, science, and law from around the world; means for analyzing the collected data and discovering important trends and patterns; means for conducting scenario simulations based on the analysis results to generate optimal policies; means for verifying the transparency of the generated policy proposals and comparing them with lobby databases of relevant parties; means for automatically managing budgets related to legislators and elections and extracting cost savings; means for users to access a cloud-based server and check policy progress and proposals in real time; means for recognizing user sentiment and improving policy proposals based on the analysis results; and means for collecting security-related data and generating security policies. This dramatically improves the speed, transparency, and accuracy of policymaking, enabling more realistic and supportable policy proposals that reflect user sentiment. Furthermore, generating optimal policies for security measures also improves overall reliability and transparency.

[0698] "Political, economic, educational, childcare, cultural, scientific and legal data" refers to the totality of information collected from governments, economic institutions, educational institutions, childcare support groups, cultural institutions, scientific research institutions, legal organizations and other sources around the world.

[0699] "Means of collecting data" refers to systems that automatically collect data from specific sources using APIs, web crawling, scraping technologies, etc.

[0700] "Methods for discovering significant trends and patterns" refers to techniques that use machine learning algorithms and statistical analysis to extract meaningful trends and patterns from large amounts of data.

[0701] "Means for performing scenario simulation" refers to algorithms and software that quantitatively evaluate hypothetical policy scenarios based on collected data and analysis results and simulate their effects.

[0702] "Means to confirm the transparency of the generated policy proposals" refers to the process of comparing the generated policy proposals with lobby databases, etc., to verify the possibility of fraud.

[0703] "Automated budget management tools" refers to software and algorithms that digitally manage legislative and election-related budgets, calculate efficiency, and reduce waste.

[0704] "Means for accessing servers in the cloud" refers to the interfaces and protocols for accessing and using data and functions stored in the cloud through an Internet connection.

[0705] "Means for recognizing user emotions" refers to an emotion engine that uses sensors such as cameras and microphones to analyze emotions from the user's facial expressions and voice.

[0706] "Means of collecting security-related data" refers to systems that collect security-related information such as cyberattack trends, vulnerability information, and changes in laws.

[0707] "Means for generating security policies" refers to a system that analyzes collected security-related data and automatically proposes optimal security measures and policies.

[0708] "Means for improving policy proposals that reflect user emotions" refers to a function for dynamically modifying and improving the content of policy proposals based on user emotion data analyzed by the emotion engine.

[0709] This is a comprehensive system that collects and analyzes data related to politics, economy, education, childcare, culture, science, and law from around the world, generates optimal policies, ensures transparency, and efficiently manages budgets. It also has the ability to recognize users' emotions and improve policy proposals based on the analysis results.

[0710] The server contains the following main means:

[0711] 1. Data Collection Methods

[0712] The server uses APIs and web crawling technology to collect data related to politics, economics, education, childcare, culture, science, and law from around the world. For example, you can use APIs to obtain economic statistics or web crawling technology to download the latest research papers.

[0713] 2. Data analysis methods

[0714] The collected data is then standardized and analyzed using machine learning algorithms and statistical analysis to uncover important trends and patterns, allowing, for example, regression analysis to explore the impact of education investment on economic growth.

[0715] 3. Policy generation instruments

[0716] The server then performs scenario simulations based on the analysis results to generate optimal policies. This involves using an algorithm to generate multiple policy proposals and simulate their effects. For example, it simulates different education policy scenarios to identify the one that contributes most to economic growth.

[0717] 4. Transparency confirmation method

[0718] The generated policy proposals are checked against a lobby database to ensure transparency about the policy proposals, investigate ties with relevant companies and organizations, and detect any potential irregularities.

[0719] 5. Budgetary Control Tools

[0720] The server automatically manages budgets related to legislators and elections, using algorithms that analyze past expenditure data and identify potential cost savings, such as travel and accommodation costs that can be reduced by implementing digital meetings.

[0721] 6. Cloud Access Methods

[0722] Users can access the cloud server through a web browser or a dedicated app to check the progress and proposals of policies in real time, and can also directly enter their opinions and feedback on policies.

[0723] 7. Emotion recognition means

[0724] It is equipped with an emotion engine that recognizes the user's emotions, and uses a camera and microphone to analyze the user's facial expressions and voice, allowing it to dynamically modify and improve policy proposals based on the user's emotions.

[0725] 8. Security Policy Generation Method

[0726] The server collects and analyzes security-related data (e.g., cyberattack trends, vulnerability information, legal changes, etc.), and generates optimal security policies based on the results, ensuring transparency.

[0727] Specific examples of the hardware and software used include machine learning algorithms implemented in the Python language, a RESTful API used for data collection, Pandas and Scikit-learn used for data analysis, and AWS, Azure, and Google Cloud Platform as cloud servers.

[0728] As a concrete example, the following prompt sentence could be input to a generative AI model:

[0729] "Please build a system that collects and analyzes security data from around the world, generates optimal security policies, ensures transparency, and manages budgets. Also, please add a function to analyze user sentiment and reflect it in policy proposals."

[0730] This will dramatically improve the speed, transparency, and accuracy of policymaking, and will enable more realistic and supportable policy proposals that reflect user sentiment. It will also improve overall reliability and transparency by generating optimal policies for security measures.

[0731] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0732] Step 1:

[0733] The server uses APIs and web crawling technology to collect data related to politics, economics, education, childcare, culture, science, and law from around the world. Specifically, it uses APIs to obtain economic statistics and web crawling technology to collect the latest research papers. The input data is public data and web pages related to each field, and the output data is structured data containing that information. Data collection is carried out regularly to keep the content up to date.

[0734] Step 2:

[0735] The server pre-processes the collected data. This involves standardizing the data format and removing missing values ​​and noise. For example, data provided in different formats is standardized into CSV format and missing values ​​are imputed. The input data is the raw data collected in step 1, and the output data is the normalized, clean data.

[0736] Step 3:

[0737] The server analyzes the pre-processed data. This analysis uses machine learning algorithms and statistical analysis to discover important trends and patterns. Specifically, it performs regression analysis and clustering to examine the impact of education investment on economic growth. The input data is the pre-processed data, and the output data is the analysis results and trend information.

[0738] Step 4:

[0739] The server performs a scenario simulation based on the analysis results. In the scenario simulation, different policy scenarios are generated and their effects are evaluated. For example, multiple education policy scenarios are generated and it is determined which scenario will contribute most to economic growth. The input data are the analysis results obtained in Step 3, and the output data are the results of the simulation.

[0740] Step 5:

[0741] The server checks the transparency of the generated policy proposals. Specifically, it checks the policy proposals against the lobby database to detect possible fraud. The input data is the policy proposals resulting from the simulation, and the output data is the policy proposals whose transparency has been confirmed.

[0742] Step 6:

[0743] The server automatically manages budgets related to legislators and elections. It analyzes past expenditure data and extracts cost items that can be reduced. Specifically, it suggests travel and accommodation costs that can be reduced by introducing digital meetings. The input data is budget data related to legislators and elections, and the output data is a list of cost items that can be reduced.

[0744] Step 7:

[0745] Users with devices can access the cloud server using a web browser or a dedicated app to check policy progress and proposals in real time. They can also directly input their opinions and feedback on policies. The input data is the user's access information and feedback, and the output data is real-time policy progress information.

[0746] Step 8:

[0747] The server uses an emotion engine to recognize the user's emotions. It analyzes emotions using a camera and microphone and reflects the feedback in policy proposals. Specifically, it analyzes the user's facial expressions and voice to calculate the policy's support and satisfaction. The input data is the user's facial expressions and voice data, and the output data is feedback information based on the emotion analysis.

[0748] Step 9:

[0749] The server collects and analyzes security-related data to generate optimal security policies. Specifically, it collects cyber-attack trends and vulnerability information, and formulates security policies based on the results. The input data is public security-related data, and the output data is the generated security policy.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] [Third embodiment]

[0754] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0755] 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.

[0756] 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).

[0757] 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.

[0758] 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.

[0759] 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).

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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."

[0766] This invention provides a comprehensive system for collecting, analyzing, formulating policies, verifying transparency, managing budgets, and accessing data related to politics, economics, education, childcare, culture, science, and law from around the world. This system has the functions to speed up policymaking, prevent fraud, efficiently manage budgets, and improve transparency.

[0767] Each function of the present invention will now be described in detail.

[0768] Data collection methods

[0769] The server periodically collects data related to politics, economics, education, childcare, culture, science, law, and other areas from public databases, research papers, news articles, etc. This information includes a wide range of data, such as economic statistics, educational achievements, and scientific research progress.

[0770] Examples:

[0771] The server uses APIs to retrieve economic statistics from the World Bank database and store them in the system.

[0772] The server uses web crawling technology to download the latest research papers and store them in a database.

[0773] Data Analysis Methods

[0774] The server pre-processes the collected data (e.g., noise removal, data normalization) and analyzes it using machine learning algorithms and statistical analysis to discover significant trends and patterns.

[0775] Examples:

[0776] The server performs a regression analysis to investigate the impact of educational investment on economic growth.

[0777] The server performs clustering to group data that have similar policy effects.

[0778] Policy generation means

[0779] The server then performs scenario simulations based on the analysis results to generate optimal policies. Scenario simulations are mock experiments that create hypothetical policy scenarios and evaluate their effects and impacts.

[0780] Examples:

[0781] The server simulates different education policy scenarios and identifies those that contribute most to economic growth.

[0782] The server compares multiple policy proposals and selects the most appropriate one.

[0783] Transparency confirmation method

[0784] The server checks the transparency of the generated policy proposals and compares them with lobby databases of relevant parties, etc. This measure is intended to prevent fraud and collusion.

[0785] Examples:

[0786] The server investigates the ties between each submitted policy proposal and the relevant companies and organizations.

[0787] The server checks the lobbying records to detect possible fraud.

[0788] Budget Control Tools

[0789] The server automatically manages budgets related to legislators and elections and extracts expenses that can be reduced. This function reduces wasteful spending and enables efficient budget management.

[0790] Examples:

[0791] The server analyzes traditional travel and accommodation expenses for lawmakers and makes proposals for cost reduction through digital meetings.

[0792] The server reviews and reduces unnecessary expense items.

[0793] Cloud Access Methods

[0794] Users can access the cloud server via their devices and check the progress of policies and proposals in real time, which will increase the transparency of the policy-making process and boost public trust.

[0795] Examples:

[0796] Users can view the latest policy proposals using a web browser or a dedicated app.

[0797] Users have the ability to directly input their opinions and feedback on policies.

[0798] This system will dramatically improve the speed and transparency of policy formulation and the accuracy of budget management, enabling efficient policy implementation.

[0799] The processing flow will be explained below.

[0800] Step 1:

[0801] The server collects data related to politics, economics, education, childcare, culture, science, and law from around the world, from public databases, research papers, news articles, etc. The server uses APIs to access various databases and periodically retrieve the latest information.

[0802] Step 2:

[0803] The server pre-processes the collected data, removing incomplete data and noise and normalizing the data to make it easier to analyze. During this process, necessary data is supplemented and organized as time series data or category data.

[0804] Step 3:

[0805] The server analyzes the pre-processed data, using machine learning algorithms and statistical analysis techniques to uncover important trends and patterns in the data. Specifically, it performs regression analysis to examine the impact of education investment on economic growth, and clusters data to group together data with similar policy effects.

[0806] Step 4:

[0807] The server performs scenario simulations based on the analysis results, creating hypothetical policy scenarios and predicting their effects and impacts. As a result of the simulations, optimal policy proposals are automatically generated.

[0808] Step 5:

[0809] The server checks the generated policy proposals against a lobbying database of relevant parties to ensure transparency. It also checks the relationships between each part of the submitted policy proposal and related companies and organizations, and compares them with lobbying records to detect possible irregularities.

[0810] Step 6:

[0811] The server will keep a detailed record of the decision-making process and generate transparency reports that will be accessible to third parties and stored in an audit-ready format.

[0812] Step 7:

[0813] The server automatically manages budgets related to legislators and elections. It analyzes each expense item and identifies expenses that can be reduced. It reviews traditional legislator travel and accommodation expenses and proposes cost reductions, such as transitioning to digital meetings.

[0814] Step 8:

[0815] Users can access the cloud server via their devices to check the progress of policies and proposals in real time. They can also use a web browser or a dedicated app to view the latest policy proposals and directly enter their opinions and feedback as needed.

