system

Abstract

This system will allow voters to quickly grasp information about candidates, enabling them to make informed voting decisions, and also provide a way to follow their activities as legislators after the election. [Solution] A system comprising: information gathering means for collecting data from election management agencies, local government websites, parliamentary broadcast data, candidates' social media, blogs, election information websites, and general social media; information analysis means for analyzing, extracting, and classifying the collected data; database registration means; user interface means for providing an interface for users to select their interests, electoral districts, and specific candidates; candidate suggestion means for searching the database for and presenting the most suitable candidate; candidate comparison means for comparing and displaying information such as policies and achievements for multiple candidates selected by the user; and legislator activity tracking means for periodically collecting information on legislators' activities after elections, allowing users to check the legislators' activities even after elections.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In elections, there is a problem that it is difficult for voters to determine which candidate to vote for. Information for grasping the ideas, personalities, and past achievements of candidates is scattered, leading to political distrust and a decline in the willingness to vote. Also, after the election, it is difficult to easily track the activity information of parliamentarians, resulting in a problem that voters' interest in politics fades. There is a need for a system to solve these problems.

Means for Solving the Problems

[0005] The present invention provides a system including information gathering means, information analysis means, database registration means, user interface means, candidate suggestion means, candidate comparison means, and legislator activity tracking means. The information gathering means collects data from election management agencies, local government websites, parliamentary proceedings data, candidates' social media, blogs, election information websites, and general social media. The information analysis means analyzes the collected data using natural language processing algorithms to extract and classify textual information. The database registration means registers the analyzed information in a database and provides an index for easy user access. The user interface means provides an interface for users to select their interests, electoral districts, and specific candidates. The candidate suggestion means searches the database for the most suitable candidate based on the information entered by the user and presents it. The candidate comparison means displays a comparative view of information such as policies and achievements for multiple candidates selected by the user. The legislator activity tracking means periodically collects information on legislators' activities after elections, allowing users to check the legislators' activities even after the election. This enables voters to grasp candidate information at a glance, make appropriate voting decisions, and follow legislators' activities after the election.

[0006] "Information gathering means" refers to the function of collecting data from sources such as election management agencies, local government websites, parliamentary proceedings data, candidates' social media, blogs, election information websites, and general social media.

[0007] "Information analysis means" refers to a function that uses natural language processing algorithms to analyze collected information and extract and classify specific information from text data.

[0008] "Database registration method" refers to the function of registering the analyzed information in a database and adding an index to make it easier for users to access.

[0009] "User interface means" refers to functions that provide an operable screen for users to interact with the system, enabling them to select interests, electoral districts, or specific candidates.

[0010] "Candidate suggestion method" refers to a function that searches a database for the most suitable candidates based on the interests and values ​​entered by the user and presents them to the user.

[0011] "Candidate comparison tools" refer to a function that allows users to compare information on multiple candidates they have selected and visually display it for each item, such as policies and achievements.

[0012] "Methods for tracking legislative activities" refers to a function that periodically collects information on the activities of elected legislators after an election and makes it available for users to review. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]Shows an emotion map to which multiple emotions are mapped. [Figure 10] Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0018] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the 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 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the 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 processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] The present invention is a system that includes information gathering means, information analysis means, database registration means, user interface means, candidate proposal means, candidate comparison means, and legislator activity tracking means. As an example of implementing this system, the following program configuration and specific processing steps will be described.

[0035] Information gathering and analysis

[0036] The server first periodically collects data from election management agencies, local government websites, council broadcast data, candidates' social media (e.g., Twitter, Facebook), blogs, election information sites, and general social media (e.g., YouTube, Instagram). This data collection uses scraping techniques and APIs.

[0037] The server applies natural language processing (NLP) algorithms to the collected data, extracting and classifying information such as candidates' names, policies, and statements from the text data. For example, it might extract "statements on environmental policy" and analyze the positive / negative sentiment associated with those statements.

[0038] Registration to the database

[0039] The server converts the analyzed information into a structured format (e.g., JSON, XML) and registers it in the database. The database stores indexed information on each candidate, ranging from basic information (name, age, political party affiliation, etc.) to policy-specific statements and activity history.

[0040] Providing a user interface

[0041] The device provides an intuitive user interface via a web browser or dedicated app when accessed by the user. Users can utilize search fields and filter options to select policies or constituencies of interest.

[0042] Comparison of candidates' proposals

[0043] When a user enters specific interests or policies, the device sends that information to a server, which then retrieves information on suitable candidates from a database. For example, a user interested in environmental issues or education would search for candidates with experience in those areas.

[0044] The server selects highly suitable candidates based on user input and sends a list of them to the terminal. The terminal displays a list of candidates' photos, summaries, and policy positions. Users can also select multiple candidates and compare their policies and track records.

[0045] Tracking parliamentary activities

[0046] After the election, the server continuously collects information on the activities of the elected representatives. For example, it updates the database with information such as parliamentary attendance rates, the number of bills introduced, and the content of their social media posts.

[0047] If users want to check the activities of elected representatives after their election, they can view the latest information through their devices. This allows users to continuously monitor the performance of the representatives they voted for.

[0048] Specific example

[0049] This example shows a user's search for candidates based on their particular interest in environmental policy. The user enters information emphasizing "environmental policy" and "childcare support," and the server searches its database for matching candidates. As a result, a list of candidates with a proven track record in environmental policy and a strong commitment to childcare support is displayed. Each candidate's past statements and specific policy achievements are also shown, allowing the user to compare and select the most suitable candidate.

[0050] Thus, this system not only allows voters to easily obtain information about candidates and support appropriate decision-making, but also enables them to follow the activities of their representatives even after the election. This promotes voter participation in politics and contributes to the healthy functioning of democracy.

[0051] The following describes the processing flow.

[0052] Step 1:

[0053] The server collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media, blogs, election information sites, and general social media. This collection uses web scraping techniques and APIs, and data is retrieved periodically according to a specified schedule.

[0054] Step 2:

[0055] The server processes the collected data using natural language processing (NLP) algorithms to extract and analyze necessary information from the text data (such as candidate names, policies, statements, and emotional tone). The analyzed data is then converted into a structured format (such as JSON or XML).

[0056] Step 3:

[0057] The server registers the analyzed and structured data into a database. The database stores and indexes basic information about each candidate, their statements on specific policies, past performance, and electoral district information.

[0058] Step 4:

[0059] The device provides a user interface via a web browser or dedicated app when accessed by the user. The user interface includes search fields and filter options based on interests, allowing users to select policies or constituencies of interest.

[0060] Step 5:

[0061] The user enters their interests, desired policies, specific electoral districts, and candidate names. The device then transmits this input data to the server in real time.

[0062] Step 6:

[0063] The server searches the database for suitable candidates based on the user's input. The search results list candidates ranked by their relevance to the entered interests and policies.

[0064] Step 7:

[0065] The server sends the search results to the terminal. This includes the candidate's basic information, policy list, past statements, and track record.

[0066] Step 8:

[0067] The device displays search results visually to the user. Candidates' photos, names, political affiliations, policies, and achievements are displayed in a list format that can be understood at a glance.

[0068] Step 9:

[0069] If a user wants to select multiple candidates from the displayed list and perform a detailed comparison, they can choose a comparison option.

[0070] Step 10:

[0071] The device displays a comparative view of the policies, achievements, and statements of multiple selected candidates. A visual comparison table is generated for each item, making it easy for users to understand the differences.

[0072] Step 11:

[0073] After the election, the server continuously collects information on the activities of the elected representatives. In addition to the information collection process described above, attendance rates in parliament, the number of bills introduced, and statements made on social media are also tracked.

[0074] Step 12:

[0075] If a user wants to check the activities of their elected officials after an election, the latest information will be displayed on their device. The data includes the latest reports on the officials' activities, records of their speeches in parliament, and recent social media posts.

[0076] Through these steps, voters can quickly and easily obtain information about candidates and make informed decisions. Furthermore, they can continue to follow the activities of their representatives after the election, contributing to the healthy functioning of democracy.

[0077] (Example 1)

[0078] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0079] Modern voters struggle to efficiently and accurately obtain the information necessary to choose the right candidate in an election. Relying on traditional media or specific sources often results in biased or insufficient information. Furthermore, there are limited means to continuously follow the activities of elected officials after the election. As a result, it is difficult for voters to actively participate in politics and support the healthy functioning of democracy.

[0080] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0081] In this invention, the server includes means for collecting information, means for analyzing information, means for registering in a database, means for a user interface, means for proposing candidates, means for comparing candidates, means for tracking the activities of legislators, means for searching for information from a database based on user input and selecting candidates with a high degree of suitability, means for collecting data using scraping techniques or APIs, means for analyzing text data by applying natural language processing algorithms, converting it into a structured format and registering it in a database, means for providing an intuitive user interface, and means for continuously collecting information on the activities of legislators after their election and updating the database. This makes it possible for voters to quickly and efficiently obtain detailed information on candidates and to continuously monitor the activities of legislators even after the election.

[0082] "Information gathering methods" refer to means of collecting data from public institution websites, parliamentary broadcast data, social media, and blogs.

[0083] "Information analysis means" refers to means for analyzing data collected using natural language processing algorithms, and for extracting and classifying textual information.

[0084] A "database registration method" is a means of converting analyzed information into a structured format and registering it in a database.

[0085] A "user interface means" is a means of providing an intuitive user interface that allows users to search for and compare information.

[0086] A "candidate suggestion method" is a means of searching for information from a database based on user input and selecting candidates with a high degree of suitability.

[0087] A "candidate comparison tool" is a means that allows users to compare information on multiple candidates in detail.

[0088] "Methods for tracking parliamentary activities" refer to means for continuously collecting information on the activities of elected members of parliament and updating the database accordingly.

[0089] "Web scraping" is a technique for automatically obtaining data from websites.

[0090] "API" stands for Application Programming Interface, and refers to an interface that enables data exchange between different software programs.

[0091] A "natural language processing algorithm" is an algorithm that analyzes text data and interprets, understands, and generates human language.

[0092] A "structured format" is a format that arranges data according to specific rules, making it easier to analyze mechanically. Examples include JSON and XML.

[0093] The present invention is a system that includes information gathering means, information analysis means, database registration means, user interface means, candidate proposal means, candidate comparison means, and legislator activity tracking means. As an example of implementing the present invention, the following program configuration and specific processing steps will be described.

[0094] Information gathering and analysis

[0095] The server first periodically collects data from government websites, parliamentary broadcast data, social media (e.g., Twitter, Facebook), blogs, election information sites, and general social media (e.g., YouTube, Instagram). This collection utilizes scraping techniques using Python's Scrapy library and Beautiful Soup, as well as APIs from each data source.

[0096] The server applies natural language processing (NLP) algorithms to the collected data. It analyzes text data using Python's NLTK library and spaCy. Specifically, it extracts and classifies information such as candidates' names, policies, and statements. For example, it extracts statements related to environmental policy and analyzes positive / negative sentiment based on specific keywords.

[0097] Registration to the database

[0098] The server converts the parsed information into a structured format (e.g., JSON, XML) and registers it in the database. MySQL or PostgreSQL are used as the database. Specifically, SQLAlchemy is used to connect to the database and insert data into the appropriate tables.

[0099] Providing a user interface

[0100] The device provides an intuitive user interface via a web browser or dedicated app when accessed by the user. React.js or Vue.js are used for the frontend, while Django or Flask are used for managing the API in the backend.

[0101] Users can enter their areas of interest, such as policies or constituencies, into the search field and use filter options to narrow down candidates based on specific criteria.

[0102] Comparison of candidates' proposals

[0103] When a user enters specific interests or policies, the terminal sends that information to the server. The communication protocol used here is HTTP.

[0104] The server queries the database based on user input to retrieve information on highly suitable candidates. It extracts data using SQL WHERE statements and filtering functions.

[0105] The device displays this information in a list format. Users can compare the policies and track records of multiple candidates in detail.

[0106] Tracking parliamentary activities

[0107] The server continuously collects information on the activities of elected representatives and updates the database. This includes parliamentary attendance rates, the number of bills introduced, and statements made on social media. This data is also collected via scraping and APIs.