[0816] This series of steps will dramatically improve the speed and transparency of policy formulation and the accuracy of budget management, resulting in efficient policy implementation.

[0817] Example 1

[0818] 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."

[0819] In modern society, a huge amount of data is generated in a wide range of fields, including politics, economics, education, childcare, culture, science, and law. However, it is difficult to efficiently collect and analyze this data and generate optimal policies. Ensuring transparency in the policy generation process and efficient budget management are also major challenges. Existing systems have difficulty comprehensively processing these complex processes, resulting in delays in policy implementation and the risk of fraud.

[0820] 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.

[0821] In this invention, the server includes: means for periodically collecting data related to politics, economics, education, childcare, culture, science, and law from around the world; means for pre-processing the collected data, removing noise and normalizing the data, and analyzing it; means for conducting scenario simulations based on the analysis results and automatically generating optimal policies; means for confirming the transparency of the generated policy proposals and comparing them with databases of relevant parties; means for automatically managing budgets related to lawmakers and elections and extracting expenses that can be reduced; and means for users to access the system on the cloud and check policy progress and proposals in real time. This enables efficient data collection and analysis, ensures transparency in policy generation, and improves the efficiency of budget management.

[0822] "Data Collection Tools" refers to a function that periodically collects data related to politics, economics, education, childcare, culture, science, and law from public databases, research papers, news articles, etc. from around the world.

[0823] "Data analysis means" refers to a function that pre-processes collected data, removes noise, normalizes data, and analyzes it using machine learning algorithms and statistical methods.

[0824] "Scenario simulation means" is a function that creates hypothetical policy scenarios based on the results of data analysis and conducts simulation experiments to evaluate their effects and impacts.

[0825] The "policy generation means" is a function that automatically generates optimal policies based on the results of scenario simulations.

[0826] The "transparency verification means" is a function that verifies the transparency of generated policy proposals by comparing them with the databases of relevant parties in order to prevent fraud and collusion.

[0827] "Budget management tools" are functions that automatically manage budgets related to legislators and elections and extract expenses that can be reduced.

[0828] "Cloud access means" is a function that allows users to access a system on the cloud via their terminal and check policy progress and proposals in real time.

[0829] This invention provides a system for collecting, analyzing, policy-making, transparency checks, budget management, and cloud accessing data related to politics, economics, education, childcare, culture, science, and law from around the world. This system aims to speed up policy-making, prevent fraud, efficiently manage budgets, and improve transparency.

[0830] Data collection

[0831] The server periodically collects data related to politics, economics, education, childcare, culture, science, law, etc. from public databases, research papers, news articles, etc. For example, it uses the World Bank's API to obtain economic statistics and stores them in the system. In addition, it uses web crawling technology (e.g., Beautiful Soup) to download the latest research papers and news articles and store them in the database.

[0832] Data analysis

[0833] The server preprocesses the collected data (e.g., noise removal, data normalization) and analyzes it using machine learning algorithms and statistical analysis, using libraries such as Pandas and Scikit-learn. For example, it performs regression analysis to investigate the impact of education investment on economic growth. It also performs clustering to group data with similar policy effects.

[0834] policy generation

[0835] The server performs scenario simulations based on the analysis results and automatically generates optimal policies. Scenario simulations create different policy scenarios and evaluate their effects and impacts. For example, different education policy scenarios can be simulated to identify the one that contributes most to economic growth. The optimal policy proposals are compiled in document form and submitted to the relevant parties.

[0836] Transparency confirmation

[0837] The server checks the transparency of the generated policy proposals and compares them with relevant databases. To prevent corruption and collusion, it checks the relationships with companies and organizations and compares them with lobbying records. For example, it checks the relationships with relevant companies and organizations for each part of the submitted policy proposal.

[0838] Budget Management

[0839] The server automatically manages budgets related to legislators and elections, extracting expenses that can be reduced. This reduces wasteful spending and enables efficient budget management. For example, it analyzes traditional legislators' travel and accommodation expenses and proposes cost reductions through digital meetings.

[0840] Cloud Access

[0841] Users can access the cloud server via their devices and check the progress of policies and proposals in real time. This increases transparency in the policy-making process and strengthens public trust. Users can view the latest policy proposals using a web browser or a dedicated app, and can directly enter their opinions on policies using the feedback function.

[0842] Examples and prompts

[0843] A specific example would be the analysis and transparency check of education policies.

[0844] Examples:

[0845] 1. The server collects economic statistical data on education from the World Bank database via an API.

[0846] 2. The server analyzes the collected data and uses regression analysis to investigate the impact of educational investment on economic growth.

[0847] 3. Based on the analysis results, the server simulates multiple educational policy scenarios and generates the optimal policy.

[0848] 4. The server investigates the relationships of related companies and organizations with the generated educational policy proposals using transparency verification measures.

[0849] Example prompt sentence:

[0850] "How can I conduct a regression analysis using the latest World Bank data on the relationship between education investment and economic growth, create optimal education policy scenarios based on the results, and ensure the transparency of the policy proposals generated to eliminate the possibility of fraud?"

[0851] The system enables efficient data collection and analysis, ensures transparency in policy development, streamlines budget management, and allows users to monitor progress in real time.

[0852] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0853] Step 1:

[0854] Data collection

[0855] The server periodically collects data related to politics, economics, education, childcare, culture, science, law, etc. from around the world. Input data is obtained from public databases (e.g., the World Bank), research papers, news articles, etc. Specifically, the server uses an API to request economic statistical data from the World Bank and stores the received data. It also uses web crawling technology (e.g., Beautiful Soup) to download research papers and news articles and store them in a database. As output, the collected raw data is stored in a database.

[0856] Step 2:

[0857] Data Preprocessing

[0858] The server pre-processes the collected data. The input data is the raw data collected in step 1. Specifically, the server converts it into a data frame using the Pandas library, removes inconsistencies and missing values, and normalizes and denoises the data. The output is a clean, normalized dataset.

[0859] Step 3:

[0860] Data analysis

[0861] The server analyzes the preprocessed data. The input data is the dataset preprocessed in step 2. Specifically, the server uses the Scikit-learn library to run machine learning algorithms (e.g., regression analysis and clustering). For example, regression analysis is used to investigate the impact of educational investment on economic growth. The output is trends and patterns as analytical results, as well as the results of various models.

[0862] Step 4:

[0863] policy generation

[0864] The server performs scenario simulations based on the analysis results and generates optimal policies. The input data are the analysis results obtained in step 3. Specifically, the server uses a scenario simulation tool to generate hypothetical policy scenarios and evaluate their effects and impacts. For example, it simulates different education policy scenarios to identify the policy that contributes most to economic growth. The output is an optimal policy proposal.

[0865] Step 5:

[0866] Transparency confirmation

[0867] The server checks the transparency of the generated policy proposal. The input data is the policy proposal generated in step 4. Specifically, the server checks the data against the databases of relevant parties to check for possible fraud or collusion. For example, it investigates relationships with companies and organizations and compares them with lobbying records. The output is the transparency check result, and a warning is issued if there is any possibility of fraud.

[0868] Step 6:

[0869] Budget Management

[0870] The server automatically manages budgets related to legislators and elections, extracting expenses that can be reduced. The input data is past budget data. Specifically, the server analyzes legislators' past travel and accommodation expenses, etc., and proposes cost reductions through digital meetings. It also reviews unnecessary expense items and generates reduction proposals. The output is an efficient budget proposal.

[0871] Step 7:

[0872] Cloud Access

[0873] Users access the cloud server via their devices and check policy progress and proposals in real time. The input data is policy information stored on the cloud. Specifically, users launch a web browser or a dedicated app, enter their authentication information, and log in. They can then check the latest policy information and progress on the dashboard screen. They can also use the feedback function to enter their opinions and proposals regarding policies. The output is policy information and feedback updated in real time.

[0874] (Application example 1)

[0875] 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."

[0876] Modern society requires efficient collection and analysis of data related to politics, economy, education, childcare, culture, science, and law from around the world, ensuring transparency and improving budget management. However, there is a lack of systems that can systematically collect and analyze this data and efficiently generate policies, verify transparency, and manage budgets. Similar challenges exist in factory operations, where it is difficult to utilize data from sensors and equipment to optimize manufacturing processes, prevent fraud, and improve budget management efficiency.

[0877] 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.

[0878] In this invention, the server includes: means for collecting data related to politics, economics, education, childcare, culture, science, and law from around the world; means for analyzing the collected data and discovering important trends and patterns; means for conducting scenario simulations based on the analysis results to generate optimal policies; means for verifying the transparency of the generated policy proposals and comparing them with lobby databases of relevant parties; means for automatically managing budgets related to legislators and elections and extracting cost savings; means for users to access the cloud server and check policy progress and proposals in real time; means for collecting data from sensors and devices in factories and analyzing the data to optimize manufacturing processes; means for conducting simulations of manufacturing processes and generating optimal manufacturing plans; means for verifying the transparency of manufacturing plans and preventing fraud; and means for automatically managing manufacturing-related budgets and proposing cost savings. This enables more efficient and transparent policy generation in all fields, including politics, economics, and education, as well as optimizing manufacturing processes, preventing fraud, and streamlining budget management in factory operations.

[0879] "Collection tools" are devices and processes for systematically collecting data from sensors and equipment in politics, economics, education, childcare, culture, science, law, and factories around the world.

[0880] "Analysis tools" are techniques and methods for analyzing collected data and discovering trends and patterns in the data.

[0881] "Scenario simulation" is a method for generating hypothetical scenarios based on analysis results and evaluating the effects and impacts of those scenarios.

[0882] A "policy generator" is an algorithm and process for creating optimal policies from the results of scenario simulations.

[0883] "Transparency verification means" refers to a mechanism or method for verifying the transparency of generated policy proposals and manufacturing plans and preventing fraudulent activities.

[0884] "Budget control tools" are technologies and processes for automatically managing legislative and election-related budgets and manufacturing-related budgets and extracting expenses and costs that can be reduced.

[0885] "Cloud access means" refers to the technology and process that allows users to access a server on the cloud via the Internet and check policies, the progress of manufacturing processes, and proposals in real time.

[0886] This invention is a comprehensive system that systematically collects and analyzes data related to politics, economics, education, childcare, culture, science, and law from around the world, as well as data from sensors and equipment in factories, to generate policies, optimize manufacturing processes, prevent fraud, and manage budgets.

[0887] The server first uses APIs and web crawling technology to collect a wide range of data from public databases, news articles, research papers, etc. For example, it can retrieve economic statistics from the World Bank database or download the latest research papers. It can also collect real-time data from sensors and equipment in factories.

[0888] The collected data undergoes preprocessing such as noise removal and data normalization, and is then analyzed using machine learning algorithms and statistical analysis. Specifically, regression analysis and clustering are used to discover important trends and patterns. Regression analysis is used to investigate the impact of education investment on economic growth, and data with similar policy effects are grouped together.

[0889] Based on the analysis results, the server runs scenario simulations, which evaluate the effects of different policies and manufacturing scenarios in a virtual environment and generate optimal policies and manufacturing plans. Scenario simulations are conducted to select the optimal training policies, and simulations are conducted to select the optimal manufacturing plans.

[0890] The policy proposals and production plans generated are cross-checked with relevant databases to ensure transparency. Each part of the policy proposal is checked for ties to relevant companies and organizations and cross-checked with lobbying records to prevent corruption and collusion.

[0891] The server also automatically manages budgets related to legislators and elections, as well as manufacturing, and identifies expenses and costs that can be reduced. For example, it can analyze legislators' travel and accommodation expenses and propose cost reductions through digital meetings.

[0892] Users can access the cloud server using a web browser or a dedicated app to check the progress of policies, manufacturing process status, and proposals in real time. They can also directly input their opinions and feedback on policies.

[0893] The implementation of this system uses the following hardware and software:

[0894] Hardware: Factory sensors, smartphones, tablets, robotic arms

[0895] Software: Python environment, Pandas, NumPy, scikit-learn, API access

[0896] For example, an application used on a factory production line collects data in real time from all the equipment in the factory and sends it to a server on the cloud. The data is analyzed, and suggestions for optimizing the manufacturing process and reducing costs are sent to the user's smartphone or tablet.

[0897] Example prompts to input to a generative AI model:

[0898] "Design an integrated manufacturing management application for factory robots. This application will collect data from sensors and robots, perform data analysis, optimize the manufacturing process, ensure transparency, and manage budgets. Specific functions will include data collection and pre-processing, data analysis using machine learning, scenario simulation, transparency, and budget management."

[0899] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0900] Step 1:

[0901] The server collects data from public databases, news articles, and research papers around the world. Specifically, it uses APIs to retrieve economic statistics from the World Bank database and web crawling technology to download the latest research papers. It also collects data in real time from sensors and equipment within the factory. Inputs include data from APIs and sensors, and outputs are the collected data.

[0902] Step 2:

[0903] The server preprocesses the collected data. Preprocessing includes noise removal and data normalization, such as imputing missing values ​​and removing outliers. The input includes the raw data collected in step 1, and the output is the preprocessed, clean data.

[0904] Step 3:

[0905] The server analyzes the preprocessed data. This analysis uses machine learning algorithms and statistical analysis. For example, regression analysis can be used to investigate the impact of education investment on economic growth, or clustering can be used to group data with similar policy effects. The input includes the preprocessed data, and the output is trends and patterns resulting from the analysis.

[0906] Step 4:

[0907] The server performs scenario simulations based on the analysis results. It virtually generates different policy and manufacturing scenarios and evaluates their effects and impacts. For example, it simulates different education policy scenarios to identify the optimal one. The input includes the analysis results, and the output is the evaluation results of the scenarios.

[0908] Step 5:

[0909] The server checks the transparency of the generated policy proposals and manufacturing plans, checking them against lobby databases and other related parties to check for signs of fraud or collusion. The inputs include the generated scenarios and information from related databases, and the output is a transparency check report.

[0910] Step 6:

[0911] The server automatically manages budgets related to legislators, elections, and manufacturing. It analyzes historical budget data to identify expenses and costs that can be reduced. For example, it analyzes historical legislators' travel and accommodation expenses and proposes cost reductions through digital meetings. The input includes historical budget data, and the output is budget reduction proposals.

[0912] Step 7:

[0913] Users can access the cloud server using a web browser or a dedicated app to check the progress and suggestions of policies and manufacturing processes in real time. They can also directly input their opinions and feedback on policies. The input includes the user's access information and feedback, and the output is a display of real-time progress and suggestions.

[0914] This system will systematically collect and analyze data from around the world in fields such as politics, economics, and education, making it possible to improve the efficiency and transparency of policymaking.It will also help optimize manufacturing processes, prevent fraud, and streamline budget management in factory operations.

[0915] 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.

[0916] This invention provides a comprehensive system that combines data collection and analysis related to politics, economics, education, childcare, culture, science, and law from around the world, policy generation, transparency confirmation, budget management, cloud access, and an emotion engine that recognizes user emotions. This system has the functions to speed up policymaking, prevent fraud, efficiently manage budgets, improve transparency, and generate policies that reflect user emotions.

[0917] Each function of the present invention will now be described in detail.

[0918] Data collection methods

[0919] The server periodically collects data related to politics, economics, education, childcare, culture, science, law, and other areas from public databases, research papers, news articles, etc. This information includes a wide range of data, such as economic statistics, educational achievements, and scientific research progress.

[0920] Examples:

[0921] The server uses APIs to retrieve economic statistics from the World Bank database and store them in the system.

[0922] The server uses web crawling technology to download the latest research papers and store them in a database.

[0923] Data Analysis Methods

[0924] The server pre-processes the collected data (e.g., noise removal, data normalization) and analyzes it using machine learning algorithms and statistical analysis to discover significant trends and patterns.

[0925] Examples:

[0926] The server performs a regression analysis to investigate the impact of educational investment on economic growth.

[0927] The server performs clustering to group data that have similar policy effects.

[0928] Policy generation means

[0929] The server then performs scenario simulations based on the analysis results to generate optimal policies. Scenario simulations are mock experiments that create hypothetical policy scenarios and evaluate their effects and impacts.

[0930] Examples:

[0931] The server simulates different education policy scenarios and identifies those that contribute most to economic growth.

[0932] The server compares multiple policy proposals and selects the most appropriate one.

[0933] Transparency confirmation method

[0934] The server checks the generated policy proposals against a lobbying database of relevant parties to ensure transparency. It also checks the relationships between each part of the submitted policy proposal and related companies and organizations, and compares them with lobbying records to detect possible irregularities.

[0935] Examples:

[0936] The server investigates the ties between each submitted policy proposal and the relevant companies and organizations.

[0937] The server checks the lobbying records to detect possible fraud.

[0938] Budget Control Tools

[0939] The server automatically manages budgets related to legislators and elections and extracts expenses that can be reduced. This function reduces wasteful spending and enables efficient budget management.

[0940] Examples:

[0941] The server analyzes traditional travel and accommodation expenses for lawmakers and makes proposals for cost reduction through digital meetings.

[0942] The server reviews and reduces unnecessary expense items.

[0943] Cloud Access Methods

[0944] Users can access the cloud server via their devices and check the progress of policies and proposals in real time, which will increase the transparency of the policy-making process and boost public trust.

[0945] Examples:

[0946] Users can view the latest policy proposals using a web browser or a dedicated app.

[0947] Users have the ability to directly input their opinions and feedback on policies.

[0948] Emotion Engine

[0949] The server is equipped with an emotion engine that recognizes the user's emotions, analyzes the user's emotions based on the collected data, and reflects them in policy proposals.

[0950] Examples:

[0951] As users view policy proposals, the server collects their reactions using cameras and microphones and performs sentiment analysis.

[0952] The server immediately improves the content and presentation of policy proposals based on the results of user sentiment analysis.

[0953] Utilizing emotional feedback

[0954] The server uses an emotion engine to collect and analyze users' reactions to policy proposals in real time, allowing for a quantitative evaluation of the level of support for the policy.

[0955] Examples:

[0956] The server calculates the support and satisfaction of the policy based on the results of the user's sentiment analysis and stores the results in a database.

[0957] The server will use this data as a reference for future policy planning.

[0958] This system will dramatically improve the speed, transparency, and accuracy of policymaking, and will also enable more realistic and popular policy proposals that reflect the feelings of users.

[0959] The processing flow will be explained below.

[0960] Step 1:

[0961] The server collects data related to politics, economics, education, childcare, culture, science, and law from around the world, from public databases, research papers, news articles, etc. It uses APIs to access various databases and periodically retrieve the latest information.

[0962] Step 2:

[0963] The server pre-processes the collected data, specifically removing incomplete data and noise, normalizing the data, supplementing it with necessary data, and organizing it as time series data or categorical data.

[0964] Step 3:

[0965] The server analyzes the pre-processed data, using machine learning algorithms and statistical analysis techniques to uncover significant trends and patterns in the data. For example, it performs regression analysis to analyze the impact of education investment on economic growth.

[0966] Step 4:

[0967] The server performs scenario simulations based on the analysis results, creates hypothetical policy scenarios, and predicts their effects and impacts. As a result of the simulation, optimal policy proposals are automatically generated.

[0968] Step 5:

[0969] The server checks the generated policy proposals against a lobbying database of relevant parties to ensure transparency. It also checks the relationships between each part of the submitted policy proposal and related companies and organizations, and compares them with lobbying records to detect possible irregularities.

[0970] Step 6:

[0971] The server will keep a detailed record of the decision-making process and generate transparency reports that will be accessible to third parties and stored in an audit-ready format.

[0972] Step 7:

[0973] The server automatically manages budgets related to legislators and elections. It analyzes each expense item and identifies expenses that can be reduced. It reviews traditional legislator travel and accommodation expenses and proposes cost reductions, such as transitioning to digital meetings.

[0974] Step 8:

[0975] Users can access the cloud server via their devices to check the progress of policies and proposals in real time. They can also use a web browser or a dedicated app to view the latest policy proposals and directly enter their opinions and feedback as needed.

[0976] Step 9:

[0977] The server analyzes users' reactions using an emotion engine to recognize their emotions. Specifically, it collects emotional data using a camera and microphone while users are viewing policy proposals, and performs emotion analysis in real time.

[0978] Step 10:

[0979] The server improves policy proposals based on the results of sentiment analysis. It analyzes user reactions and instantly adjusts policy content and presentation methods. The results of this analysis are saved as reference data for future policy formulation.

[0980] This series of steps will improve the speed and transparency of policymaking, the accuracy of budget management, and lead to realistic and supportable policy proposals that reflect user sentiment.

[0981] Example 2

[0982] 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."

[0983] Conventional policymaking systems have limitations in terms of policy transparency, efficient budget management, and the ability to reflect user opinions. Furthermore, they lack centralized data collection and analysis, resulting in a lack of speed and accuracy in policy decisions. Furthermore, they lack the functionality to collect and reflect user sentiment and opinions in real time, making it difficult to generate realistic and popular policies.

[0984] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting data related to politics, economy, education, childcare, culture, science, and law from around the world; means for pre-processing and analyzing the collected data to discover important trends and patterns; means for conducting scenario simulations based on the analysis results to generate optimal policies; means for verifying the transparency of the generated policy proposals and comparing them with lobby databases of relevant parties; means for automatically managing budgets related to legislators and elections and extracting expenses that can be reduced; means for users to access a server on the cloud and check policy progress and proposals in real time; an emotion engine that recognizes and analyzes user emotions and reflects them in policy proposals; and means for collecting and analyzing user emotions regarding policy proposals using the emotion engine and utilizing them in future policy planning. This enables faster policy planning, improved transparency, more efficient budget management, and the generation of realistic and supportable policies that reflect user emotions.

[0985] "Global political, economic, educational, childcare, cultural, scientific and legal data" refers to information on various areas around the world collected from governments, economic institutions, educational institutions, childcare-related organizations, cultural institutions, scientific research institutions, legal institutions, etc.

[0986] "Means of collecting data" refers to a method of automatically obtaining information from public data on the Internet or dedicated databases using web crawling technology or API access.

[0987] "Preprocessing" refers to the process of performing preprocessing such as noise removal, normalization, and missing value completion on collected data, and is a preparatory step to improve the accuracy of analysis.

[0988] "Means of analysis to discover important trends and patterns" refers to techniques that use machine learning algorithms and statistical analysis methods to extract significant information from large amounts of data and reveal trends and patterns.

[0989] "A means of conducting scenario simulations and generating optimal policies" is a technology that sets up multiple hypothetical policy scenarios, evaluates and compares their effects and impacts, and automatically generates the most effective policies.

[0990] "Means to verify transparency and compare with lobbying databases of relevant parties" refers to a method of making the background and impact of generated policy proposals transparent and comparing them with existing lobbying databases to detect irregularities and bias.

[0991] "Means for automatically managing budgets and extracting expenses that can be reduced" refers to technology that centrally manages budgets related to lawmakers and elections through a system, uses digital analysis to identify wasteful spending, and proposes reductions.

[0992] "A means of accessing a server on the cloud and checking policy progress and proposals in real time" is a function that allows users to connect to the cloud via the Internet and instantly view and check the latest policy information and proposals.

[0993] The "emotion engine" is a system that analyzes a user's facial expressions and voice data to assess their emotional state and incorporates this information into the policy generation process.

[0994] "Means of collecting and analyzing emotions and utilizing them in future policy formulation" refers to a technology that uses an emotion engine to collect and analyze users' reactions to policy proposals, stores the results in a database, and reflects them in the generation of future policies.

[0995] This invention is a comprehensive system that collects and analyzes data related to politics, economy, education, childcare, culture, science, and law from around the world, generates optimal policies, checks transparency, streamlines budget management, and reflects user sentiment. The system of this invention is composed of three main entities: a server, a terminal, and a user.

[0996] Data collection methods

[0997] The server periodically collects data related to politics, economy, education, childcare, culture, science, and law from around the world using web crawlers, API access technologies (e.g., World Bank API), and downloads from databases. Examples of data collected include economic statistics, educational achievements, and scientific research progress.

[0998] Examples:

[0999] The server uses the World Bank API to obtain GDP data for each country.

[1000] The server uses web crawling techniques to download new research papers and store them in a database.

[1001] Data Analysis Methods

[1002] The server pre-processes the collected data (denoising, normalising the data, etc.) and then analyses it using machine learning algorithms and statistical analysis techniques, including regression analysis and clustering, to discover significant trends and patterns.