[0108] If users want to check the activities of elected representatives after their election, they can view the latest information through their devices.

[0109] Specific example

[0110] If a user has a particular interest in environmental policy, this example illustrates how they can enter that information to search for candidates.

[0111] First, the user enters information emphasizing "environmental policy" and "childcare support" into the search field. Next, the device sends this information to the server. The server queries the database for candidates that match these criteria and retrieves the results. The device displays a list of candidates with a proven track record in environmental policy and a proactive stance on childcare support, along with detailed information on each candidate's past statements and specific policy achievements. Based on this information, the user can compare candidates and select the appropriate one.

[0112] The following is an example of a prompt statement:

[0113] "I am interested in environmental policy. Could you recommend any candidates with a proven track record in environmental policy?"

[0114] "Please search for candidates who are proactive in supporting childcare."

[0115] Thus, this system not only allows voters to easily obtain information about candidates and support appropriate decision-making, but also enables them to follow the activities of their representatives even after the election. This promotes voter participation in politics and contributes to the healthy functioning of democracy.

[0116] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0117] Step 1:

[0118] The server collects data from government websites, parliamentary broadcast data, social media, and blogs. Specifically, it performs web scraping using Python's Scrapy library and Beautiful Soup, and retrieves data using APIs. The input is the URL of each site or API, and the output is the collected raw data.

[0119] Step 2:

[0120] The server analyzes the collected raw data using natural language processing (NLP) algorithms. Using Python's NLTK library and spaCy, it extracts and classifies information such as candidate names, policies, and statements from the text data. The input is the raw data collected in step 1, and the output is structured data as a result of the analysis.

[0121] Step 3:

[0122] The server converts the parsed structured data into formats such as JSON or XML. It uses Python's json library or xml.etree.ElementTree. The input is the data parsed in step 2, and the output is the data converted into a structured format.

[0123] Step 4:

[0124] The server registers the converted structured data into a database. MySQL or PostgreSQL is used as the database, and SQLAlchemy is used for database operations. The input is the data converted in step 3, and the output is the data registered in the database.

[0125] Step 5:

[0126] The device provides an intuitive user interface via a web browser or dedicated app when accessed by the user. React.js or Vue.js are used for the frontend. Input is the user's access request, and output is the display of the interface.

[0127] Step 6:

[0128] Users enter the policies or electoral districts they are interested in into the search field. For example, they might enter keywords such as "environmental policy" or "childcare support." The input is the user's search query, and the output is the search request to the device.

[0129] Step 7:

[0130] The terminal sends the search query entered by the user to the server. The communication protocol used here is HTTP. The input is the search query entered by the user in step 6, and the output is the HTTP request to the server.

[0131] Step 8:

[0132] The server searches the database based on the received search query and retrieves information on matching candidates. It extracts data using SQL WHERE statements and filtering functions. The input is an HTTP request to the server, and the output is information on matching candidates.

[0133] Step 9:

[0134] The terminal displays candidate information received from the server to the user in a list format. React.js and Vue.js components are used here. The input is candidate information from the server, and the output is the display of candidate information to the user.

[0135] Step 10:

[0136] Users compare information on multiple candidates. Specifically, they use filter options and display formats to compare each candidate's policies and track record in detail. The input is the candidate information displayed on the terminal, and the output is the user's comparison results.

[0137] Step 11:

[0138] The server continuously collects information on the activities of elected representatives. It collects data from public institution websites and social media using scraping and APIs. The input is the URL of each site or API, and the output is the collected activity data of the representatives.

[0139] Step 12:

[0140] The server updates the database with the collected information on the activities of the legislators. Database operations are performed using libraries such as SQLAlchemy. The input is the data collected in step 11, and the output is the updated database.

[0141] Step 13:

[0142] Users can view the latest information on the activities of elected representatives through their devices if they wish to check the status of their activities after being elected. The input is the user's viewing request, and the output is the latest activity information of the representative displayed on the device.

[0143] (Application Example 1)

[0144] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0145] Modern political information is vast, making it difficult for voters to obtain the information they need quickly and accurately. Furthermore, many voters struggle to keep track of their politicians' activities after an election. Meanwhile, in today's world of widespread autonomous vehicles, there is a need for a system that allows for efficient acquisition of political information while on the go. This invention aims to solve this problem and promote political participation by providing voters with relevant information in a timely manner.

[0146] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0147] In this invention, the server includes means for collecting information, means for analyzing information, means for registering in a database, means for a user interface, means for proposing candidates, means for comparing candidates, means for tracking the activities of legislators, means for coordinating with an in-vehicle display unit, and means for displaying candidate information on the in-vehicle display unit based on the interests of general users. This allows voters to easily obtain information on policies and candidates of interest to them, even while on the move, and to make appropriate decisions.

[0148] "Information gathering methods" refer to means of regularly collecting data from election management agencies, local government websites, parliamentary proceedings data, candidates' social media, blogs, election information websites, and general social media.

[0149] "Information analysis means" refers to methods that apply natural language processing algorithms to collected data to extract and classify information such as candidates' names, policies, and statements from text data.

[0150] A "database registration method" is a means of converting the analyzed information into a structured format (e.g., JSON, XML) and registering it in a database.

[0151] A "user interface means" refers to a means by which users can access an intuitive user interface through a web browser or dedicated application, and utilize search fields and filter options to select policies or electoral districts of interest.

[0152] A "candidate suggestion method" is a system that, when a user inputs specific interests or policies, retrieves information on suitable candidates from a database based on that information and selects candidates with a high degree of suitability.

[0153] A "candidate comparison tool" is a means by which users can select multiple candidates and compare and display their respective policies and track records.

[0154] A "method for tracking the activities of elected officials" refers to a means of continuously collecting information on the activities of officials who have been elected after an election and updating a database accordingly.

[0155] An "in-vehicle display unit" is hardware that functions as a display device inside an autonomous vehicle.

[0156] "Means for displaying candidate information on in-vehicle display units based on the interests of general users" refers to means of displaying information related to policies and elections that users are interested in, in an appropriate format, on in-vehicle display units.

[0157] The present invention is a system that works in conjunction with an in-vehicle display unit to provide political information to users of autonomous vehicles. This system includes means for collecting information, means for analyzing information, means for registering in a database, means for a user interface, means for proposing candidates, means for comparing candidates, means for tracking the activities of legislators, means for working in conjunction with an in-vehicle display unit, and means for displaying candidate information on the in-vehicle display unit based on the interests of general users.

[0158] hardware

[0159] The system uses the following hardware:

[0160] Server: A central computer that performs tasks such as data collection, analysis, and registration in databases.

[0161] In-vehicle display unit: A device for displaying information inside an autonomous vehicle.

[0162] User terminals (smartphones and tablets): Used for auxiliary operations and checking information.

[0163] software

[0164] The system uses the following software:

[0165] Flask: A lightweight web application framework for Python

[0166] spaCy and NLTK: Libraries for implementing natural language processing algorithms

[0167] PostgreSQL: A relational database for storing analysis results.

[0168] Data processing and data calculation

[0169] 1. Information Gathering Methods: The server regularly collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media, blogs, election information websites, and general social media. Scraping techniques and public APIs are used for data collection.

[0170] 2. Information Analysis Method: The server applies natural language processing (NLP) algorithms to the collected data to extract and classify information such as candidates' names, policies, and statements from the text data. Libraries such as spaCy and NLTK are used. This analysis enables the analysis of statements and sentiments regarding specific policies.

[0171] 3. Database Registration Method: The parsed information is converted into a structured format and stored in a PostgreSQL database. This indexes the information, making it quickly searchable and retrievalable.

[0172] 4. User Interface: Search fields and filter options are provided via a web browser or dedicated app for users to select policies and electoral districts of interest. This allows users to intuitively manipulate information. The user interface is integrated with the in-car display unit.

[0173] 5. Candidate Suggestion Method: When a user enters specific interests or policies into the in-car display unit, that information is sent to a server, which retrieves information on suitable candidates from the database. This allows the system to suggest the most suitable candidates based on the user's interests.

[0174] 6. Candidate Comparison Method: Users can select multiple candidates and compare their policies and track records. This makes it easier for users to compare and consider their options.

[0175] 7. Tracking Legislative Activities: After the election, the server continuously collects information on the activities of elected legislators (parliamentary attendance rate, number of bills introduced, social media posts, etc.) and updates the database. Users can view the latest information through the in-car display unit.

[0176] Specific example

[0177] When a user enters "environmental policy" and "childcare support" as areas of interest into the in-car display unit, the server retrieves relevant candidate information from the database and displays a list of the candidates' past statements and specific policy achievements. This allows users to efficiently obtain information even while on the go.

[0178] Examples of prompts for a generative AI model:

[0179] "Please search for information on candidates related to environmental policy and childcare support. You can choose from the following candidates:"

[0180] Candidate A: Achievements in environmental policy and initiatives in childcare support.

[0181] Candidate B: Number of statements on environmental policy, specific achievements in childcare support.

[0182] Please compare and select the most suitable candidate.

[0183] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0184] Step 1:

[0185] The server collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media, blogs, election information sites, and general social media. It uses scraping techniques and public APIs for data collection, and continuously collects data according to a specific time schedule. Inputs are the URLs and API keys of each information source, and output is the collected source data.

[0186] Step 2:

[0187] The server applies natural language processing (NLP) algorithms to the collected data, extracting and classifying information such as candidates' names, policies, and statements from the text data. Specifically, it uses libraries such as spaCy and NLTK to parse the text and perform sentiment analysis. The input is the original text data collected in step 1, and the output is the analyzed structured data (in text format).

[0188] Step 3:

[0189] The server converts the parsed information into a structured format (e.g., JSON, XML) and registers it in a PostgreSQL database. This indexes the information so that it can be quickly searched and retrieved. The input is the structured data obtained in step 2, and the output is the information registered in the database.

[0190] Step 4:

[0191] The user operates the in-car display unit or smartphone and inputs information through search fields and filter options to select policies or electoral districts of interest. This generates a search query based on those interests. The input is the search criteria entered by the user into the device, and the output is the generated search query.

[0192] Step 5:

[0193] The server receives a search query submitted by the user and retrieves information on suitable candidates from the database. The input is the search query from step 4, and the output is a list of candidate information that matches the search query.

[0194] Step 6:

[0195] The server converts the acquired candidate information into a format suitable for the user interface and sends it to the in-car display unit or smartphone. The input is the candidate information list acquired in step 5, and the output is the information in the appropriate format to be displayed in the GUI (Graphical User Interface).

[0196] Step 7:

[0197] The user views a list of candidates' policies and achievements via an in-car display unit or smartphone, and selects and compares multiple candidates. The input is the candidate information displayed in step 6, and the output is the information of the compared candidates.

[0198] Step 8:

[0199] The server periodically collects information on the activities of elected legislators (such as parliamentary attendance rates, number of bills introduced, and social media posts) even after the election, and updates the database. Inputs are the URLs and API keys of each information source, and output is the updated information on the legislators' activities.

[0200] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0201] The present invention is a system that includes information gathering means, information analysis means, database registration means, user interface means, candidate suggestion means, candidate comparison means, legislator activity tracking means, and an emotion engine. As an example of implementing this system, the following program configuration and specific processing steps are described.

[0202] Information gathering and analysis

[0203] The server periodically collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media (e.g., Twitter, Facebook), blogs, election information sites, and general social media (e.g., YouTube, Instagram). This collection is performed according to a specified schedule using web scraping techniques and APIs.

[0204] The server analyzes the collected data using natural language processing (NLP) algorithms, extracting and classifying necessary information (candidate names, policies, statements, emotional tone, etc.) from the text data. The analyzed data is then converted into a structured format (e.g., JSON, XML).

[0205] Registration to the database

[0206] The server registers the analyzed and structured data into a database. This database stores indexed information such as each candidate's basic information, policy statements, past achievements, and electoral district information.

[0207] Providing a user interface

[0208] The device provides an intuitive user interface via a web browser or dedicated app upon user access. This interface includes search fields and filter options based on interests, allowing users to select policies or constituencies of interest.