[1003] Examples:

[1004] The server uses regression analysis to investigate the impact of educational investment on economic growth.

[1005] The server performs clustering to group data that have similar policy effects.

[1006] Policy generation means

[1007] The server performs scenario simulation based on the analysis results to generate optimal policies. Scenario simulation is a method of setting up multiple hypothetical scenarios and evaluating and comparing their effects.

[1008] Examples:

[1009] The server hypothesizes different scenarios for education policy and evaluates the impact of each scenario on economic growth.

[1010] The server generates the most effective policy based on the simulation results.

[1011] Transparency confirmation method

[1012] The server checks the transparency of the generated policy proposals and checks them against a lobby database to detect possible fraud.

[1013] Examples:

[1014] The server checks whether the policy proposals are biased toward any particular company or organization.

[1015] The server checks the lobbying records to detect possible fraud.

[1016] Budget Control Tools

[1017] The server automatically manages budgets related to legislators and elections and identifies expenses that can be reduced.

[1018] Examples:

[1019] The server analyzes past travel and accommodation expenses for lawmakers and makes proposals for cost reduction through digital meetings.

[1020] The server reviews and reduces unnecessary expense items.

[1021] Cloud Access Methods

[1022] Users can access the cloud server using their devices and check policy progress and proposals in real time.

[1023] Examples:

[1024] Users can view the latest policy proposals using a web browser or a dedicated app.

[1025] Users can directly input their opinions and feedback on policies.

[1026] Emotion Engine

[1027] The server is equipped with an emotion engine that recognizes the user's emotions by analyzing the user's facial expressions and voice data to evaluate their emotional state.

[1028] Examples:

[1029] The server uses cameras and microphones to collect users' reactions as they view policy proposals.

[1030] The server refines the content of policy proposals based on the results of the sentiment analysis.

[1031] Utilizing emotional feedback

[1032] The server uses an emotion engine to collect and analyze users' emotions regarding policy proposals and use them in future policy planning.

[1033] Examples:

[1034] The server aggregates users' emotional data and evaluates the support and satisfaction of the policy.

[1035] The server uses the evaluation results in the next policy generation.

[1036] Prompt Sentence Examples

[1037] "Analyze the effects of education policies and see the correlation with economic growth."

[1038] "Perform user sentiment analysis on the latest policy proposals and refine them based on their feedback."

[1039] In this way, the system of the present invention realizes rapid policy formulation, improved transparency, more efficient budget management, and the generation of realistic and supportable policies that reflect the feelings of users.

[1040] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1041] Step 1:

[1042] The server collects data related to politics, economics, education, childcare, culture, science, and law from around the world using web crawling technology and API access (e.g., World Bank API). The input is various public databases and news sites, and the output is the collected dataset. Specifically, it periodically sends API requests to retrieve and store the latest data.

[1043] Step 2:

[1044] The server pre-processes the collected data. The input is the raw data collected in step 1, and the output is clean data that has been normalized and noise removed. Specific operations include detecting and removing outliers, filling in missing values, and standardizing the data format.

[1045] Step 3:

[1046] The server uses the cleaned data to run machine learning algorithms and statistical analysis. The input is the cleaned data processed in step 2, and the output is the analysis results (discovery of trends and patterns). As a specific example, regression analysis is used to analyze the impact of education investment on economic growth, and clustering is used to group data with similar policy effects.

[1047] Step 4:

[1048] The server runs a scenario simulation based on the analysis results and generates optimal policies. The input is the analysis results from Step 3, and the output is an optimal policy proposal. Specifically, multiple hypothetical scenarios are set up, and the most effective policy is selected by evaluating and comparing their effects.

[1049] Step 5:

[1050] The server compares the generated policy proposal with the lobby database to verify its transparency. The input is the generated policy proposal, and the output is the transparency evaluation result. Specifically, it checks whether the proposed policy proposal is biased toward specific companies or organizations.

[1051] Step 6:

[1052] The server automatically manages budgets related to legislators and elections and extracts expenses that can be reduced. The input is budget data related to legislators and elections, and the output is reduction proposals. As a specific example, it analyzes data on past legislator travel and accommodation expenses and proposes cost reductions through digital meetings.

[1053] Step 7:

[1054] Users use their devices to access the cloud server and check the progress and proposals of policies in real time. The input is a request to access the cloud database, and the output is the latest policy information. Specifically, users use a web browser or a dedicated app to view the latest policy proposals and provide feedback.

[1055] Step 8:

[1056] The server activates an emotion engine that recognizes the user's emotions and analyzes the user's facial expressions and voice data. The input is the user's reaction data, and the output is the emotion analysis results. As a specific example, the server analyzes the user's facial expressions and voice tone in real time while viewing policy proposals to evaluate their emotional state.

[1057] Step 9:

[1058] The server improves policy proposals based on the results of user sentiment analysis and uses them in future policy planning. The input is the sentiment analysis results, and the output is improved policy proposals and data to be used in the next policy generation. Specifically, it automatically adjusts the content and presentation method to elicit a positive response from users, and stores the evaluation results in a database.

[1059] Through these processing steps, rapid policy formulation, increased transparency, efficient budget management, and the generation of realistic and supportable policies that reflect user sentiment are realized.

[1060] (Application example 2)

[1061] 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."

[1062] In conventional policymaking systems, data collection and analysis are localized, making it difficult to comprehensively analyze a wide range of data from around the world. Furthermore, the transparency of the policies generated and the efficiency of budget management are insufficient, and there are also insufficient mechanisms to prevent fraud and reflect public sentiment in the actual policy generation. Furthermore, there is no optimized method for generating policies in terms of security measures, and improvements in overall reliability and transparency are needed.

[1063] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting data related to politics, economics, education, childcare, culture, science, and law from around the world; means for analyzing the collected data and discovering important trends and patterns; means for conducting scenario simulations based on the analysis results to generate optimal policies; means for verifying the transparency of the generated policy proposals and comparing them with lobby databases of relevant parties; means for automatically managing budgets related to legislators and elections and extracting cost savings; means for users to access a cloud-based server and check policy progress and proposals in real time; means for recognizing user sentiment and improving policy proposals based on the analysis results; and means for collecting security-related data and generating security policies. This dramatically improves the speed, transparency, and accuracy of policymaking, enabling more realistic and supportable policy proposals that reflect user sentiment. Furthermore, generating optimal policies for security measures also improves overall reliability and transparency.

[1064] "Political, economic, educational, childcare, cultural, scientific and legal data" refers to the totality of information collected from governments, economic institutions, educational institutions, childcare support groups, cultural institutions, scientific research institutions, legal organizations and other sources around the world.

[1065] "Means of collecting data" refers to systems that automatically collect data from specific sources using APIs, web crawling, scraping technologies, etc.

[1066] "Methods for discovering significant trends and patterns" refers to techniques that use machine learning algorithms and statistical analysis to extract meaningful trends and patterns from large amounts of data.

[1067] "Means for performing scenario simulation" refers to algorithms and software that quantitatively evaluate hypothetical policy scenarios based on collected data and analysis results and simulate their effects.

[1068] "Means to confirm the transparency of the generated policy proposals" refers to the process of comparing the generated policy proposals with lobby databases, etc., to verify the possibility of fraud.

[1069] "Automated budget management tools" refers to software and algorithms that digitally manage legislative and election-related budgets, calculate efficiency, and reduce waste.

[1070] "Means for accessing servers in the cloud" refers to the interfaces and protocols for accessing and using data and functions stored in the cloud through an Internet connection.

[1071] "Means for recognizing user emotions" refers to an emotion engine that uses sensors such as cameras and microphones to analyze emotions from the user's facial expressions and voice.

[1072] "Means of collecting security-related data" refers to systems that collect security-related information such as cyberattack trends, vulnerability information, and changes in laws.

[1073] "Means for generating security policies" refers to a system that analyzes collected security-related data and automatically proposes optimal security measures and policies.

[1074] "Means for improving policy proposals that reflect user emotions" refers to a function for dynamically modifying and improving the content of policy proposals based on user emotion data analyzed by the emotion engine.

[1075] This is a comprehensive system that collects and analyzes data related to politics, economy, education, childcare, culture, science, and law from around the world, generates optimal policies, ensures transparency, and efficiently manages budgets. It also has the ability to recognize users' emotions and improve policy proposals based on the analysis results.

[1076] The server contains the following main means:

[1077] 1. Data Collection Methods

[1078] The server uses APIs and web crawling technology to collect data related to politics, economics, education, childcare, culture, science, and law from around the world. For example, you can use APIs to obtain economic statistics or web crawling technology to download the latest research papers.

[1079] 2. Data analysis methods

[1080] The collected data is then standardized and analyzed using machine learning algorithms and statistical analysis to uncover important trends and patterns, allowing, for example, regression analysis to explore the impact of education investment on economic growth.

[1081] 3. Policy generation instruments

[1082] The server then performs scenario simulations based on the analysis results to generate optimal policies. This involves using an algorithm to generate multiple policy proposals and simulate their effects. For example, it simulates different education policy scenarios to identify the one that contributes most to economic growth.

[1083] 4. Transparency confirmation method

[1084] The generated policy proposals are checked against a lobby database to ensure transparency about the policy proposals, investigate ties with relevant companies and organizations, and detect any potential irregularities.

[1085] 5. Budgetary Control Tools

[1086] The server automatically manages budgets related to legislators and elections, using algorithms that analyze past expenditure data and identify potential cost savings, such as travel and accommodation costs that can be reduced by implementing digital meetings.

[1087] 6. Cloud Access Methods

[1088] Users can access the cloud server through a web browser or a dedicated app to check the progress and proposals of policies in real time, and can also directly enter their opinions and feedback on policies.

[1089] 7. Emotion recognition means

[1090] It is equipped with an emotion engine that recognizes the user's emotions, and uses a camera and microphone to analyze the user's facial expressions and voice, allowing it to dynamically modify and improve policy proposals based on the user's emotions.

[1091] 8. Security Policy Generation Method

[1092] The server collects and analyzes security-related data (e.g., cyberattack trends, vulnerability information, legal changes, etc.), and generates optimal security policies based on the results, ensuring transparency.

[1093] Specific examples of the hardware and software used include machine learning algorithms implemented in the Python language, a RESTful API used for data collection, Pandas and Scikit-learn used for data analysis, and AWS, Azure, and Google Cloud Platform as cloud servers.

[1094] As a concrete example, the following prompt sentence could be input to a generative AI model:

[1095] "Please build a system that collects and analyzes security data from around the world, generates optimal security policies, ensures transparency, and manages budgets. Also, please add a function to analyze user sentiment and reflect it in policy proposals."

[1096] This will dramatically improve the speed, transparency, and accuracy of policymaking, and will enable more realistic and supportable policy proposals that reflect user sentiment. It will also improve overall reliability and transparency by generating optimal policies for security measures.

[1097] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1098] Step 1:

[1099] The server uses APIs and web crawling technology to collect data related to politics, economics, education, childcare, culture, science, and law from around the world. Specifically, it uses APIs to obtain economic statistics and web crawling technology to collect the latest research papers. The input data is public data and web pages related to each field, and the output data is structured data containing that information. Data collection is carried out regularly to keep the content up to date.

[1100] Step 2:

[1101] The server pre-processes the collected data. This involves standardizing the data format and removing missing values ​​and noise. For example, data provided in different formats is standardized into CSV format and missing values ​​are imputed. The input data is the raw data collected in step 1, and the output data is the normalized, clean data.

[1102] Step 3:

[1103] The server analyzes the pre-processed data. This analysis uses machine learning algorithms and statistical analysis to discover important trends and patterns. Specifically, it performs regression analysis and clustering to examine the impact of education investment on economic growth. The input data is the pre-processed data, and the output data is the analysis results and trend information.

[1104] Step 4:

[1105] The server performs a scenario simulation based on the analysis results. In the scenario simulation, different policy scenarios are generated and their effects are evaluated. For example, multiple education policy scenarios are generated and it is determined which scenario will contribute most to economic growth. The input data are the analysis results obtained in Step 3, and the output data are the results of the simulation.

[1106] Step 5:

[1107] The server checks the transparency of the generated policy proposals. Specifically, it checks the policy proposals against the lobby database to detect possible fraud. The input data is the policy proposals resulting from the simulation, and the output data is the policy proposals whose transparency has been confirmed.

[1108] Step 6:

[1109] The server automatically manages budgets related to legislators and elections. It analyzes past expenditure data and extracts cost items that can be reduced. Specifically, it suggests travel and accommodation costs that can be reduced by introducing digital meetings. The input data is budget data related to legislators and elections, and the output data is a list of cost items that can be reduced.

[1110] Step 7:

[1111] Users with devices can access the cloud server using a web browser or a dedicated app to check policy progress and proposals in real time. They can also directly input their opinions and feedback on policies. The input data is the user's access information and feedback, and the output data is real-time policy progress information.

[1112] Step 8:

[1113] The server uses an emotion engine to recognize the user's emotions. It analyzes emotions using a camera and microphone and reflects the feedback in policy proposals. Specifically, it analyzes the user's facial expressions and voice to calculate the policy's support and satisfaction. The input data is the user's facial expressions and voice data, and the output data is feedback information based on the emotion analysis.

[1114] Step 9:

[1115] The server collects and analyzes security-related data to generate optimal security policies. Specifically, it collects cyber-attack trends and vulnerability information, and formulates security policies based on the results. The input data is public security-related data, and the output data is the generated security policy.

[1116] 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.

[1117] 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.

[1118] 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.

[1119] [Fourth embodiment]

[1120] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1121] 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.

[1122] 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).

[1123] 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.

[1124] 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.

[1125] 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).

[1126] 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.

[1127] 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.

[1128] 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.

[1129] 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.

[1130] 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.

[1131] 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.

[1132] 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."

[1133] This invention provides a comprehensive system for collecting, analyzing, formulating policies, verifying transparency, managing budgets, and accessing data related to politics, economics, education, childcare, culture, science, and law from around the world. This system has the functions to speed up policymaking, prevent fraud, efficiently manage budgets, and improve transparency.

[1134] Each function of the present invention will now be described in detail.

[1135] Data collection methods

[1136] The server periodically collects data related to politics, economics, education, childcare, culture, science, law, and other areas from public databases, research papers, news articles, etc. This information includes a wide range of data, such as economic statistics, educational achievements, and scientific research progress.

[1137] Examples:

[1138] The server uses APIs to retrieve economic statistics from the World Bank database and store them in the system.

[1139] The server uses web crawling technology to download the latest research papers and store them in a database.

[1140] Data Analysis Methods

[1141] The server pre-processes the collected data (e.g., noise removal, data normalization) and analyzes it using machine learning algorithms and statistical analysis to discover significant trends and patterns.

[1142] Examples:

[1143] The server performs a regression analysis to investigate the impact of educational investment on economic growth.

[1144] The server performs clustering to group data that have similar policy effects.

[1145] Policy generation means

[1146] The server then performs scenario simulations based on the analysis results to generate optimal policies. Scenario simulations are mock experiments that create hypothetical policy scenarios and evaluate their effects and impacts.

[1147] Examples:

[1148] The server simulates different education policy scenarios and identifies those that contribute most to economic growth.

[1149] The server compares multiple policy proposals and selects the most appropriate one.

[1150] Transparency confirmation method

[1151] The server checks the transparency of the generated policy proposals and compares them with lobby databases of relevant parties, etc. This measure is intended to prevent fraud and collusion.

[1152] Examples:

[1153] The server investigates the ties between each submitted policy proposal and the relevant companies and organizations.

[1154] The server checks the lobbying records to detect possible fraud.

[1155] Budget Control Tools

[1156] The server automatically manages budgets related to legislators and elections and extracts expenses that can be reduced. This function reduces wasteful spending and enables efficient budget management.

[1157] Examples:

[1158] The server analyzes traditional travel and accommodation expenses for lawmakers and makes proposals for cost reduction through digital meetings.

[1159] The server reviews and reduces unnecessary expense items.

[1160] Cloud Access Methods

[1161] Users can access the cloud server via their devices and check the progress of policies and proposals in real time, which will increase the transparency of the policy-making process and boost public trust.

[1162] Examples:

[1163] Users can view the latest policy proposals using a web browser or a dedicated app.

[1164] Users have the ability to directly input their opinions and feedback on policies.

[1165] This system will dramatically improve the speed and transparency of policy formulation and the accuracy of budget management, enabling efficient policy implementation.

[1166] The processing flow will be explained below.

[1167] Step 1:

[1168] The server collects data related to politics, economics, education, childcare, culture, science, and law from around the world, from public databases, research papers, news articles, etc. The server uses APIs to access various databases and periodically retrieve the latest information.

[1169] Step 2:

[1170] The server pre-processes the collected data, removing incomplete data and noise and normalizing the data to make it easier to analyze. During this process, necessary data is supplemented and organized as time series data or category data.

[1171] Step 3:

[1172] The server analyzes the pre-processed data, using machine learning algorithms and statistical analysis techniques to uncover important trends and patterns in the data. Specifically, it performs regression analysis to examine the impact of education investment on economic growth, and clusters data to group together data with similar policy effects.

[1173] Step 4:

[1174] The server performs scenario simulations based on the analysis results, creating hypothetical policy scenarios and predicting their effects and impacts. As a result of the simulations, optimal policy proposals are automatically generated.

[1175] Step 5:

[1176] The server checks the generated policy proposals against a lobbying database of relevant parties to ensure transparency. It also checks the relationships between each part of the submitted policy proposal and related companies and organizations, and compares them with lobbying records to detect possible irregularities.

[1177] Step 6:

[1178] The server will keep a detailed record of the decision-making process and generate transparency reports that will be accessible to third parties and stored in an audit-ready format.

[1179] Step 7:

[1180] The server automatically manages budgets related to legislators and elections. It analyzes each expense item and identifies expenses that can be reduced. It reviews traditional legislator travel and accommodation expenses and proposes cost reductions, such as transitioning to digital meetings.

[1181] Step 8:

[1182] Users can access the cloud server via their devices to check the progress of policies and proposals in real time. They can also use a web browser or a dedicated app to view the latest policy proposals and directly enter their opinions and feedback as needed.

[1183] This series of steps will dramatically improve the speed and transparency of policy formulation and the accuracy of budget management, resulting in efficient policy implementation.

[1184] Example 1

[1185] 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."

[1186] In modern society, a huge amount of data is generated in a wide range of fields, including politics, economics, education, childcare, culture, science, and law. However, it is difficult to efficiently collect and analyze this data and generate optimal policies. Ensuring transparency in the policy generation process and efficient budget management are also major challenges. Existing systems have difficulty comprehensively processing these complex processes, resulting in delays in policy implementation and the risk of fraud.

[1187] 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.

[1188] In this invention, the server includes: means for periodically collecting data related to politics, economics, education, childcare, culture, science, and law from around the world; means for pre-processing the collected data, removing noise and normalizing the data, and analyzing it; means for conducting scenario simulations based on the analysis results and automatically generating optimal policies; means for confirming the transparency of the generated policy proposals and comparing them with databases of relevant parties; means for automatically managing budgets related to lawmakers and elections and extracting expenses that can be reduced; and means for users to access the system on the cloud and check policy progress and proposals in real time. This enables efficient data collection and analysis, ensures transparency in policy generation, and improves the efficiency of budget management.

[1189] "Data Collection Tools" refers to a function that periodically collects data related to politics, economics, education, childcare, culture, science, and law from public databases, research papers, news articles, etc. from around the world.

[1190] "Data analysis means" refers to a function that pre-processes collected data, removes noise, normalizes data, and analyzes it using machine learning algorithms and statistical methods.

[1191] "Scenario simulation means" is a function that creates hypothetical policy scenarios based on the results of data analysis and conducts simulation experiments to evaluate their effects and impacts.

[1192] The "policy generation means" is a function that automatically generates optimal policies based on the results of scenario simulations.

[1193] The "transparency verification means" is a function that verifies the transparency of generated policy proposals by comparing them with the databases of relevant parties in order to prevent fraud and collusion.

[1194] "Budget management tools" are functions that automatically manage budgets related to legislators and elections and extract expenses that can be reduced.

[1195] "Cloud access means" is a function that allows users to access a system on the cloud via their terminal and check policy progress and proposals in real time.

[1196] This invention provides a system for collecting, analyzing, policy-making, transparency checks, budget management, and cloud accessing data related to politics, economics, education, childcare, culture, science, and law from around the world. This system aims to speed up policy-making, prevent fraud, efficiently manage budgets, and improve transparency.

[1197] Data collection

[1198] The server periodically collects data related to politics, economics, education, childcare, culture, science, law, etc. from public databases, research papers, news articles, etc. For example, it uses the World Bank's API to obtain economic statistics and stores them in the system. In addition, it uses web crawling technology (e.g., Beautiful Soup) to download the latest research papers and news articles and store them in the database.

[1199] Data analysis

[1200] The server preprocesses the collected data (e.g., noise removal, data normalization) and analyzes it using machine learning algorithms and statistical analysis, using libraries such as Pandas and Scikit-learn. For example, it performs regression analysis to investigate the impact of education investment on economic growth. It also performs clustering to group data with similar policy effects.

[1201] policy generation

[1202] The server performs scenario simulations based on the analysis results and automatically generates optimal policies. Scenario simulations create different policy scenarios and evaluate their effects and impacts. For example, different education policy scenarios can be simulated to identify the one that contributes most to economic growth. The optimal policy proposals are compiled in document form and submitted to the relevant parties.

[1203] Transparency confirmation

[1204] The server checks the transparency of the generated policy proposals and compares them with relevant databases. To prevent corruption and collusion, it checks the relationships with companies and organizations and compares them with lobbying records. For example, it checks the relationships with relevant companies and organizations for each part of the submitted policy proposal.

[1205] Budget Management

[1206] The server automatically manages budgets related to legislators and elections, extracting expenses that can be reduced. This reduces wasteful spending and enables efficient budget management. For example, it analyzes traditional legislators' travel and accommodation expenses and proposes cost reductions through digital meetings.

[1207] Cloud Access

[1208] Users can access the cloud server via their devices and check the progress of policies and proposals in real time. This increases transparency in the policy-making process and strengthens public trust. Users can view the latest policy proposals using a web browser or a dedicated app, and can directly enter their opinions on policies using the feedback function.

[1209] Examples and prompts

[1210] A specific example would be the analysis and transparency check of education policies.

[1211] Examples:

[1212] 1. The server collects economic statistical data on education from the World Bank database via an API.

[1213] 2. The server analyzes the collected data and uses regression analysis to investigate the impact of educational investment on economic growth.

[1214] 3. Based on the analysis results, the server simulates multiple educational policy scenarios and generates the optimal policy.

[1215] 4. The server investigates the relationships of related companies and organizations with the generated educational policy proposals using transparency verification measures.

[1216] Example prompt sentence:

[1217] "How can I conduct a regression analysis using the latest World Bank data on the relationship between education investment and economic growth, create optimal education policy scenarios based on the results, and ensure the transparency of the policy proposals generated to eliminate the possibility of fraud?"

[1218] The system enables efficient data collection and analysis, ensures transparency in policy development, streamlines budget management, and allows users to monitor progress in real time.

[1219] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1220] Step 1:

[1221] Data collection

[1222] The server periodically collects data related to politics, economics, education, childcare, culture, science, law, etc. from around the world. Input data is obtained from public databases (e.g., the World Bank), research papers, news articles, etc. Specifically, the server uses an API to request economic statistical data from the World Bank and stores the received data. It also uses web crawling technology (e.g., Beautiful Soup) to download research papers and news articles and store them in a database. As output, the collected raw data is stored in a database.

[1223] Step 2:

[1224] Data Preprocessing

[1225] The server pre-processes the collected data. The input data is the raw data collected in step 1. Specifically, the server converts it into a data frame using the Pandas library, removes inconsistencies and missing values, and normalizes and denoises the data. The output is a clean, normalized dataset.

[1226] Step 3:

[1227] Data analysis

[1228] The server analyzes the preprocessed data. The input data is the dataset preprocessed in step 2. Specifically, the server uses the Scikit-learn library to run machine learning algorithms (e.g., regression analysis and clustering). For example, regression analysis is used to investigate the impact of educational investment on economic growth. The output is trends and patterns as analytical results, as well as the results of various models.