[0209] Introducing an emotional engine

[0210] The device uses an emotion engine to recognize emotions from the text and voice input by the user. The emotion engine analyzes the user's input text and voice to determine the type of emotion (e.g., joy, anger, sadness, etc.). Based on this determination, the server selects the most suitable candidate information.

[0211] Comparison of candidates' proposals

[0212] Users input specific interests or policies, and their emotions at the time are also recorded. The device sends this input data to the server in real time.

[0213] The server searches the database for the most suitable candidates based on the user's input information and sentiments. The search results list candidates ranked by relevance, matching the entered interests, policies, and sentiments.

[0214] The server sends search results to the device, providing information on suitable candidates, taking sentiment into consideration. The device displays a list of candidates, including their photos, names, political affiliations, policies, and track records, in an easily understandable format.

[0215] Tracking parliamentary activities

[0216] After the election, the server continuously collects information on the activities of the elected representatives. In addition to the information gathering process before the election, it regularly updates the database with activity information such as parliamentary attendance rates, the number of bills introduced, and statements made on social media.

[0217] If a user wants to check the activities of their elected officials after an election, the latest information will be displayed on their device. This includes the latest reports on the officials' activities, transcripts of their speeches in parliament, and recent social media posts.

[0218] Specific example

[0219] For example, suppose a user has a particular interest in environmental policy and enters information on this topic. Simultaneously, if the server recognizes the user's emotions (e.g., "anxiety") from the input, it searches its database for candidates who can provide information that alleviates anxiety (e.g., candidates with policies that appeal to a sense of security). As a result, a list of candidates who are proactive in environmental policy but also propose policies that reduce the user's anxiety is displayed.

[0220] Thus, this system allows voters to quickly and easily obtain information about candidates and make appropriate decisions. Furthermore, by customizing information while considering the user's emotions, it provides a more personalized experience. In addition, it contributes to the healthy functioning of democracy by allowing voters to continue following the activities of their representatives even after the election.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] The server periodically collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media, blogs, election information sites, and general social media. This collection is performed using web scraping techniques and APIs, and is carried out according to a specified schedule.

[0224] Step 2:

[0225] The server processes the collected data using natural language processing (NLP) algorithms to extract and analyze necessary information from the text data (such as the candidate's name, policies, statements, and emotional tone). The analyzed data is then converted into a structured format (such as JSON or XML).

[0226] Step 3:

[0227] The server registers the analyzed and structured data into a database. This database stores indexed information such as each candidate's basic information, policy statements, past achievements, and electoral district information.

[0228] Step 4:

[0229] The device provides a user interface via a web browser or dedicated app upon user access. This user interface includes search fields and filter options based on interests, allowing users to select policies or constituencies of interest.

[0230] Step 5:

[0231] Users input their interests, desired policies, specific electoral districts, and candidate names. The device transmits this input data to the server in real time. Additionally, an emotion engine analyzes the user's emotions from their input data and voice, and transmits that information to the server as well.

[0232] Step 6:

[0233] The server searches the database for suitable candidates based on the user's input and analyzed sentiment data. The search results list candidates ranked by relevance, matching the user's interests, policies, and sentiments.

[0234] Step 7:

[0235] The server sends search results to the terminal. This includes basic information about the candidate, a list of policies, past statements, and achievements. It also provides information on suitable candidates, taking sentiment into consideration.

[0236] Step 8:

[0237] The device displays search results to the user visually. Candidates' photos, names, political affiliations, policies, and achievements are presented in a list format that allows for quick and easy viewing. Suggestions based on sentiment data are also highlighted.

[0238] Step 9:

[0239] If a user wants to select multiple candidates from the displayed list and perform a detailed comparison, they can choose a comparison option.

[0240] Step 10:

[0241] The device displays a comparative view of the policies, achievements, and statements of multiple selected candidates. A visual comparison table is generated for each item, making it easy for users to understand the differences.

[0242] Step 11:

[0243] After the election, the server continuously collects information on the activities of the elected representatives. In addition to the information gathering process before the election, it updates the database with information such as parliamentary attendance rates, the number of bills introduced, and social media posts.

[0244] Step 12:

[0245] If users want to check the activities of their elected officials after the election, the latest information will be displayed on their devices. This includes the latest reports on their activities, transcripts of their speeches in parliament, and their latest social media posts.

[0246] Through these steps, voters can quickly and easily obtain information about candidates and make informed decisions. By customizing the information to take into account emotions such as anxieties and interests, a more personalized experience is provided. Furthermore, voters can continue to follow the activities of their representatives after the election, contributing to the healthy functioning of democracy.

[0247] (Example 2)

[0248] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0249] Traditional election candidate information systems lacked sufficient information collection and analysis, making it difficult for voters to quickly and accurately obtain detailed information on specific policies or candidates of interest. Furthermore, they failed to consider voter sentiment, resulting in a lack of personalized user experiences. Additionally, post-election tracking of legislative activities was neglected, potentially hindering the healthy functioning of democracy.

[0250] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0251] In this invention, the server includes information gathering means, information analysis means, database registration means, user interface means, candidate suggestion means, candidate comparison means, legislator activity tracking means, emotion recognition means, data structuring means, and recommendation result display means. This enables voters to quickly obtain candidate information and provides personalized information that takes emotions into consideration. Furthermore, it enables continuous tracking of legislator activities after elections, supporting the healthy operation of democracy.

[0252] "Information gathering methods" refer to means of regularly collecting data from election management agencies, local government websites, parliamentary proceedings data, candidates' social media, blogs, election information websites, and general social media.

[0253] "Information analysis means" refers to means of analyzing data collected using natural language processing algorithms, extracting and classifying textual information, and further determining emotional tone from the collected data.

[0254] A "database registration method" is a means of registering analyzed and structured data in a database, indexing it, and storing it.

[0255] "User interface means" refers to means that provide an intuitive interface when a user accesses something, and includes search fields and filter options.

[0256] A "candidate suggestion method" is a means of searching for the most suitable candidates from a database based on user input information and sentiment, ranking them by suitability, and listing them.

[0257] A "candidate comparison method" is a means of displaying search results in a user-friendly format, allowing users to compare multiple candidates.

[0258] A "method for tracking parliamentary activities" refers to a means of continuously collecting information on the activities of elected members of parliament after an election and updating the database accordingly.

[0259] An "emotion recognition method" is a means of recognizing and analyzing emotions from text or voice input by the user.

[0260] A "data structuring method" is a means of converting parsed information into a structured format (e.g., JSON, XML).

[0261] A "recommendation result display method" is a means of displaying candidate information, based on the results of sentiment recognition, in a user-friendly format.

[0262] This invention provides a system that enables voters to quickly and accurately obtain information on election candidates and support more appropriate decision-making. This system includes information gathering means, information analysis means, database registration means, user interface means, candidate suggestion means, candidate comparison means, legislator activity tracking means, sentiment recognition means, data structuring means, and recommendation result display means. The specific implementation methods for each means are described below.

[0263] Information gathering and analysis

[0264] The server periodically collects data from election management agencies, local government websites, council broadcast data, candidate social media, blogs, election information sites, and general social media. This data collection is performed using Python's BeautifulSoup and various APIs. For example, it uses the Twitter API to collect tweets with the hashtag "election2023". Cron jobs are also used to perform periodic data collection.

[0265] The server analyzes the collected data using spaCy, a natural language processing (NLP) module. It extracts information such as the candidate's name, policies, statements, and emotional tone from the collected text data. Furthermore, NLTK's VADER is used for sentiment analysis to determine the emotional tone of the statements. For example, the statement "This bill is absolutely necessary" is analyzed as "positive."

[0266] Registration to the database

[0267] The server converts the analyzed data into JSON format and registers it in a MySQL database. The database stores indexed information such as each candidate's basic information, policy statements, past achievements, and electoral district information. For example, information such as "Name: Candidate A, Policy: Environmental protection, Emotion: Positive" is registered.

[0268] Providing a user interface

[0269] The device displays an interface developed with React when a user accesses the site via a web browser. Users can search for information on policies and candidates of interest through a search bar and filter options. For example, if a user selects "environmental policy" as their area of ​​interest, candidates related to that policy will be displayed.

[0270] Introducing an emotional engine

[0271] The device retrieves text entered by the user and sends it to an emotion engine (e.g., Google Cloud Natural Language API). For example, if the user enters "I'm worried about the future of the Earth," that text is sent to the emotion engine.

[0272] The server receives the analysis results from the emotion engine, and if the user's emotion is recognized as "anxiety," it uses that information to supplement the search criteria.

[0273] Comparison of candidates' proposals

[0274] The server searches a MySQL database for the most suitable candidates based on the user's interests and emotional information. For example, based on "environmental policy" and "anxiety," it identifies candidates with environmental policies that appeal to a sense of security.

[0275] The search results are sorted in a ranking format, with the datasets prepared in order of relevance. The server sends the search results to the device, which displays them in a user-friendly format. For example, a list of candidates' photos, names, political affiliations, policies, and achievements might be displayed.

[0276] Tracking parliamentary activities

[0277] After an election, the server periodically collects information on the legislators' activities, such as attendance rates, the number of bills introduced, and their social media activity. For example, it uses AWS Lambda to periodically call an API to retrieve the latest data on legislators and update the database.

[0278] If users want to check the activities of their elected officials after an election, they can view the latest information through their devices. For example, the content of recent statements made in parliament and attendance rates will be displayed.

[0279] Specific example

[0280] For example, if a user is particularly interested in environmental policies and enters the following prompt sentence: "I'm worried about the future of the Earth. Which candidate is proactive in environmental policies?" At this time, the emotion "uneasiness" is recognized. As a result, the server searches for candidates who can give a sense of security, and candidate B is displayed at the top of the list. In this way, the user can quickly and easily obtain appropriate candidate information.

[0281] The flow of the specific process in Example 2 will be described using FIG. 13.

[0282] Step 1:

[0283] The server collects data from the websites of electoral management bodies and local governments, the social media of candidates, blogs, election information sites, and general social media. Specifically, it is executed using BeautifulSoup in Python and the APIs of each platform. For example, BeautifulSoup is used to extract candidate speech data related to environmental policies from web pages. This collection is regularly executed by a Cron job. The input is the URL of the website or the endpoint of the API, and the output is raw data.

[0284] Step 2:

[0285] The server analyzes the collected raw data using natural language processing (NLP) algorithms. Specifically, spaCy is used to extract the names of candidates, policies, speech contents, etc. from the text data. Also, VADER of NLTK is used for sentiment analysis to determine the sentiment tone of the speech. For example, from the speech "I think environmental protection is important", the policy "environmental protection" is extracted, and the sentiment tone is determined to be "positive". The input is raw data, and the output is analyzed data.

[0286] Step 3:

[0287] The server converts the parsed data into JSON format and registers it in the database. Specifically, it uses a MySQL database and applies indexes to enable fast searching. For example, it structures and stores data in the format "Name: Candidate A, Policy: Environmental Protection, Sentiment: Positive". The input is the parsed data, and the output is the data stored in the database.

[0288] Step 4:

[0289] The device provides an intuitive interface for users to access. Specifically, a web application developed with React is displayed, allowing users to search for information on policies and candidates of interest through a search bar and filter options. For example, typing "environmental policy" into the search bar will display a list of candidates related to that policy. The input is the user's search query, and the output is filtered candidate information.

[0290] Step 5:

[0291] The device sends the text entered by the user to the emotion engine. Specifically, it uses the Google Cloud Natural Language API or IBM Watson to analyze the text and determine the type of emotion. For example, if the user enters "I'm worried about the future of the Earth," that text is sent to the emotion engine, and the emotional tone is determined to be "anxious." The input is the user's text, and the output is the emotion analysis result.

[0292] Step 6:

[0293] The server searches for the most suitable candidate from the database based on the user's input information and sentiment information. Specifically, it identifies the most suitable candidate from the MySQL database based on "environmental policy" and "anxiety." For example, the database might detect that "Candidate B" proposes policies that provide a sense of security. The input is the user's interests and sentiment information, and the output is a list of candidates.