[1229] Step 4:

[1230] policy generation

[1231] The server performs scenario simulations based on the analysis results and generates optimal policies. The input data are the analysis results obtained in step 3. Specifically, the server uses a scenario simulation tool to generate hypothetical policy scenarios and evaluate their effects and impacts. For example, it simulates different education policy scenarios to identify the policy that contributes most to economic growth. The output is an optimal policy proposal.

[1232] Step 5:

[1233] Transparency confirmation

[1234] The server checks the transparency of the generated policy proposal. The input data is the policy proposal generated in step 4. Specifically, the server checks the data against the databases of relevant parties to check for possible fraud or collusion. For example, it investigates relationships with companies and organizations and compares them with lobbying records. The output is the transparency check result, and a warning is issued if there is any possibility of fraud.

[1235] Step 6:

[1236] Budget Management

[1237] The server automatically manages budgets related to legislators and elections, extracting expenses that can be reduced. The input data is past budget data. Specifically, the server analyzes legislators' past travel and accommodation expenses, etc., and proposes cost reductions through digital meetings. It also reviews unnecessary expense items and generates reduction proposals. The output is an efficient budget proposal.

[1238] Step 7:

[1239] Cloud Access

[1240] Users access the cloud server via their devices and check policy progress and proposals in real time. The input data is policy information stored on the cloud. Specifically, users launch a web browser or a dedicated app, enter their authentication information, and log in. They can then check the latest policy information and progress on the dashboard screen. They can also use the feedback function to enter their opinions and proposals regarding policies. The output is policy information and feedback updated in real time.

[1241] (Application example 1)

[1242] 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."

[1243] Modern society requires efficient collection and analysis of data related to politics, economy, education, childcare, culture, science, and law from around the world, ensuring transparency and improving budget management. However, there is a lack of systems that can systematically collect and analyze this data and efficiently generate policies, verify transparency, and manage budgets. Similar challenges exist in factory operations, where it is difficult to utilize data from sensors and equipment to optimize manufacturing processes, prevent fraud, and improve budget management efficiency.

[1244] 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.

[1245] In this invention, the server includes: means for collecting data related to politics, economics, education, childcare, culture, science, and law from around the world; means for analyzing the collected data and discovering important trends and patterns; means for conducting scenario simulations based on the analysis results to generate optimal policies; means for verifying the transparency of the generated policy proposals and comparing them with lobby databases of relevant parties; means for automatically managing budgets related to legislators and elections and extracting cost savings; means for users to access the cloud server and check policy progress and proposals in real time; means for collecting data from sensors and devices in factories and analyzing the data to optimize manufacturing processes; means for conducting simulations of manufacturing processes and generating optimal manufacturing plans; means for verifying the transparency of manufacturing plans and preventing fraud; and means for automatically managing manufacturing-related budgets and proposing cost savings. This enables more efficient and transparent policy generation in all fields, including politics, economics, and education, as well as optimizing manufacturing processes, preventing fraud, and streamlining budget management in factory operations.

[1246] "Collection tools" are devices and processes for systematically collecting data from sensors and equipment in politics, economics, education, childcare, culture, science, law, and factories around the world.

[1247] "Analysis tools" are techniques and methods for analyzing collected data and discovering trends and patterns in the data.

[1248] "Scenario simulation" is a method for generating hypothetical scenarios based on analysis results and evaluating the effects and impacts of those scenarios.

[1249] A "policy generator" is an algorithm and process for creating optimal policies from the results of scenario simulations.

[1250] "Transparency verification means" refers to a mechanism or method for verifying the transparency of generated policy proposals and manufacturing plans and preventing fraudulent activities.

[1251] "Budget control tools" are technologies and processes for automatically managing legislative and election-related budgets and manufacturing-related budgets and extracting expenses and costs that can be reduced.

[1252] "Cloud access means" refers to the technology and process that allows users to access a server on the cloud via the Internet and check policies, the progress of manufacturing processes, and proposals in real time.

[1253] This invention is a comprehensive system that systematically collects and analyzes data related to politics, economics, education, childcare, culture, science, and law from around the world, as well as data from sensors and equipment in factories, to generate policies, optimize manufacturing processes, prevent fraud, and manage budgets.

[1254] The server first uses APIs and web crawling technology to collect a wide range of data from public databases, news articles, research papers, etc. For example, it can retrieve economic statistics from the World Bank database or download the latest research papers. It can also collect real-time data from sensors and equipment in factories.

[1255] The collected data undergoes preprocessing such as noise removal and data normalization, and is then analyzed using machine learning algorithms and statistical analysis. Specifically, regression analysis and clustering are used to discover important trends and patterns. Regression analysis is used to investigate the impact of education investment on economic growth, and data with similar policy effects are grouped together.

[1256] Based on the analysis results, the server runs scenario simulations, which evaluate the effects of different policies and manufacturing scenarios in a virtual environment and generate optimal policies and manufacturing plans. Scenario simulations are conducted to select the optimal training policies, and simulations are conducted to select the optimal manufacturing plans.

[1257] The policy proposals and production plans generated are cross-checked with relevant databases to ensure transparency. Each part of the policy proposal is checked for ties to relevant companies and organizations and cross-checked with lobbying records to prevent corruption and collusion.

[1258] The server also automatically manages budgets related to legislators and elections, as well as manufacturing, and identifies expenses and costs that can be reduced. For example, it can analyze legislators' travel and accommodation expenses and propose cost reductions through digital meetings.

[1259] Users can access the cloud server using a web browser or a dedicated app to check the progress of policies, manufacturing process status, and proposals in real time. They can also directly input their opinions and feedback on policies.

[1260] The implementation of this system uses the following hardware and software:

[1261] Hardware: Factory sensors, smartphones, tablets, robotic arms

[1262] Software: Python environment, Pandas, NumPy, scikit-learn, API access

[1263] For example, an application used on a factory production line collects data in real time from all the equipment in the factory and sends it to a server on the cloud. The data is analyzed, and suggestions for optimizing the manufacturing process and reducing costs are sent to the user's smartphone or tablet.

[1264] Example prompts to input to a generative AI model:

[1265] "Design an integrated manufacturing management application for factory robots. This application will collect data from sensors and robots, perform data analysis, optimize the manufacturing process, ensure transparency, and manage budgets. Specific functions will include data collection and pre-processing, data analysis using machine learning, scenario simulation, transparency, and budget management."

[1266] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1267] Step 1:

[1268] The server collects data from public databases, news articles, and research papers around the world. Specifically, it uses APIs to retrieve economic statistics from the World Bank database and web crawling technology to download the latest research papers. It also collects data in real time from sensors and equipment within the factory. Inputs include data from APIs and sensors, and outputs are the collected data.

[1269] Step 2:

[1270] The server preprocesses the collected data. Preprocessing includes noise removal and data normalization, such as imputing missing values ​​and removing outliers. The input includes the raw data collected in step 1, and the output is the preprocessed, clean data.

[1271] Step 3:

[1272] The server analyzes the preprocessed data. This analysis uses machine learning algorithms and statistical analysis. For example, regression analysis can be used to investigate the impact of education investment on economic growth, or clustering can be used to group data with similar policy effects. The input includes the preprocessed data, and the output is trends and patterns resulting from the analysis.

[1273] Step 4:

[1274] The server performs scenario simulations based on the analysis results. It virtually generates different policy and manufacturing scenarios and evaluates their effects and impacts. For example, it simulates different education policy scenarios to identify the optimal one. The input includes the analysis results, and the output is the evaluation results of the scenarios.

[1275] Step 5:

[1276] The server checks the transparency of the generated policy proposals and manufacturing plans, checking them against lobby databases and other related parties to check for signs of fraud or collusion. The inputs include the generated scenarios and information from related databases, and the output is a transparency check report.

[1277] Step 6:

[1278] The server automatically manages budgets related to legislators, elections, and manufacturing. It analyzes historical budget data to identify expenses and costs that can be reduced. For example, it analyzes historical legislators' travel and accommodation expenses and proposes cost reductions through digital meetings. The input includes historical budget data, and the output is budget reduction proposals.

[1279] Step 7:

[1280] Users can access the cloud server using a web browser or a dedicated app to check the progress and suggestions of policies and manufacturing processes in real time. They can also directly input their opinions and feedback on policies. The input includes the user's access information and feedback, and the output is a display of real-time progress and suggestions.

[1281] This system will systematically collect and analyze data from around the world in fields such as politics, economics, and education, making it possible to improve the efficiency and transparency of policymaking.It will also help optimize manufacturing processes, prevent fraud, and streamline budget management in factory operations.

[1282] 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.

[1283] This invention provides a comprehensive system that combines data collection and analysis related to politics, economics, education, childcare, culture, science, and law from around the world, policy generation, transparency confirmation, budget management, cloud access, and an emotion engine that recognizes user emotions. This system has the functions to speed up policymaking, prevent fraud, efficiently manage budgets, improve transparency, and generate policies that reflect user emotions.

[1284] Each function of the present invention will now be described in detail.

[1285] Data collection methods

[1286] The server periodically collects data related to politics, economics, education, childcare, culture, science, law, and other areas from public databases, research papers, news articles, etc. This information includes a wide range of data, such as economic statistics, educational achievements, and scientific research progress.

[1287] Examples:

[1288] The server uses APIs to retrieve economic statistics from the World Bank database and store them in the system.

[1289] The server uses web crawling technology to download the latest research papers and store them in a database.

[1290] Data Analysis Methods

[1291] The server pre-processes the collected data (e.g., noise removal, data normalization) and analyzes it using machine learning algorithms and statistical analysis to discover significant trends and patterns.

[1292] Examples:

[1293] The server performs a regression analysis to investigate the impact of educational investment on economic growth.

[1294] The server performs clustering to group data that have similar policy effects.

[1295] Policy generation means

[1296] The server then performs scenario simulations based on the analysis results to generate optimal policies. Scenario simulations are mock experiments that create hypothetical policy scenarios and evaluate their effects and impacts.

[1297] Examples:

[1298] The server simulates different education policy scenarios and identifies those that contribute most to economic growth.

[1299] The server compares multiple policy proposals and selects the most appropriate one.

[1300] Transparency confirmation method

[1301] The server checks the generated policy proposals against a lobbying database of relevant parties to ensure transparency. It also checks the relationships between each part of the submitted policy proposal and related companies and organizations, and compares them with lobbying records to detect possible irregularities.

[1302] Examples:

[1303] The server investigates the ties between each submitted policy proposal and the relevant companies and organizations.

[1304] The server checks the lobbying records to detect possible fraud.

[1305] Budget Control Tools

[1306] The server automatically manages budgets related to legislators and elections and extracts expenses that can be reduced. This function reduces wasteful spending and enables efficient budget management.

[1307] Examples:

[1308] The server analyzes traditional travel and accommodation expenses for lawmakers and makes proposals for cost reduction through digital meetings.

[1309] The server reviews and reduces unnecessary expense items.

[1310] Cloud Access Methods

[1311] Users can access the cloud server via their devices and check the progress of policies and proposals in real time, which will increase the transparency of the policy-making process and boost public trust.

[1312] Examples:

[1313] Users can view the latest policy proposals using a web browser or a dedicated app.

[1314] Users have the ability to directly input their opinions and feedback on policies.

[1315] Emotion Engine

[1316] The server is equipped with an emotion engine that recognizes the user's emotions, analyzes the user's emotions based on the collected data, and reflects them in policy proposals.

[1317] Examples:

[1318] As users view policy proposals, the server collects their reactions using cameras and microphones and performs sentiment analysis.

[1319] The server immediately improves the content and presentation of policy proposals based on the results of user sentiment analysis.

[1320] Utilizing emotional feedback

[1321] The server uses an emotion engine to collect and analyze users' reactions to policy proposals in real time, allowing for a quantitative evaluation of the level of support for the policy.

[1322] Examples:

[1323] The server calculates the support and satisfaction of the policy based on the results of the user's sentiment analysis and stores the results in a database.

[1324] The server will use this data as a reference for future policy planning.

[1325] This system will dramatically improve the speed, transparency, and accuracy of policymaking, and will also enable more realistic and popular policy proposals that reflect the feelings of users.

[1326] The processing flow will be explained below.

[1327] Step 1:

[1328] The server collects data related to politics, economics, education, childcare, culture, science, and law from around the world, from public databases, research papers, news articles, etc. It uses APIs to access various databases and periodically retrieve the latest information.