[0294] Step 7:

[0295] The server sends the search results to the terminal. The terminal displays the search results in a user-friendly format. Specifically, it lists the candidates' photos, names, political affiliations, policies they advocate, and past achievements. For example, "Candidate B, environmental protection, positive policies" might appear at the top. The input is a list of candidates, and the output is a list displayed on the user interface.

[0296] Step 8:

[0297] After the election, the server periodically collects information on the activities of legislators and updates the database. Specifically, it uses AWS Lambda and API Gateway to periodically retrieve information from various data sources, such as parliamentary attendance rates, the number of bills introduced, and social media posts. For example, "the number of bills recently introduced by legislator A" is collected. The input is periodic API calls, and the output is the latest legislator data.

[0298] Step 9:

[0299] If a user wants to check the activities of a legislator after an election, the latest information will be displayed on their device. Specifically, a dashboard developed with React will display the legislator's recent statements and activities. For example, "Legal Legislator B's latest statements" can be viewed. The input is the user's request, and the output is the latest legislator activity information.

[0300] (Application Example 2)

[0301] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0302] The current election information system requires users to refer to multiple sources to obtain policy and candidate information of interest, which is time-consuming. In addition, since it is unable to propose candidate information and election-related products tailored to users' emotions and interests, there is an issue that it is difficult to attract interest and concern for elections.

[0303] 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.

[0304] In this invention, the server includes information collection means, information analysis means, database registration means, user interface means, candidate proposal means, candidate comparison means, councilor activity tracking means, emotion engine, means for providing election-related products and candidate information in a virtual store, and means for proposing election-related products based on emotion and interest. As a result, users can quickly obtain policy and candidate information of interest through one system, and can further receive personalized information and product proposals tailored to their emotions and interests.

[0305] The "information collection means" is means for collecting election-related information from election management authorities, local government websites, parliamentary relay data, candidates' social media, blogs, election information sites, and general social media.

[0306] The "information analysis means" is means for analyzing the collected information using natural language processing algorithms, and extracting and classifying necessary text information.

[0307] The "database registration means" is means for registering the analyzed information in the database in a structured format, indexing it, and storing it.

[0308] The "user interface means" is means for providing an interface that can be intuitively operated by the user, and for the user to input or search for information of interest.

[0309] A "candidate suggestion method" is a means of searching a database for and suggesting the most suitable candidate information based on the user's input information and emotions.

[0310] A "candidate comparison tool" is a means of displaying a list of information such as the policies advocated, track record, and sentiment analysis results of multiple candidates, allowing users to compare their information.

[0311] A "method for tracking parliamentary activities" refers to a system for continuously collecting information on the activities of elected members of parliament after the election and updating a database accordingly.

[0312] An "emotion engine" is an engine that analyzes emotions from user input text and voice, and provides data and product suggestions based on the results.

[0313] "Means of providing election-related goods and candidate information in a virtual store" refers to means of displaying election-related goods (e.g., posters, books, merchandise, etc.) and candidate information within a virtual store, making them easily accessible to users.

[0314] "A means of suggesting election-related products based on emotions and interests" refers to a means of suggesting the most suitable election-related products within a virtual store based on the user's emotions and interests.

[0315] A description of embodiments for carrying out the present invention will be provided.

[0316] System Configuration

[0317] First, as a means of gathering information, the server collects data from election management agencies, local government websites, council broadcast data, candidates' social media, blogs, election information sites, and general social media. This collection is performed periodically using web scraping techniques and APIs.

[0318] Hardware and software

[0319] Server: Performs information gathering, analysis, and database management.

[0320] Smartphones / Tablets: Provide a user interface

[0321] software:

[0322] Python: Implementation of an information analysis program

[0323] Requests: Data collection using HTTP communication

[0324] JSON: Data parsing and storage

[0325] NLP Library: Implementation of Natural Language Processing Algorithms

[0326] Emotion analysis engine: Extracts and analyzes user emotions.

[0327] Data analysis and registration

[0328] The server analyzes the collected data using natural language processing (NLP) algorithms, extracting necessary information from the text data (e.g., candidate names, policies, statements, emotional tone) and converting it into a structured format (e.g., JSON, XML). The analyzed data is indexed and stored in a database.

[0329] User Interface and Emotion Engine

[0330] When a user enters information via their device, the emotion engine analyzes that input to understand their emotions and provides relevant information based on their interests. Specifically, using a smartphone or tablet, users can input policies or topics they are interested in, and their emotions are automatically analyzed. This input data is transmitted to the server in real time.

[0331] Comparison with candidate proposals

[0332] The server searches its database for the most suitable candidates based on the user's input and sentiments. The search results list candidates ranked by relevance, matching the user's interests, policies, and sentiments. Information on the listed candidates is displayed through the user interface. Users can see a candidate's photo, name, political affiliation, policies, and track record at a glance.

[0333] Applications in virtual stores

[0334] The server also provides election-related merchandise and candidate information within the virtual store. For example, if a user is interested in "environmental policy," related posters, books, and merchandise will be displayed in the virtual store. Based on the user's emotions (e.g., "seeking reassurance"), the most suitable products will be suggested.

[0335] Examples of prompt statements

[0336] For example, if the user enters the following prompt:

[0337] I'm looking for election-related merchandise. I'm interested in "environmental policy," so please display posters and related products from candidates who advocate for environmentally conscious policies. Also, please provide candidate recommendations based on the user's sentiment (e.g., "I want to feel safe").

[0338] In this way, the system provides appropriate data based on users' interests and emotions, facilitating the acquisition of election-related information and suggesting election-related products and services. This enhances users' understanding of and interest in elections, supporting better decision-making.

[0339] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0340] Step 1:

[0341] The server regularly collects information from election management agencies, local government websites, parliamentary broadcast data, candidate social media accounts, blogs, election information sites, and general social media. It receives target website URLs and social media account information as input, retrieves data using web scraping techniques and APIs, and stores it as text data. The output is raw data.

[0342] Step 2:

[0343] The server analyzes the collected raw data using natural language processing (NLP) algorithms. Specifically, it extracts and classifies candidates' names, policies, statements, and emotional tone from the text data. Using the raw data obtained in step 1 as input, NLP extracts the necessary information as structured data. The output is structured data in JSON or XML format.

[0344] Step 3:

[0345] The server registers the analyzed and structured data into a database. This process indexes extracted candidate information, policies, sentiment tones, etc., making it efficiently searchable. It accepts structured data as input and stores it in the database. The output is an indexed database entry.

[0346] Step 4:

[0347] Users input policies and themes of interest via devices such as smartphones and tablets. The user's input data is analyzed by an emotion engine, which determines emotions from the input text and audio. The input consists of the user's text or voice, and the emotion analysis algorithm identifies the emotional tone. The output is the analyzed emotion data.

[0348] Step 5:

[0349] The server searches the database for the most suitable candidates based on the user's input and sentiment. It receives user interest and sentiment data as input and searches the database for matching candidates. These search results are ranked by relevance, and the output is a list of the most suitable candidates.

[0350] Step 6:

[0351] The search results are displayed immediately through the user interface (UI). The displayed information includes the candidate's photo, name, political affiliation, policies, and past achievements. Candidate information from search results is received as input and presented to the user in an intuitive UI format. The output is a visually displayed list of candidates.

[0352] Step 7:

[0353] In virtual stores offering election-related merchandise and candidate information, the system also suggests relevant products based on the user's emotions and interests. It uses data on the user's emotions and interests as input to find and suggest the most suitable products. The output displays a list of election-related products and reasons for recommendation.

[0354] In this way, by linking the server, terminal, and emotion engine, a system is realized that allows users to efficiently obtain election information and related products.

[0355] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0356] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0357] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0358] [Second Embodiment]

[0359] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0360] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0361] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0362] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0363] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0364] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0365] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0366] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0367] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0368] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0369] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0370] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0371] The present invention is a system that includes information gathering means, information analysis means, database registration means, user interface means, candidate proposal means, candidate comparison means, and legislator activity tracking means. As an example of implementing this system, the following program configuration and specific processing steps will be described.

[0372] Information gathering and analysis

[0373] The server first periodically collects data from election management agencies, local government websites, council broadcast data, candidates' social media (e.g., Twitter, Facebook), blogs, election information sites, and general social media (e.g., YouTube, Instagram). This data collection uses scraping techniques and APIs.

[0374] The server applies natural language processing (NLP) algorithms to the collected data, extracting and classifying information such as candidates' names, policies, and statements from the text data. For example, it might extract "statements on environmental policy" and analyze the positive / negative sentiment associated with those statements.

[0375] Registration to the database

[0376] The server converts the analyzed information into a structured format (e.g., JSON, XML) and registers it in the database. The database stores indexed information on each candidate, ranging from basic information (name, age, political party affiliation, etc.) to policy-specific statements and activity history.

[0377] Providing a user interface

[0378] The device provides an intuitive user interface via a web browser or dedicated app when accessed by the user. Users can utilize search fields and filter options to select policies or constituencies of interest.

[0379] Comparison of candidates' proposals

[0380] When a user enters specific interests or policies, the device sends that information to a server, which then retrieves information on suitable candidates from a database. For example, a user interested in environmental issues or education would search for candidates with experience in those areas.

[0381] The server selects highly suitable candidates based on user input and sends a list of them to the terminal. The terminal displays a list of candidates' photos, summaries, and policy positions. Users can also select multiple candidates and compare their policies and track records.

[0382] Tracking parliamentary activities

[0383] After the election, the server continuously collects information on the activities of the elected representatives. For example, it updates the database with information such as parliamentary attendance rates, the number of bills introduced, and the content of their social media posts.

[0384] If users want to check the activities of elected representatives after their election, they can view the latest information through their devices. This allows users to continuously monitor the performance of the representatives they voted for.

[0385] Specific example

[0386] This example shows a user's search for candidates based on their particular interest in environmental policy. The user enters information emphasizing "environmental policy" and "childcare support," and the server searches its database for matching candidates. As a result, a list of candidates with a proven track record in environmental policy and a strong commitment to childcare support is displayed. Each candidate's past statements and specific policy achievements are also shown, allowing the user to compare and select the most suitable candidate.

[0387] Thus, this system not only allows voters to easily obtain information about candidates and support appropriate decision-making, but also enables them to follow the activities of their representatives even after the election. This promotes voter participation in politics and contributes to the healthy functioning of democracy.

[0388] The following describes the processing flow.

[0389] Step 1:

[0390] The server collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media, blogs, election information sites, and general social media. This collection uses web scraping techniques and APIs, and data is retrieved periodically according to a specified schedule.

[0391] Step 2:

[0392] The server processes the collected data using natural language processing (NLP) algorithms to extract and analyze necessary information from the text data (such as candidate names, policies, statements, and emotional tone). The analyzed data is then converted into a structured format (such as JSON or XML).

[0393] Step 3:

[0394] The server registers the analyzed and structured data into a database. The database stores and indexes basic information about each candidate, their statements on specific policies, past performance, and electoral district information.

[0395] Step 4:

[0396] The device provides a user interface via a web browser or dedicated app when accessed by the user. The user interface includes search fields and filter options based on interests, allowing users to select policies or constituencies of interest.

[0397] Step 5:

[0398] The user enters their interests, desired policies, specific electoral districts, and candidate names. The device then transmits this input data to the server in real time.

[0399] Step 6:

[0400] The server searches the database for suitable candidates based on the user's input. The search results list candidates ranked by their relevance to the entered interests and policies.

[0401] Step 7:

[0402] The server sends the search results to the terminal. This includes the candidate's basic information, policy list, past statements, and track record.

[0403] Step 8:

[0404] The device displays search results visually to the user. Candidates' photos, names, political affiliations, policies, and achievements are displayed in a list format that can be understood at a glance.

[0405] Step 9:

[0406] If a user wants to select multiple candidates from the displayed list and perform a detailed comparison, they can choose a comparison option.

[0407] Step 10:

[0408] The device displays a comparative view of the policies, achievements, and statements of multiple selected candidates. A visual comparison table is generated for each item, making it easy for users to understand the differences.

[0409] Step 11:

[0410] After the election, the server continuously collects information on the activities of the elected representatives. In addition to the information collection process described above, attendance rates in parliament, the number of bills introduced, and statements made on social media are also tracked.

[0411] Step 12:

[0412] If a user wants to check the activities of their elected officials after an election, the latest information will be displayed on their device. The data includes the latest reports on the officials' activities, records of their speeches in parliament, and recent social media posts.

[0413] Through these steps, voters can quickly and easily obtain information about candidates and make informed decisions. Furthermore, they can continue to follow the activities of their representatives after the election, contributing to the healthy functioning of democracy.

[0414] (Example 1)

[0415] Next, we will describe Example 1. 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".

[0416] Modern voters struggle to efficiently and accurately obtain the information necessary to choose the right candidate in an election. Relying on traditional media or specific sources often results in biased or insufficient information. Furthermore, there are limited means to continuously follow the activities of elected officials after the election. As a result, it is difficult for voters to actively participate in politics and support the healthy functioning of democracy.

[0417] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0418] In this invention, the server includes means for collecting information, means for analyzing information, means for registering in a database, means for a user interface, means for proposing candidates, means for comparing candidates, means for tracking the activities of legislators, means for searching for information from a database based on user input and selecting candidates with a high degree of suitability, means for collecting data using scraping techniques or APIs, means for analyzing text data by applying natural language processing algorithms, converting it into a structured format and registering it in a database, means for providing an intuitive user interface, and means for continuously collecting information on the activities of legislators after their election and updating the database. This makes it possible for voters to quickly and efficiently obtain detailed information on candidates and to continuously monitor the activities of legislators even after the election.

[0419] "Information gathering methods" refer to means of collecting data from public institution websites, parliamentary broadcast data, social media, and blogs.

[0420] "Information analysis means" refers to means for analyzing data collected using natural language processing algorithms, and for extracting and classifying textual information.

[0421] A "database registration method" is a means of converting analyzed information into a structured format and registering it in a database.

[0422] A "user interface means" is a means of providing an intuitive user interface that allows users to search for and compare information.

[0423] A "candidate suggestion method" is a means of searching for information from a database based on user input and selecting candidates with a high degree of suitability.

[0424] A "candidate comparison tool" is a means that allows users to compare information on multiple candidates in detail.

[0425] "Methods for tracking parliamentary activities" refer to means for continuously collecting information on the activities of elected members of parliament and updating the database accordingly.

[0426] "Web scraping" is a technique for automatically obtaining data from websites.

[0427] "API" stands for Application Programming Interface, and refers to an interface that enables data exchange between different software programs.

[0428] A "natural language processing algorithm" is an algorithm that analyzes text data and interprets, understands, and generates human language.

[0429] A "structured format" is a format that arranges data according to specific rules, making it easier to analyze mechanically. Examples include JSON and XML.

[0430] The present invention is a system that includes information gathering means, information analysis means, database registration means, user interface means, candidate proposal means, candidate comparison means, and legislator activity tracking means. As an example of implementing the present invention, the following program configuration and specific processing steps will be described.

[0431] Information gathering and analysis

[0432] The server first periodically collects data from government websites, parliamentary broadcast data, social media (e.g., Twitter, Facebook), blogs, election information sites, and general social media (e.g., YouTube, Instagram). This collection utilizes scraping techniques using Python's Scrapy library and Beautiful Soup, as well as APIs from each data source.

[0433] The server applies natural language processing (NLP) algorithms to the collected data. It analyzes text data using Python's NLTK library and spaCy. Specifically, it extracts and classifies information such as candidates' names, policies, and statements. For example, it extracts statements related to environmental policy and analyzes positive / negative sentiment based on specific keywords.

[0434] Registration to the database

[0435] The server converts the parsed information into a structured format (e.g., JSON, XML) and registers it in the database. MySQL or PostgreSQL are used as the database. Specifically, SQLAlchemy is used to connect to the database and insert data into the appropriate tables.

[0436] Providing a user interface

[0437] The device provides an intuitive user interface via a web browser or dedicated app when accessed by the user. React.js or Vue.js are used for the frontend, while Django or Flask are used for managing the API in the backend.

[0438] Users can enter their areas of interest, such as policies or constituencies, into the search field and use filter options to narrow down candidates based on specific criteria.

[0439] Comparison of candidates' proposals

[0440] When a user enters specific interests or policies, the terminal sends that information to the server. The communication protocol used here is HTTP.

[0441] The server queries the database based on user input to retrieve information on highly suitable candidates. It extracts data using SQL WHERE statements and filtering functions.

[0442] The device displays this information in a list format. Users can compare the policies and track records of multiple candidates in detail.

[0443] Tracking parliamentary activities

[0444] The server continuously collects information on the activities of elected representatives and updates the database. This includes parliamentary attendance rates, the number of bills introduced, and statements made on social media. This data is also collected via scraping and APIs.

[0445] If users want to check the activities of elected representatives after their election, they can view the latest information through their devices.

[0446] Specific example

[0447] If a user has a particular interest in environmental policy, this example illustrates how they can enter that information to search for candidates.

[0448] First, the user enters information emphasizing "environmental policy" and "childcare support" into the search field. Next, the device sends this information to the server. The server queries the database for candidates that match these criteria and retrieves the results. The device displays a list of candidates with a proven track record in environmental policy and a proactive stance on childcare support, along with detailed information on each candidate's past statements and specific policy achievements. Based on this information, the user can compare candidates and select the appropriate one.

[0449] The following is an example of a prompt statement:

[0450] "I am interested in environmental policy. Could you recommend any candidates with a proven track record in environmental policy?"

[0451] "Please search for candidates who are proactive in supporting childcare."

[0452] Thus, this system not only allows voters to easily obtain information about candidates and support appropriate decision-making, but also enables them to follow the activities of their representatives even after the election. This promotes voter participation in politics and contributes to the healthy functioning of democracy.

[0453] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0454] Step 1:

[0455] The server collects data from government websites, parliamentary broadcast data, social media, and blogs. Specifically, it performs web scraping using Python's Scrapy library and Beautiful Soup, and retrieves data using APIs. The input is the URL of each site or API, and the output is the collected raw data.

[0456] Step 2:

[0457] The server analyzes the collected raw data using natural language processing (NLP) algorithms. Using Python's NLTK library and spaCy, it extracts and classifies information such as candidate names, policies, and statements from the text data. The input is the raw data collected in step 1, and the output is structured data as a result of the analysis.

[0458] Step 3:

[0459] The server converts the parsed structured data into formats such as JSON or XML. It uses Python's json library or xml.etree.ElementTree. The input is the data parsed in step 2, and the output is the data converted into a structured format.

[0460] Step 4:

[0461] The server registers the converted structured data into a database. MySQL or PostgreSQL is used as the database, and SQLAlchemy is used for database operations. The input is the data converted in step 3, and the output is the data registered in the database.

[0462] Step 5:

[0463] The device provides an intuitive user interface via a web browser or dedicated app when accessed by the user. React.js or Vue.js are used for the frontend. Input is the user's access request, and output is the display of the interface.

[0464] Step 6:

[0465] Users enter the policies or electoral districts they are interested in into the search field. For example, they might enter keywords such as "environmental policy" or "childcare support." The input is the user's search query, and the output is the search request to the device.

[0466] Step 7:

[0467] The terminal sends the search query entered by the user to the server. The communication protocol used here is HTTP. The input is the search query entered by the user in step 6, and the output is the HTTP request to the server.

[0468] Step 8:

[0469] The server searches the database based on the received search query and retrieves information on matching candidates. It extracts data using SQL WHERE statements and filtering functions. The input is an HTTP request to the server, and the output is information on matching candidates.

[0470] Step 9:

[0471] The terminal displays candidate information received from the server to the user in a list format. React.js and Vue.js components are used here. The input is candidate information from the server, and the output is the display of candidate information to the user.

[0472] Step 10:

[0473] Users compare information on multiple candidates. Specifically, they use filter options and display formats to compare each candidate's policies and track record in detail. The input is the candidate information displayed on the terminal, and the output is the user's comparison results.

[0474] Step 11:

[0475] The server continuously collects information on the activities of elected representatives. It collects data from public institution websites and social media using scraping and APIs. The input is the URL of each site or API, and the output is the collected activity data of the representatives.

[0476] Step 12:

[0477] The server updates the database with the collected information on the activities of the legislators. Database operations are performed using libraries such as SQLAlchemy. The input is the data collected in step 11, and the output is the updated database.

[0478] Step 13:

[0479] Users can view the latest information on the activities of elected representatives through their devices if they wish to check the status of their activities after being elected. The input is the user's viewing request, and the output is the latest activity information of the representative displayed on the device.

[0480] (Application Example 1)

[0481] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0482] Modern political information is vast, making it difficult for voters to obtain the information they need quickly and accurately. Furthermore, many voters struggle to keep track of their politicians' activities after an election. Meanwhile, in today's world of widespread autonomous vehicles, there is a need for a system that allows for efficient acquisition of political information while on the go. This invention aims to solve this problem and promote political participation by providing voters with relevant information in a timely manner.

[0483] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0484] In this invention, the server includes means for collecting information, means for analyzing information, means for registering in a database, means for a user interface, means for proposing candidates, means for comparing candidates, means for tracking the activities of legislators, means for coordinating with an in-vehicle display unit, and means for displaying candidate information on the in-vehicle display unit based on the interests of general users. This allows voters to easily obtain information on policies and candidates of interest to them, even while on the move, and to make appropriate decisions.

[0485] "Information gathering methods" refer to means of regularly collecting data from election management agencies, local government websites, parliamentary proceedings data, candidates' social media, blogs, election information websites, and general social media.

[0486] "Information analysis means" refers to methods that apply natural language processing algorithms to collected data to extract and classify information such as candidates' names, policies, and statements from text data.

[0487] A "database registration method" is a means of converting the analyzed information into a structured format (e.g., JSON, XML) and registering it in a database.

[0488] A "user interface means" refers to a means by which users can access an intuitive user interface through a web browser or dedicated application, and utilize search fields and filter options to select policies or electoral districts of interest.

[0489] A "candidate suggestion method" is a system that, when a user inputs specific interests or policies, retrieves information on suitable candidates from a database based on that information and selects candidates with a high degree of suitability.

[0490] A "candidate comparison tool" is a means by which users can select multiple candidates and compare and display their respective policies and track records.

[0491] A "method for tracking the activities of elected officials" refers to a means of continuously collecting information on the activities of officials who have been elected after an election and updating a database accordingly.

[0492] An "in-vehicle display unit" is hardware that functions as a display device inside an autonomous vehicle.

[0493] "Means for displaying candidate information on in-vehicle display units based on the interests of general users" refers to means of displaying information related to policies and elections that users are interested in, in an appropriate format, on in-vehicle display units.

[0494] The present invention is a system that works in conjunction with an in-vehicle display unit to provide political information to users of autonomous vehicles. This system includes means for collecting information, means for analyzing information, means for registering in a database, means for a user interface, means for proposing candidates, means for comparing candidates, means for tracking the activities of legislators, means for working in conjunction with an in-vehicle display unit, and means for displaying candidate information on the in-vehicle display unit based on the interests of general users.

[0495] hardware

[0496] The system uses the following hardware:

[0497] Server: A central computer that performs tasks such as data collection, analysis, and registration in databases.

[0498] In-vehicle display unit: A device for displaying information inside an autonomous vehicle.

[0499] User terminals (smartphones and tablets): Used for auxiliary operations and checking information.

[0500] software

[0501] The system uses the following software:

[0502] Flask: A lightweight web application framework for Python

[0503] spaCy and NLTK: Libraries for implementing natural language processing algorithms

[0504] PostgreSQL: A relational database for storing analysis results.

[0505] Data processing and data calculation

[0506] 1. Information Gathering Methods: The server regularly collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media, blogs, election information websites, and general social media. Scraping techniques and public APIs are used for data collection.

[0507] 2. Information Analysis Method: The server applies natural language processing (NLP) algorithms to the collected data to extract and classify information such as candidates' names, policies, and statements from the text data. Libraries such as spaCy and NLTK are used. This analysis enables the analysis of statements and sentiments regarding specific policies.