[1329] Step 2:

[1330] The server pre-processes the collected data, specifically removing incomplete data and noise, normalizing the data, supplementing it with necessary data, and organizing it as time series data or categorical data.

[1331] Step 3:

[1332] The server analyzes the pre-processed data, using machine learning algorithms and statistical analysis techniques to uncover significant trends and patterns in the data. For example, it performs regression analysis to analyze the impact of education investment on economic growth.

[1333] Step 4:

[1334] The server performs scenario simulations based on the analysis results, creates hypothetical policy scenarios, and predicts their effects and impacts. As a result of the simulation, optimal policy proposals are automatically generated.

[1335] Step 5:

[1336] The server checks the generated policy proposals against a lobbying database of relevant parties to ensure transparency. It also checks the relationships between each part of the submitted policy proposal and related companies and organizations, and compares them with lobbying records to detect possible irregularities.

[1337] Step 6:

[1338] The server will keep a detailed record of the decision-making process and generate transparency reports that will be accessible to third parties and stored in an audit-ready format.

[1339] Step 7:

[1340] The server automatically manages budgets related to legislators and elections. It analyzes each expense item and identifies expenses that can be reduced. It reviews traditional legislator travel and accommodation expenses and proposes cost reductions, such as transitioning to digital meetings.

[1341] Step 8:

[1342] Users can access the cloud server via their devices to check the progress of policies and proposals in real time. They can also use a web browser or a dedicated app to view the latest policy proposals and directly enter their opinions and feedback as needed.

[1343] Step 9:

[1344] The server analyzes users' reactions using an emotion engine to recognize their emotions. Specifically, it collects emotional data using a camera and microphone while users are viewing policy proposals, and performs emotion analysis in real time.

[1345] Step 10:

[1346] The server improves policy proposals based on the results of sentiment analysis. It analyzes user reactions and instantly adjusts policy content and presentation methods. The results of this analysis are saved as reference data for future policy formulation.

[1347] This series of steps will improve the speed and transparency of policymaking, the accuracy of budget management, and lead to realistic and supportable policy proposals that reflect user sentiment.

[1348] Example 2

[1349] 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."

[1350] Conventional policymaking systems have limitations in terms of policy transparency, efficient budget management, and the ability to reflect user opinions. Furthermore, they lack centralized data collection and analysis, resulting in a lack of speed and accuracy in policy decisions. Furthermore, they lack the functionality to collect and reflect user sentiment and opinions in real time, making it difficult to generate realistic and popular policies.

[1351] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting data related to politics, economy, education, childcare, culture, science, and law from around the world; means for pre-processing and analyzing the collected data to discover important trends and patterns; means for conducting scenario simulations based on the analysis results to generate optimal policies; means for verifying the transparency of the generated policy proposals and comparing them with lobby databases of relevant parties; means for automatically managing budgets related to legislators and elections and extracting expenses that can be reduced; means for users to access a server on the cloud and check policy progress and proposals in real time; an emotion engine that recognizes and analyzes user emotions and reflects them in policy proposals; and means for collecting and analyzing user emotions regarding policy proposals using the emotion engine and utilizing them in future policy planning. This enables faster policy planning, improved transparency, more efficient budget management, and the generation of realistic and supportable policies that reflect user emotions.

[1352] "Global political, economic, educational, childcare, cultural, scientific and legal data" refers to information on various areas around the world collected from governments, economic institutions, educational institutions, childcare-related organizations, cultural institutions, scientific research institutions, legal institutions, etc.

[1353] "Means of collecting data" refers to a method of automatically obtaining information from public data on the Internet or dedicated databases using web crawling technology or API access.

[1354] "Preprocessing" refers to the process of performing preprocessing such as noise removal, normalization, and missing value completion on collected data, and is a preparatory step to improve the accuracy of analysis.

[1355] "Means of analysis to discover important trends and patterns" refers to techniques that use machine learning algorithms and statistical analysis methods to extract significant information from large amounts of data and reveal trends and patterns.

[1356] "A means of conducting scenario simulations and generating optimal policies" is a technology that sets up multiple hypothetical policy scenarios, evaluates and compares their effects and impacts, and automatically generates the most effective policies.

[1357] "Means to verify transparency and compare with lobbying databases of relevant parties" refers to a method of making the background and impact of generated policy proposals transparent and comparing them with existing lobbying databases to detect irregularities and bias.

[1358] "Means for automatically managing budgets and extracting expenses that can be reduced" refers to technology that centrally manages budgets related to lawmakers and elections through a system, uses digital analysis to identify wasteful spending, and proposes reductions.

[1359] "A means of accessing a server on the cloud and checking policy progress and proposals in real time" is a function that allows users to connect to the cloud via the Internet and instantly view and check the latest policy information and proposals.

[1360] The "emotion engine" is a system that analyzes a user's facial expressions and voice data to assess their emotional state and incorporates this information into the policy generation process.

[1361] "Means of collecting and analyzing emotions and utilizing them in future policy formulation" refers to a technology that uses an emotion engine to collect and analyze users' reactions to policy proposals, stores the results in a database, and reflects them in the generation of future policies.

[1362] This invention is a comprehensive system that collects and analyzes data related to politics, economy, education, childcare, culture, science, and law from around the world, generates optimal policies, checks transparency, streamlines budget management, and reflects user sentiment. The system of this invention is composed of three main entities: a server, a terminal, and a user.

[1363] Data collection methods

[1364] The server periodically collects data related to politics, economy, education, childcare, culture, science, and law from around the world using web crawlers, API access technologies (e.g., World Bank API), and downloads from databases. Examples of data collected include economic statistics, educational achievements, and scientific research progress.

[1365] Examples:

[1366] The server uses the World Bank API to obtain GDP data for each country.

[1367] The server uses web crawling techniques to download new research papers and store them in a database.

[1368] Data Analysis Methods

[1369] The server pre-processes the collected data (denoising, normalising the data, etc.) and then analyses it using machine learning algorithms and statistical analysis techniques, including regression analysis and clustering, to discover significant trends and patterns.

[1370] Examples:

[1371] The server uses regression analysis to investigate the impact of educational investment on economic growth.

[1372] The server performs clustering to group data that have similar policy effects.

[1373] Policy generation means

[1374] The server performs scenario simulation based on the analysis results to generate optimal policies. Scenario simulation is a method of setting up multiple hypothetical scenarios and evaluating and comparing their effects.

[1375] Examples:

[1376] The server hypothesizes different scenarios for education policy and evaluates the impact of each scenario on economic growth.

[1377] The server generates the most effective policy based on the simulation results.

[1378] Transparency confirmation method

[1379] The server checks the transparency of the generated policy proposals and checks them against a lobby database to detect possible fraud.

[1380] Examples:

[1381] The server checks whether the policy proposals are biased toward any particular company or organization.

[1382] The server checks the lobbying records to detect possible fraud.

[1383] Budget Control Tools

[1384] The server automatically manages budgets related to legislators and elections and identifies expenses that can be reduced.

[1385] Examples:

[1386] The server analyzes past travel and accommodation expenses for lawmakers and makes proposals for cost reduction through digital meetings.

[1387] The server reviews and reduces unnecessary expense items.

[1388] Cloud Access Methods

[1389] Users can access the cloud server using their devices and check policy progress and proposals in real time.

[1390] Examples:

[1391] Users can view the latest policy proposals using a web browser or a dedicated app.

[1392] Users can directly input their opinions and feedback on policies.

[1393] Emotion Engine

[1394] The server is equipped with an emotion engine that recognizes the user's emotions by analyzing the user's facial expressions and voice data to evaluate their emotional state.

[1395] Examples:

[1396] The server uses cameras and microphones to collect users' reactions as they view policy proposals.

[1397] The server refines the content of policy proposals based on the results of the sentiment analysis.

[1398] Utilizing emotional feedback

[1399] The server uses an emotion engine to collect and analyze users' emotions regarding policy proposals and use them in future policy planning.

[1400] Examples:

[1401] The server aggregates users' emotional data and evaluates the support and satisfaction of the policy.

[1402] The server uses the evaluation results in the next policy generation.

[1403] Prompt Sentence Examples

[1404] "Analyze the effects of education policies and see the correlation with economic growth."

[1405] "Perform user sentiment analysis on the latest policy proposals and refine them based on their feedback."

[1406] In this way, the system of the present invention realizes rapid policy formulation, improved transparency, more efficient budget management, and the generation of realistic and supportable policies that reflect the feelings of users.

[1407] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1408] Step 1:

[1409] The server collects data related to politics, economics, education, childcare, culture, science, and law from around the world using web crawling technology and API access (e.g., World Bank API). The input is various public databases and news sites, and the output is the collected dataset. Specifically, it periodically sends API requests to retrieve and store the latest data.

[1410] Step 2:

[1411] The server pre-processes the collected data. The input is the raw data collected in step 1, and the output is clean data that has been normalized and noise removed. Specific operations include detecting and removing outliers, filling in missing values, and standardizing the data format.

[1412] Step 3:

[1413] The server uses the cleaned data to run machine learning algorithms and statistical analysis. The input is the cleaned data processed in step 2, and the output is the analysis results (discovery of trends and patterns). As a specific example, regression analysis is used to analyze the impact of education investment on economic growth, and clustering is used to group data with similar policy effects.

[1414] Step 4:

[1415] The server runs a scenario simulation based on the analysis results and generates optimal policies. The input is the analysis results from Step 3, and the output is an optimal policy proposal. Specifically, multiple hypothetical scenarios are set up, and the most effective policy is selected by evaluating and comparing their effects.

[1416] Step 5:

[1417] The server compares the generated policy proposal with the lobby database to verify its transparency. The input is the generated policy proposal, and the output is the transparency evaluation result. Specifically, it checks whether the proposed policy proposal is biased toward specific companies or organizations.

[1418] Step 6:

[1419] The server automatically manages budgets related to legislators and elections and extracts expenses that can be reduced. The input is budget data related to legislators and elections, and the output is reduction proposals. As a specific example, it analyzes data on past legislator travel and accommodation expenses and proposes cost reductions through digital meetings.

[1420] Step 7:

[1421] Users use their devices to access the cloud server and check the progress and proposals of policies in real time. The input is a request to access the cloud database, and the output is the latest policy information. Specifically, users use a web browser or a dedicated app to view the latest policy proposals and provide feedback.

[1422] Step 8:

[1423] The server activates an emotion engine that recognizes the user's emotions and analyzes the user's facial expressions and voice data. The input is the user's reaction data, and the output is the emotion analysis results. As a specific example, the server analyzes the user's facial expressions and voice tone in real time while viewing policy proposals to evaluate their emotional state.

[1424] Step 9:

[1425] The server improves policy proposals based on the results of user sentiment analysis and uses them in future policy planning. The input is the sentiment analysis results, and the output is improved policy proposals and data to be used in the next policy generation. Specifically, it automatically adjusts the content and presentation method to elicit a positive response from users, and stores the evaluation results in a database.

[1426] Through these processing steps, rapid policy formulation, increased transparency, efficient budget management, and the generation of realistic and supportable policies that reflect user sentiment are realized.

[1427] (Application example 2)

[1428] 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."

[1429] In conventional policymaking systems, data collection and analysis are localized, making it difficult to comprehensively analyze a wide range of data from around the world. Furthermore, the transparency of the policies generated and the efficiency of budget management are insufficient, and there are also insufficient mechanisms to prevent fraud and reflect public sentiment in the actual policy generation. Furthermore, there is no optimized method for generating policies in terms of security measures, and improvements in overall reliability and transparency are needed.

[1430] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting data related to politics, economics, education, childcare, culture, science, and law from around the world; means for analyzing the collected data and discovering important trends and patterns; means for conducting scenario simulations based on the analysis results to generate optimal policies; means for verifying the transparency of the generated policy proposals and comparing them with lobby databases of relevant parties; means for automatically managing budgets related to legislators and elections and extracting cost savings; means for users to access a cloud-based server and check policy progress and proposals in real time; means for recognizing user sentiment and improving policy proposals based on the analysis results; and means for collecting security-related data and generating security policies. This dramatically improves the speed, transparency, and accuracy of policymaking, enabling more realistic and supportable policy proposals that reflect user sentiment. Furthermore, generating optimal policies for security measures also improves overall reliability and transparency.