[0508] 3. Database Registration Method: The parsed information is converted into a structured format and stored in a PostgreSQL database. This indexes the information, making it quickly searchable and retrievalable.

[0509] 4. User Interface: Search fields and filter options are provided via a web browser or dedicated app for users to select policies and electoral districts of interest. This allows users to intuitively manipulate information. The user interface is integrated with the in-car display unit.

[0510] 5. Candidate Suggestion Method: When a user enters specific interests or policies into the in-car display unit, that information is sent to a server, which retrieves information on suitable candidates from the database. This allows the system to suggest the most suitable candidates based on the user's interests.

[0511] 6. Candidate Comparison Method: Users can select multiple candidates and compare their policies and track records. This makes it easier for users to compare and consider their options.

[0512] 7. Tracking Legislative Activities: After the election, the server continuously collects information on the activities of elected legislators (parliamentary attendance rate, number of bills introduced, social media posts, etc.) and updates the database. Users can view the latest information through the in-car display unit.

[0513] Specific example

[0514] When a user enters "environmental policy" and "childcare support" as areas of interest into the in-car display unit, the server retrieves relevant candidate information from the database and displays a list of the candidates' past statements and specific policy achievements. This allows users to efficiently obtain information even while on the go.

[0515] Examples of prompts for a generative AI model:

[0516] "Please search for information on candidates related to environmental policy and childcare support. You can choose from the following candidates:"

[0517] Candidate A: Achievements in environmental policy and initiatives in childcare support.

[0518] Candidate B: Number of statements on environmental policy, specific achievements in childcare support.

[0519] Please compare and select the most suitable candidate.

[0520] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0521] Step 1:

[0522] The server collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media, blogs, election information sites, and general social media. It uses scraping techniques and public APIs for data collection, and continuously collects data according to a specific time schedule. Inputs are the URLs and API keys of each information source, and output is the collected source data.

[0523] Step 2:

[0524] The server applies natural language processing (NLP) algorithms to the collected data, extracting and classifying information such as candidates' names, policies, and statements from the text data. Specifically, it uses libraries such as spaCy and NLTK to parse the text and perform sentiment analysis. The input is the original text data collected in step 1, and the output is the analyzed structured data (in text format).

[0525] Step 3:

[0526] The server converts the parsed information into a structured format (e.g., JSON, XML) and registers it in a PostgreSQL database. This indexes the information so that it can be quickly searched and retrieved. The input is the structured data obtained in step 2, and the output is the information registered in the database.

[0527] Step 4:

[0528] The user operates the in-car display unit or smartphone and inputs information through search fields and filter options to select policies or electoral districts of interest. This generates a search query based on those interests. The input is the search criteria entered by the user into the device, and the output is the generated search query.

[0529] Step 5:

[0530] The server receives a search query submitted by the user and retrieves information on suitable candidates from the database. The input is the search query from step 4, and the output is a list of candidate information that matches the search query.

[0531] Step 6:

[0532] The server converts the acquired candidate information into a format suitable for the user interface and sends it to the in-car display unit or smartphone. The input is the candidate information list acquired in step 5, and the output is the information in the appropriate format to be displayed in the GUI (Graphical User Interface).

[0533] Step 7:

[0534] The user views a list of candidates' policies and achievements via an in-car display unit or smartphone, and selects and compares multiple candidates. The input is the candidate information displayed in step 6, and the output is the information of the compared candidates.

[0535] Step 8:

[0536] The server periodically collects information on the activities of elected legislators (such as parliamentary attendance rates, number of bills introduced, and social media posts) even after the election, and updates the database. Inputs are the URLs and API keys of each information source, and output is the updated information on the legislators' activities.

[0537] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0538] The present invention is a system that includes information gathering means, information analysis means, database registration means, user interface means, candidate suggestion means, candidate comparison means, legislator activity tracking means, and an emotion engine. As an example of implementing this system, the following program configuration and specific processing steps are described.

[0539] Information gathering and analysis

[0540] The server periodically collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media (e.g., Twitter, Facebook), blogs, election information sites, and general social media (e.g., YouTube, Instagram). This collection is performed according to a specified schedule using web scraping techniques and APIs.

[0541] The server analyzes the collected data using natural language processing (NLP) algorithms, extracting and classifying necessary information (candidate names, policies, statements, emotional tone, etc.) from the text data. The analyzed data is then converted into a structured format (e.g., JSON, XML).

[0542] Registration to the database

[0543] The server registers the analyzed and structured data into a database. This database stores indexed information such as each candidate's basic information, policy statements, past achievements, and electoral district information.

[0544] Providing a user interface

[0545] The device provides an intuitive user interface via a web browser or dedicated app upon user access. This interface includes search fields and filter options based on interests, allowing users to select policies or constituencies of interest.

[0546] Introducing an emotional engine

[0547] The device uses an emotion engine to recognize emotions from the text and voice input by the user. The emotion engine analyzes the user's input text and voice to determine the type of emotion (e.g., joy, anger, sadness, etc.). Based on this determination, the server selects the most suitable candidate information.

[0548] Comparison of candidates' proposals

[0549] Users input specific interests or policies, and their emotions at the time are also recorded. The device sends this input data to the server in real time.

[0550] The server searches the database for the most suitable candidates based on the user's input information and sentiments. The search results list candidates ranked by relevance, matching the entered interests, policies, and sentiments.

[0551] The server sends search results to the device, providing information on suitable candidates, taking sentiment into consideration. The device displays a list of candidates, including their photos, names, political affiliations, policies, and track records, in an easily understandable format.

[0552] Tracking parliamentary activities

[0553] After the election, the server continuously collects information on the activities of the elected representatives. In addition to the information gathering process before the election, it regularly updates the database with activity information such as parliamentary attendance rates, the number of bills introduced, and statements made on social media.

[0554] If a user wants to check the activities of their elected officials after an election, the latest information will be displayed on their device. This includes the latest reports on the officials' activities, transcripts of their speeches in parliament, and recent social media posts.

[0555] Specific example

[0556] For example, suppose a user has a particular interest in environmental policy and enters information on this topic. Simultaneously, if the server recognizes the user's emotions (e.g., "anxiety") from the input, it searches its database for candidates who can provide information that alleviates anxiety (e.g., candidates with policies that appeal to a sense of security). As a result, a list of candidates who are proactive in environmental policy but also propose policies that reduce the user's anxiety is displayed.

[0557] Thus, this system allows voters to quickly and easily obtain information about candidates and make appropriate decisions. Furthermore, by customizing information while considering the user's emotions, it provides a more personalized experience. In addition, it contributes to the healthy functioning of democracy by allowing voters to continue following the activities of their representatives even after the election.

[0558] The following describes the processing flow.

[0559] Step 1:

[0560] The server periodically collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media, blogs, election information sites, and general social media. This collection is performed using web scraping techniques and APIs, and is carried out according to a specified schedule.

[0561] Step 2:

[0562] The server processes the collected data using natural language processing (NLP) algorithms to extract and analyze necessary information from the text data (such as the candidate's name, policies, statements, and emotional tone). The analyzed data is then converted into a structured format (such as JSON or XML).

[0563] Step 3:

[0564] The server registers the analyzed and structured data into a database. This database stores indexed information such as each candidate's basic information, policy statements, past achievements, and electoral district information.

[0565] Step 4:

[0566] The device provides a user interface via a web browser or dedicated app upon user access. This user interface includes search fields and filter options based on interests, allowing users to select policies or constituencies of interest.

[0567] Step 5:

[0568] Users input their interests, desired policies, specific electoral districts, and candidate names. The device transmits this input data to the server in real time. Additionally, an emotion engine analyzes the user's emotions from their input data and voice, and transmits that information to the server as well.

[0569] Step 6:

[0570] The server searches the database for suitable candidates based on the user's input and analyzed sentiment data. The search results list candidates ranked by relevance, matching the user's interests, policies, and sentiments.

[0571] Step 7:

[0572] The server sends search results to the terminal. This includes basic information about the candidate, a list of policies, past statements, and achievements. It also provides information on suitable candidates, taking sentiment into consideration.

[0573] Step 8:

[0574] The device displays search results to the user visually. Candidates' photos, names, political affiliations, policies, and achievements are presented in a list format that allows for quick and easy viewing. Suggestions based on sentiment data are also highlighted.

[0575] Step 9:

[0576] If a user wants to select multiple candidates from the displayed list and perform a detailed comparison, they can choose a comparison option.

[0577] Step 10:

[0578] The device displays a comparative view of the policies, achievements, and statements of multiple selected candidates. A visual comparison table is generated for each item, making it easy for users to understand the differences.

[0579] Step 11:

[0580] After the election, the server continuously collects information on the activities of the elected representatives. In addition to the information gathering process before the election, it updates the database with information such as parliamentary attendance rates, the number of bills introduced, and social media posts.

[0581] Step 12:

[0582] If users want to check the activities of their elected officials after the election, the latest information will be displayed on their devices. This includes the latest reports on their activities, transcripts of their speeches in parliament, and their latest social media posts.

[0583] Through these steps, voters can quickly and easily obtain information about candidates and make informed decisions. By customizing the information to take into account emotions such as anxieties and interests, a more personalized experience is provided. Furthermore, voters can continue to follow the activities of their representatives after the election, contributing to the healthy functioning of democracy.

[0584] (Example 2)

[0585] Next, we will describe Example 2. 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".

[0586] Traditional election candidate information systems lacked sufficient information collection and analysis, making it difficult for voters to quickly and accurately obtain detailed information on specific policies or candidates of interest. Furthermore, they failed to consider voter sentiment, resulting in a lack of personalized user experiences. Additionally, post-election tracking of legislative activities was neglected, potentially hindering the healthy functioning of democracy.

[0587] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0588] In this invention, the server includes information gathering means, information analysis means, database registration means, user interface means, candidate suggestion means, candidate comparison means, legislator activity tracking means, emotion recognition means, data structuring means, and recommendation result display means. This enables voters to quickly obtain candidate information and provides personalized information that takes emotions into consideration. Furthermore, it enables continuous tracking of legislator activities after elections, supporting the healthy operation of democracy.

[0589] "Information gathering methods" refer to means of regularly collecting data from election management agencies, local government websites, parliamentary proceedings data, candidates' social media, blogs, election information websites, and general social media.

[0590] "Information analysis means" refers to means of analyzing data collected using natural language processing algorithms, extracting and classifying textual information, and further determining emotional tone from the collected data.

[0591] A "database registration method" is a means of registering analyzed and structured data in a database, indexing it, and storing it.

[0592] "User interface means" refers to means that provide an intuitive interface when a user accesses something, and includes search fields and filter options.

[0593] A "candidate suggestion method" is a means of searching for the most suitable candidates from a database based on user input information and sentiment, ranking them by suitability, and listing them.

[0594] A "candidate comparison method" is a means of displaying search results in a user-friendly format, allowing users to compare multiple candidates.

[0595] A "method for tracking parliamentary activities" refers to a means of continuously collecting information on the activities of elected members of parliament after an election and updating the database accordingly.

[0596] An "emotion recognition method" is a means of recognizing and analyzing emotions from text or voice input by the user.

[0597] A "data structuring method" is a means of converting parsed information into a structured format (e.g., JSON, XML).

[0598] A "recommendation result display method" is a means of displaying candidate information, based on the results of sentiment recognition, in a user-friendly format.

[0599] This invention provides a system that enables voters to quickly and accurately obtain information on election candidates and support more appropriate decision-making. This system includes information gathering means, information analysis means, database registration means, user interface means, candidate suggestion means, candidate comparison means, legislator activity tracking means, sentiment recognition means, data structuring means, and recommendation result display means. The specific implementation methods for each means are described below.

[0600] Information gathering and analysis

[0601] The server periodically collects data from election management agencies, local government websites, council broadcast data, candidate social media, blogs, election information sites, and general social media. This data collection is performed using Python's BeautifulSoup and various APIs. For example, it uses the Twitter API to collect tweets with the hashtag "election2023". Cron jobs are also used to perform periodic data collection.