[1431] "Political, economic, educational, childcare, cultural, scientific and legal data" refers to the totality of information collected from governments, economic institutions, educational institutions, childcare support groups, cultural institutions, scientific research institutions, legal organizations and other sources around the world.

[1432] "Means of collecting data" refers to systems that automatically collect data from specific sources using APIs, web crawling, scraping technologies, etc.

[1433] "Methods for discovering significant trends and patterns" refers to techniques that use machine learning algorithms and statistical analysis to extract meaningful trends and patterns from large amounts of data.

[1434] "Means for performing scenario simulation" refers to algorithms and software that quantitatively evaluate hypothetical policy scenarios based on collected data and analysis results and simulate their effects.

[1435] "Means to confirm the transparency of the generated policy proposals" refers to the process of comparing the generated policy proposals with lobby databases, etc., to verify the possibility of fraud.

[1436] "Automated budget management tools" refers to software and algorithms that digitally manage legislative and election-related budgets, calculate efficiency, and reduce waste.

[1437] "Means for accessing servers in the cloud" refers to the interfaces and protocols for accessing and using data and functions stored in the cloud through an Internet connection.

[1438] "Means for recognizing user emotions" refers to an emotion engine that uses sensors such as cameras and microphones to analyze emotions from the user's facial expressions and voice.

[1439] "Means of collecting security-related data" refers to systems that collect security-related information such as cyberattack trends, vulnerability information, and changes in laws.

[1440] "Means for generating security policies" refers to a system that analyzes collected security-related data and automatically proposes optimal security measures and policies.

[1441] "Means for improving policy proposals that reflect user emotions" refers to a function for dynamically modifying and improving the content of policy proposals based on user emotion data analyzed by the emotion engine.

[1442] This is a comprehensive system that collects and analyzes data related to politics, economy, education, childcare, culture, science, and law from around the world, generates optimal policies, ensures transparency, and efficiently manages budgets. It also has the ability to recognize users' emotions and improve policy proposals based on the analysis results.

[1443] The server contains the following main means:

[1444] 1. Data Collection Methods

[1445] The server uses APIs and web crawling technology to collect data related to politics, economics, education, childcare, culture, science, and law from around the world. For example, you can use APIs to obtain economic statistics or web crawling technology to download the latest research papers.

[1446] 2. Data analysis methods

[1447] The collected data is then standardized and analyzed using machine learning algorithms and statistical analysis to uncover important trends and patterns, allowing, for example, regression analysis to explore the impact of education investment on economic growth.

[1448] 3. Policy generation instruments

[1449] The server then performs scenario simulations based on the analysis results to generate optimal policies. This involves using an algorithm to generate multiple policy proposals and simulate their effects. For example, it simulates different education policy scenarios to identify the one that contributes most to economic growth.

[1450] 4. Transparency confirmation method

[1451] The generated policy proposals are checked against a lobby database to ensure transparency about the policy proposals, investigate ties with relevant companies and organizations, and detect any potential irregularities.

[1452] 5. Budgetary Control Tools

[1453] The server automatically manages budgets related to legislators and elections, using algorithms that analyze past expenditure data and identify potential cost savings, such as travel and accommodation costs that can be reduced by implementing digital meetings.

[1454] 6. Cloud Access Methods

[1455] Users can access the cloud server through a web browser or a dedicated app to check the progress and proposals of policies in real time, and can also directly enter their opinions and feedback on policies.

[1456] 7. Emotion recognition means

[1457] It is equipped with an emotion engine that recognizes the user's emotions, and uses a camera and microphone to analyze the user's facial expressions and voice, allowing it to dynamically modify and improve policy proposals based on the user's emotions.

[1458] 8. Security Policy Generation Method

[1459] The server collects and analyzes security-related data (e.g., cyberattack trends, vulnerability information, legal changes, etc.), and generates optimal security policies based on the results, ensuring transparency.

[1460] Specific examples of the hardware and software used include machine learning algorithms implemented in the Python language, a RESTful API used for data collection, Pandas and Scikit-learn used for data analysis, and AWS, Azure, and Google Cloud Platform as cloud servers.

[1461] As a concrete example, the following prompt sentence could be input to a generative AI model:

[1462] "Please build a system that collects and analyzes security data from around the world, generates optimal security policies, ensures transparency, and manages budgets. Also, please add a function to analyze user sentiment and reflect it in policy proposals."

[1463] This will dramatically improve the speed, transparency, and accuracy of policymaking, and will enable more realistic and supportable policy proposals that reflect user sentiment. It will also improve overall reliability and transparency by generating optimal policies for security measures.

[1464] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1465] Step 1:

[1466] The server uses APIs and web crawling technology to collect data related to politics, economics, education, childcare, culture, science, and law from around the world. Specifically, it uses APIs to obtain economic statistics and web crawling technology to collect the latest research papers. The input data is public data and web pages related to each field, and the output data is structured data containing that information. Data collection is carried out regularly to keep the content up to date.

[1467] Step 2:

[1468] The server pre-processes the collected data. This involves standardizing the data format and removing missing values ​​and noise. For example, data provided in different formats is standardized into CSV format and missing values ​​are imputed. The input data is the raw data collected in step 1, and the output data is the normalized, clean data.

[1469] Step 3:

[1470] The server analyzes the pre-processed data. This analysis uses machine learning algorithms and statistical analysis to discover important trends and patterns. Specifically, it performs regression analysis and clustering to examine the impact of education investment on economic growth. The input data is the pre-processed data, and the output data is the analysis results and trend information.

[1471] Step 4:

[1472] The server performs a scenario simulation based on the analysis results. In the scenario simulation, different policy scenarios are generated and their effects are evaluated. For example, multiple education policy scenarios are generated and it is determined which scenario will contribute most to economic growth. The input data are the analysis results obtained in Step 3, and the output data are the results of the simulation.

[1473] Step 5:

[1474] The server checks the transparency of the generated policy proposals. Specifically, it checks the policy proposals against the lobby database to detect possible fraud. The input data is the policy proposals resulting from the simulation, and the output data is the policy proposals whose transparency has been confirmed.

[1475] Step 6:

[1476] The server automatically manages budgets related to legislators and elections. It analyzes past expenditure data and extracts cost items that can be reduced. Specifically, it suggests travel and accommodation costs that can be reduced by introducing digital meetings. The input data is budget data related to legislators and elections, and the output data is a list of cost items that can be reduced.

[1477] Step 7:

[1478] Users with devices can access the cloud server using a web browser or a dedicated app to check policy progress and proposals in real time. They can also directly input their opinions and feedback on policies. The input data is the user's access information and feedback, and the output data is real-time policy progress information.

[1479] Step 8:

[1480] The server uses an emotion engine to recognize the user's emotions. It analyzes emotions using a camera and microphone and reflects the feedback in policy proposals. Specifically, it analyzes the user's facial expressions and voice to calculate the policy's support and satisfaction. The input data is the user's facial expressions and voice data, and the output data is feedback information based on the emotion analysis.

[1481] Step 9:

[1482] The server collects and analyzes security-related data to generate optimal security policies. Specifically, it collects cyber-attack trends and vulnerability information, and formulates security policies based on the results. The input data is public security-related data, and the output data is the generated security policy.

[1483] 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.

[1484] 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.

[1485] 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.

[1486] 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.

[1487] 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.

[1488] 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.

[1489] 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).

[1490] 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.

[1491] 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."

[1492] 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.

[1493] 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).

[1494] 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.

[1495] 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.

[1496] 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.

[1497] 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.

[1498] 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.

[1499] 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.

[1500] 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.

[1501] 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.

[1502] 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.

[1503] 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.

[1504] The following is further disclosed regarding the above embodiment.

[1505] Below are draft patent claims for the proposed AI parliamentarianization system.

[1506] (Claim 1)

[1507] A means of collecting data related to politics, economics, education, childcare, culture, science, and law from around the world;

[1508] A means of analyzing the collected data to discover significant trends and patterns;

[1509] A means of conducting scenario simulations based on the analysis results and generating optimal policies;

[1510] A means to check the transparency of the policy proposals generated and compare them with lobby databases of relevant parties, etc.

[1511] A means to automatically manage legislative and election-related budgets and identify possible cost savings;

[1512] A means for users to access the cloud server and check the progress and proposals of policies in real time,

[1513] A system including:

[1514] (Claim 2)

[1515] 10. The system of claim 1, further comprising pre-processing the data.

[1516] (Claim 3)

[1517] 10. The system of claim 1, wherein a record of the policy-making process is kept and audits by a third party are possible.

[1518] "Example 1"

[1519] (Claim 1)

[1520] A means of regularly collecting data related to politics, economics, education, childcare, culture, science, and law from around the world;

[1521] A means to pre-process the collected data, remove noise, normalize the data, and analyze it;

[1522] A means to perform scenario simulations based on the analysis results and automatically generate optimal policies;

[1523] A means to check the transparency of the policy proposals generated and check them against databases of relevant parties;

[1524] A means to automatically manage legislative and election-related budgets and identify possible cost savings;

[1525] A means for users to access the cloud-based system and check policy progress and proposals in real time,

[1526] A system including:

[1527] (Claim 2)

[1528] 2. The system according to claim 1, wherein the collected data is analyzed using a machine learning algorithm.

[1529] (Claim 3)

[1530] 2. The system according to claim 1, wherein users input opinions and feedback on policies in real time, and the system reflects the feedback in the policy generation process.

[1531] "Application Example 1"

[1532] (Claim 1)

[1533] A means of collecting data related to politics, economics, education, childcare, culture, science, and law from around the world;

[1534] A means of analyzing the collected data to discover significant trends and patterns;

[1535] A means of conducting scenario simulations based on the analysis results and generating optimal policies;

[1536] A means to check the transparency of the policy proposals generated and compare them with lobby databases of relevant parties, etc.

[1537] A means to automatically manage legislative and election-related budgets and identify possible cost savings;

[1538] A means for users to access the cloud server and check the progress and proposals of policies in real time,

[1539] A means of collecting data from sensors and equipment in the factory and analyzing the data to optimize the manufacturing process;

[1540] means for simulating a manufacturing process and generating an optimal manufacturing plan;

[1541] measures to ensure transparency in production planning and prevent fraud;

[1542] A means to automatically manage manufacturing budgets and suggest potential cost savings;

[1543] A system including:

[1544] (Claim 2)

[1545] 10. The system of claim 1, further comprising pre-processing the data.

[1546] (Claim 3)

[1547] 10. The system of claim 1, wherein the system keeps records of the decision-making process and the manufacturing process, allowing for audits by a third party.

[1548] "Example 2: Combining Emotion Engines"

[1549] (Claim 1)

[1550] A means of collecting data related to politics, economics, education, childcare, culture, science, and law from around the world;

[1551] A means to pre-process and analyze the collected data to discover important trends and patterns;

[1552] A means of conducting scenario simulations based on the analysis results and generating optimal policies;

[1553] A means to check the transparency of the policy proposals generated and compare them with lobby databases of relevant parties, etc.

[1554] A means to automatically manage legislative and election-related budgets and identify possible cost savings;

[1555] A means for users to access the cloud server and check the progress and proposals of policies in real time,

[1556] An emotion engine that recognizes and analyzes user emotions and reflects them in policy proposals;

[1557] A means to collect and analyze user sentiment regarding policy proposals using an emotion engine and utilize this in future policy planning.

[1558] A system including:

[1559] (Claim 2)

[1560] 10. The system of claim 1, further comprising pre-processing the data.

[1561] (Claim 3)

[1562] 10. The system of claim 1, wherein a record of the policy-making process is kept and audits by a third party are possible.

[1563] "Application example 2 when combining emotion engines"

[1564] (Cla...

Claims

1. A means of collecting data related to politics, economics, education, childcare, culture, science, and law from around the world; A means of analyzing the collected data to discover significant trends and patterns; A means of conducting scenario simulations based on the analysis results and generating optimal policies; A means to check the transparency of the policy proposals generated and compare them with lobby databases of relevant parties, etc. A means to automatically manage legislative and election-related budgets and identify possible cost savings; A means for users to access the cloud server and check the progress and proposals of policies in real time, A system including:

2. 10. The system of claim 1, further comprising pre-processing the data.

3. 10. The system of claim 1, wherein a record of the policy-making process is kept and audits by a third party are made possible.

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