[0602] The server analyzes the collected data using spaCy, a natural language processing (NLP) module. It extracts information such as the candidate's name, policies, statements, and emotional tone from the collected text data. Furthermore, NLTK's VADER is used for sentiment analysis to determine the emotional tone of the statements. For example, the statement "This bill is absolutely necessary" is analyzed as "positive."

[0603] Registration to the database

[0604] The server converts the analyzed data into JSON format and registers it in a MySQL database. The database stores indexed information such as each candidate's basic information, policy statements, past achievements, and electoral district information. For example, information such as "Name: Candidate A, Policy: Environmental protection, Emotion: Positive" is registered.

[0605] Providing a user interface

[0606] The device displays an interface developed with React when a user accesses the site via a web browser. Users can search for information on policies and candidates of interest through a search bar and filter options. For example, if a user selects "environmental policy" as their area of ​​interest, candidates related to that policy will be displayed.

[0607] Introducing an emotional engine

[0608] The device retrieves text entered by the user and sends it to an emotion engine (e.g., Google Cloud Natural Language API). For example, if the user enters "I'm worried about the future of the Earth," that text is sent to the emotion engine.

[0609] The server receives the analysis results from the emotion engine, and if the user's emotion is recognized as "anxiety," it uses that information to supplement the search criteria.

[0610] Comparison of candidates' proposals

[0611] The server searches a MySQL database for the most suitable candidates based on the user's interests and emotional information. For example, based on "environmental policy" and "anxiety," it identifies candidates with environmental policies that appeal to a sense of security.

[0612] The search results are sorted in a ranking format, with the datasets prepared in order of relevance. The server sends the search results to the device, which displays them in a user-friendly format. For example, a list of candidates' photos, names, political affiliations, policies, and achievements might be displayed.

[0613] Tracking parliamentary activities

[0614] After an election, the server periodically collects information on the legislators' activities, such as attendance rates, the number of bills introduced, and their social media activity. For example, it uses AWS Lambda to periodically call an API to retrieve the latest data on legislators and update the database.

[0615] If users want to check the activities of their elected officials after an election, they can view the latest information through their devices. For example, the content of recent statements made in parliament and attendance rates will be displayed.

[0616] Specific example

[0617] For example, if a user is particularly interested in environmental policy and enters the following prompt: "I'm worried about the future of the planet. Which candidate is proactive on environmental policy?", the emotion "anxiety" is recognized. As a result, the server searches for a candidate who instills a sense of reassurance, and candidate B is displayed at the top of the list. In this way, the user can quickly and easily obtain appropriate candidate information.

[0618] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0619] Step 1:

[0620] The server collects data from election management agency and local government websites, candidate social media, blogs, election information sites, and general social media. Specifically, it uses Python's BeautifulSoup and the APIs of each platform. For example, BeautifulSoup is used to extract candidate statements related to environmental policy from web pages. This collection is performed periodically by a Cron job. The input is website URLs or API endpoints, and the output is raw data.

[0621] Step 2:

[0622] The server analyzes the collected raw data using natural language processing (NLP) algorithms. Specifically, it uses spaCy to extract candidate names, policies, and statements from text data. It also uses NLTK's VADER for sentiment analysis to determine the emotional tone of a statement. For example, from the statement "I believe environmental protection is important," it extracts "environmental protection" as a policy and determines the emotional tone to be "positive." The input is raw data, and the output is analyzed data.

[0623] Step 3:

[0624] The server converts the parsed data into JSON format and registers it in the database. Specifically, it uses a MySQL database and applies indexes to enable fast searching. For example, it structures and stores data in the format "Name: Candidate A, Policy: Environmental Protection, Sentiment: Positive". The input is the parsed data, and the output is the data stored in the database.

[0625] Step 4:

[0626] The device provides an intuitive interface for users to access. Specifically, a web application developed with React is displayed, allowing users to search for information on policies and candidates of interest through a search bar and filter options. For example, typing "environmental policy" into the search bar will display a list of candidates related to that policy. The input is the user's search query, and the output is filtered candidate information.

[0627] Step 5:

[0628] The device sends the text entered by the user to the emotion engine. Specifically, it uses the Google Cloud Natural Language API or IBM Watson to analyze the text and determine the type of emotion. For example, if the user enters "I'm worried about the future of the Earth," that text is sent to the emotion engine, and the emotional tone is determined to be "anxious." The input is the user's text, and the output is the emotion analysis result.

[0629] Step 6:

[0630] The server searches for the most suitable candidate from the database based on the user's input information and sentiment information. Specifically, it identifies the most suitable candidate from the MySQL database based on "environmental policy" and "anxiety." For example, the database might detect that "Candidate B" proposes policies that provide a sense of security. The input is the user's interests and sentiment information, and the output is a list of candidates.

[0631] Step 7:

[0632] The server sends the search results to the terminal. The terminal displays the search results in a user-friendly format. Specifically, it lists the candidates' photos, names, political affiliations, policies they advocate, and past achievements. For example, "Candidate B, environmental protection, positive policies" might appear at the top. The input is a list of candidates, and the output is a list displayed on the user interface.

[0633] Step 8:

[0634] After the election, the server periodically collects information on the activities of legislators and updates the database. Specifically, it uses AWS Lambda and API Gateway to periodically retrieve information from various data sources, such as parliamentary attendance rates, the number of bills introduced, and social media posts. For example, "the number of bills recently introduced by legislator A" is collected. The input is periodic API calls, and the output is the latest legislator data.

[0635] Step 9:

[0636] If a user wants to check the activities of a legislator after an election, the latest information will be displayed on their device. Specifically, a dashboard developed with React will display the legislator's recent statements and activities. For example, "Legal Legislator B's latest statements" can be viewed. The input is the user's request, and the output is the latest legislator activity information.

[0637] (Application Example 2)

[0638] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0639] Current election information systems are cumbersome, requiring users to consult multiple sources to obtain policy and candidate information of interest. Furthermore, they struggle to attract interest in elections because they cannot suggest candidate information or election-related products tailored to users' emotions and interests.

[0640] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0641] In this invention, the server includes means for collecting information, means for analyzing information, means for registering in a database, means for a user interface, means for suggesting candidates, means for comparing candidates, means for tracking the activities of legislators, an emotion engine, means for providing election-related products and candidate information in a virtual store, and means for suggesting election-related products based on emotions and interests. This allows users to quickly obtain information on policies and candidates of interest through a single system, and to receive personalized information and product suggestions tailored to their emotions and interests.

[0642] "Information gathering methods" refer to means of collecting election-related information from election management agencies, local government websites, parliamentary proceedings data, candidates' social media, blogs, election information websites, and general social media.

[0643] "Information analysis means" refers to means of analyzing collected information using natural language processing algorithms to extract and classify necessary text information.

[0644] A "database registration method" is a means of registering analyzed information in a structured format into a database, indexing it, and storing it.

[0645] A "user interface means" is a means of providing an intuitive interface that users can operate, allowing them to input or search for information of interest.

[0646] A "candidate suggestion method" is a means of searching a database for and suggesting the most suitable candidate information based on the user's input information and emotions.

[0647] A "candidate comparison tool" is a means of displaying a list of information such as the policies advocated, track record, and sentiment analysis results of multiple candidates, allowing users to compare their information.

[0648] A "method for tracking parliamentary activities" refers to a system for continuously collecting information on the activities of elected members of parliament after the election and updating a database accordingly.

[0649] An "emotion engine" is an engine that analyzes emotions from user input text and voice, and provides data and product suggestions based on the results.

[0650] "Means of providing election-related goods and candidate information in a virtual store" refers to means of displaying election-related goods (e.g., posters, books, merchandise, etc.) and candidate information within a virtual store, making them easily accessible to users.

[0651] "A means of suggesting election-related products based on emotions and interests" refers to a means of suggesting the most suitable election-related products within a virtual store based on the user's emotions and interests.

[0652] A description of embodiments for carrying out the present invention will be provided.

[0653] System Configuration

[0654] First, as a means of gathering information, the server collects data from election management agencies, local government websites, council broadcast data, candidates' social media, blogs, election information sites, and general social media. This collection is performed periodically using web scraping techniques and APIs.

[0655] Hardware and software

[0656] Server: Performs information gathering, analysis, and database management.

[0657] Smartphones / Tablets: Provide a user interface

[0658] software:

[0659] Python: Implementation of an information analysis program

[0660] Requests: Data collection using HTTP communication

[0661] JSON: Data parsing and storage

[0662] NLP Library: Implementation of Natural Language Processing Algorithms

[0663] Emotion analysis engine: Extracts and analyzes user emotions.

[0664] Data analysis and registration

[0665] The server analyzes the collected data using natural language processing (NLP) algorithms, extracting necessary information from the text data (e.g., candidate names, policies, statements, emotional tone) and converting it into a structured format (e.g., JSON, XML). The analyzed data is indexed and stored in a database.

[0666] User Interface and Emotion Engine

[0667] When a user enters information via their device, the emotion engine analyzes that input to understand their emotions and provides relevant information based on their interests. Specifically, using a smartphone or tablet, users can input policies or topics they are interested in, and their emotions are automatically analyzed. This input data is transmitted to the server in real time.

[0668] Comparison with candidate proposals

[0669] The server searches its database for the most suitable candidates based on the user's input and sentiments. The search results list candidates ranked by relevance, matching the user's interests, policies, and sentiments. Information on the listed candidates is displayed through the user interface. Users can see a candidate's photo, name, political affiliation, policies, and track record at a glance.

[0670] Applications in virtual stores

[0671] The server also provides election-related merchandise and candidate information within the virtual store. For example, if a user is interested in "environmental policy," related posters, books, and merchandise will be displayed in the virtual store. Based on the user's emotions (e.g., "seeking reassurance"), the most suitable products will be suggested.

[0672] Examples of prompt statements

[0673] For example, if the user enters the following prompt:

[0674] I'm looking for election-related merchandise. I'm interested in "environmental policy," so please display posters and related products from candidates who advocate for environmentally conscious policies. Also, please provide candidate recommendations based on the user's sentiment (e.g., "I want to feel safe").

[0675] In this way, the system provides appropriate data based on users' interests and emotions, facilitating the acquisition of election-related information and suggesting election-related products and services. This enhances users' understanding of and interest in elections, supporting better decision-making.

[0676] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0677] Step 1:

[0678] The server regularly collects information from election management agencies, local government websites, parliamentary broadcast data, candidate social media accounts, blogs, election information sites, and general social media. It receives target website URLs and social media account information as input, retrieves data using web scraping techniques and APIs, and stores it as text data. The output is raw data.

[0679] Step 2:

[0680] The server analyzes the collected raw data using natural language processing (NLP) algorithms. Specifically, it extracts and classifies candidates' names, policies, statements, and emotional tone from the text data. Using the raw data obtained in step 1 as input, NLP extracts the necessary information as structured data. The output is structured data in JSON or XML format.

[0681] Step 3:

[0682] The server registers the analyzed and structured data into a database. This process indexes extracted candidate information, policies, sentiment tones, etc., making it efficiently searchable. It accepts structured data as input and stores it in the database. The output is an indexed database entry.

[0683] Step 4:

[0684] Users input policies and themes of interest via devices such as smartphones and tablets. The user's input data is analyzed by an emotion engine, which determines emotions from the input text and audio. The input consists of the user's text or voice, and the emotion analysis algorithm identifies the emotional tone. The output is the analyzed emotion data.

[0685] Step 5:

[0686] The server searches the database for the most suitable candidates based on the user's input and sentiment. It receives user interest and sentiment data as input and searches the database for matching candidates. These search results are ranked by relevance, and the output is a list of the most suitable candidates.

[0687] Step 6:

[0688] The search results are displayed immediately through the user interface (UI). The displayed information includes the candidate's photo, name, political affiliation, policies, and past achievements. Candidate information from search results is received as input and presented to the user in an intuitive UI format. The output is a visually displayed list of candidates.

[0689] Step 7:

[0690] In virtual stores offering election-related merchandise and candidate information, the system also suggests relevant products based on the user's emotions and interests. It uses data on the user's emotions and interests as input to find and suggest the most suitable products. The output displays a list of election-related products and reasons for recommendation.

[0691] In this way, by linking the server, terminal, and emotion engine, a system is realized that allows users to efficiently obtain election information and related products.

[0692] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0693] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0694] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0695] [Third Embodiment]

[0696] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0697] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0698] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0699] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0700] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0701] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0702] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0703] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0704] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0705] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0706] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0707] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0708] The present invention is a system that includes information gathering means, information analysis means, database registration means, user interface means, candidate proposal means, candidate comparison means, and legislator activity tracking means. As an example of implementing this system, the following program configuration and specific processing steps will be described.

[0709] Information gathering and analysis

[0710] The server first periodically collects data from election management agencies, local government websites, council broadcast data, candidates' social media (e.g., Twitter, Facebook), blogs, election information sites, and general social media (e.g., YouTube, Instagram). This data collection uses scraping techniques and APIs.

[0711] The server applies natural language processing (NLP) algorithms to the collected data, extracting and classifying information such as candidates' names, policies, and statements from the text data. For example, it might extract "statements on environmental policy" and analyze the positive / negative sentiment associated with those statements.

[0712] Registration to the database

[0713] The server converts the analyzed information into a structured format (e.g., JSON, XML) and registers it in the database. The database stores indexed information on each candidate, ranging from basic information (name, age, political party affiliation, etc.) to policy-specific statements and activity history.

[0714] Providing a user interface

[0715] The device provides an intuitive user interface via a web browser or dedicated app when accessed by the user. Users can utilize search fields and filter options to select policies or constituencies of interest.

[0716] Comparison of candidates' proposals

[0717] When a user enters specific interests or policies, the device sends that information to a server, which then retrieves information on suitable candidates from a database. For example, a user interested in environmental issues or education would search for candidates with experience in those areas.

[0718] The server selects highly suitable candidates based on user input and sends a list of them to the terminal. The terminal displays a list of candidates' photos, summaries, and policy positions. Users can also select multiple candidates and compare their policies and track records.

[0719] Tracking parliamentary activities

[0720] After the election, the server continuously collects information on the activities of the elected representatives. For example, it updates the database with information such as parliamentary attendance rates, the number of bills introduced, and the content of their social media posts.

[0721] If users want to check the activities of elected representatives after their election, they can view the latest information through their devices. This allows users to continuously monitor the performance of the representatives they voted for.

[0722] Specific example

[0723] This example shows a user's search for candidates based on their particular interest in environmental policy. The user enters information emphasizing "environmental policy" and "childcare support," and the server searches its database for matching candidates. As a result, a list of candidates with a proven track record in environmental policy and a strong commitment to childcare support is displayed. Each candidate's past statements and specific policy achievements are also shown, allowing the user to compare and select the most suitable candidate.

[0724] Thus, this system not only allows voters to easily obtain information about candidates and support appropriate decision-making, but also enables them to follow the activities of their representatives even after the election. This promotes voter participation in politics and contributes to the healthy functioning of democracy.

[0725] The following describes the processing flow.

[0726] Step 1:

[0727] The server collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media, blogs, election information sites, and general social media. This collection uses web scraping techniques and APIs, and data is retrieved periodically according to a specified schedule.

[0728] Step 2:

[0729] The server processes the collected data using natural language processing (NLP) algorithms to extract and analyze necessary information from the text data (such as candidate names, policies, statements, and emotional tone). The analyzed data is then converted into a structured format (such as JSON or XML).

[0730] Step 3:

[0731] The server registers the analyzed and structured data into a database. The database stores and indexes basic information about each candidate, their statements on specific policies, past performance, and electoral district information.

[0732] Step 4:

[0733] The device provides a user interface via a web browser or dedicated app when accessed by the user. The user interface includes search fields and filter options based on interests, allowing users to select policies or constituencies of interest.

[0734] Step 5:

[0735] The user enters their interests, desired policies, specific electoral districts, and candidate names. The device then transmits this input data to the server in real time.

[0736] Step 6:

[0737] The server searches the database for suitable candidates based on the user's input. The search results list candidates ranked by their relevance to the entered interests and policies.

[0738] Step 7:

[0739] The server sends the search results to the terminal. This includes the candidate's basic information, policy list, past statements, and track record.

[0740] Step 8:

[0741] The device displays search results visually to the user. Candidates' photos, names, political affiliations, policies, and achievements are displayed in a list format that can be understood at a glance.

[0742] Step 9:

[0743] If a user wants to select multiple candidates from the displayed list and perform a detailed comparison, they can choose a comparison option.

[0744] Step 10:

[0745] The device displays a comparative view of the policies, achievements, and statements of multiple selected candidates. A visual comparison table is generated for each item, making it easy for users to understand the differences.

[0746] Step 11:

[0747] After the election, the server continuously collects information on the activities of the elected representatives. In addition to the information collection process described above, attendance rates in parliament, the number of bills introduced, and statements made on social media are also tracked.

[0748] Step 12:

[0749] If a user wants to check the activities of their elected officials after an election, the latest information will be displayed on their device. The data includes the latest reports on the officials' activities, records of their speeches in parliament, and recent social media posts.

[0750] Through these steps, voters can quickly and easily obtain information about candidates and make informed decisions. Furthermore, they can continue to follow the activities of their representatives after the election, contributing to the healthy functioning of democracy.

[0751] (Example 1)

[0752] Next, we will describe Example 1. 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."

[0753] Modern voters struggle to efficiently and accurately obtain the information necessary to choose the right candidate in an election. Relying on traditional media or specific sources often results in biased or insufficient information. Furthermore, there are limited means to continuously follow the activities of elected officials after the election. As a result, it is difficult for voters to actively participate in politics and support the healthy functioning of democracy.

[0754] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0755] In this invention, the server includes means for collecting information, means for analyzing information, means for registering in a database, means for a user interface, means for proposing candidates, means for comparing candidates, means for tracking the activities of legislators, means for searching for information from a database based on user input and selecting candidates with a high degree of suitability, means for collecting data using scraping techniques or APIs, means for analyzing text data by applying natural language processing algorithms, converting it into a structured format and registering it in a database, means for providing an intuitive user interface, and means for continuously collecting information on the activities of legislators after their election and updating the database. This makes it possible for voters to quickly and efficiently obtain detailed information on candidates and to continuously monitor the activities of legislators even after the election.

[0756] "Information gathering methods" refer to means of collecting data from public institution websites, parliamentary broadcast data, social media, and blogs.

[0757] "Information analysis means" refers to means for analyzing data collected using natural language processing algorithms, and for extracting and classifying textual information.

[0758] A "database registration method" is a means of converting analyzed information into a structured format and registering it in a database.

[0759] A "user interface means" is a means of providing an intuitive user interface that allows users to search for and compare information.

[0760] A "candidate suggestion method" is a means of searching for information from a database based on user input and selecting candidates with a high degree of suitability.

[0761] A "candidate comparison tool" is a means that allows users to compare information on multiple candidates in detail.

[0762] "Methods for tracking parliamentary activities" refer to means for continuously collecting information on the activities of elected members of parliament and updating the database accordingly.

[0763] "Web scraping" is a technique for automatically obtaining data from websites.

[0764] "API" stands for Application Programming Interface, and refers to an interface that enables data exchange between different software programs.

[0765] A "natural language processing algorithm" is an algorithm that analyzes text data and interprets, understands, and generates human language.

[0766] A "structured format" is a format that arranges data according to specific rules, making it easier to analyze mechanically. Examples include JSON and XML.

[0767] The present invention is a system that includes information gathering means, information analysis means, database registration means, user interface means, candidate proposal means, candidate comparison means, and legislator activity tracking means. As an example of implementing the present invention, the following program configuration and specific processing steps will be described.

[0768] Information gathering and analysis

[0769] The server first periodically collects data from government websites, parliamentary broadcast data, social media (e.g., Twitter, Facebook), blogs, election information sites, and general social media (e.g., YouTube, Instagram). This collection utilizes scraping techniques using Python's Scrapy library and Beautiful Soup, as well as APIs from each data source.

[0770] The server applies natural language processing (NLP) algorithms to the collected data. It analyzes text data using Python's NLTK library and spaCy. Specifically, it extracts and classifies information such as candidates' names, policies, and statements. For example, it extracts statements related to environmental policy and analyzes positive / negative sentiment based on specific keywords.

[0771] Registration to the database

[0772] The server converts the parsed information into a structured format (e.g., JSON, XML) and registers it in the database. MySQL or PostgreSQL are used as the database. Specifically, SQLAlchemy is used to connect to the database and insert data into the appropriate tables.

[0773] Providing a user interface

[0774] The device provides an intuitive user interface via a web browser or dedicated app when accessed by the user. React.js or Vue.js are used for the frontend, while Django or Flask are used for managing the API in the backend.

[0775] Users can enter their areas of interest, such as policies or constituencies, into the search field and use filter options to narrow down candidates based on specific criteria.

[0776] Comparison of candidates' proposals

[0777] When a user enters specific interests or policies, the terminal sends that information to the server. The communication protocol used here is HTTP.

[0778] The server queries the database based on user input to retrieve information on highly suitable candidates. It extracts data using SQL WHERE statements and filtering functions.

[0779] The device displays this information in a list format. Users can compare the policies and track records of multiple candidates in detail.

[0780] Tracking parliamentary activities

[0781] The server continuously collects information on the activities of elected representatives and updates the database. This includes parliamentary attendance rates, the number of bills introduced, and statements made on social media. This data is also collected via scraping and APIs.

[0782] If users want to check the activities of elected representatives after their election, they can view the latest information through their devices.

[0783] Specific example

[0784] If a user has a particular interest in environmental policy, this example illustrates how they can enter that information to search for candidates.

[0785] First, the user enters information emphasizing "environmental policy" and "childcare support" into the search field. Next, the device sends this information to the server. The server queries the database for candidates that match these criteria and retrieves the results. The device displays a list of candidates with a proven track record in environmental policy and a proactive stance on childcare support, along with detailed information on each candidate's past statements and specific policy achievements. Based on this information, the user can compare candidates and select the appropriate one.

[0786] The following is an example of a prompt statement:

[0787] "I am interested in environmental policy. Could you recommend any candidates with a proven track record in environmental policy?"

[0788] "Please search for candidates who are proactive in supporting childcare."

[0789] Thus, this system not only allows voters to easily obtain information about candidates and support appropriate decision-making, but also enables them to follow the activities of their representatives even after the election. This promotes voter participation in politics and contributes to the healthy functioning of democracy.

[0790] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0791] Step 1:

[0792] The server collects data from government websites, parliamentary broadcast data, social media, and blogs. Specifically, it performs web scraping using Python's Scrapy library and Beautiful Soup, and retrieves data using APIs. The input is the URL of each site or API, and the output is the collected raw data.

[0793] Step 2:

[0794] The server analyzes the collected raw data using natural language processing (NLP) algorithms. Using Python's NLTK library and spaCy, it extracts and classifies information such as candidate names, policies, and statements from the text data. The input is the raw data collected in step 1, and the output is structured data as a result of the analysis.

[0795] Step 3:

[0796] The server converts the parsed structured data into formats such as JSON or XML. It uses Python's json library or xml.etree.ElementTree. The input is the data parsed in step 2, and the output is the data converted into a structured format.

[0797] Step 4:

[0798] The server registers the converted structured data into a database. MySQL or PostgreSQL is used as the database, and SQLAlchemy is used for database operations. The input is the data converted in step 3, and the output is the data registered in the database.

[0799] Step 5:

[0800] The device provides an intuitive user interface via a web browser or dedicated app when accessed by the user. React.js or Vue.js are used for the frontend. Input is the user's access request, and output is the display of the interface.

[0801] Step 6:

[0802] Users enter the policies or electoral districts they are interested in into the search field. For example, they might enter keywords such as "environmental policy" or "childcare support." The input is the user's search query, and the output is the search request to the device.

[0803] Step 7:

[0804] The terminal sends the search query entered by the user to the server. The communication protocol used here is HTTP. The input is the search query entered by the user in step 6, and the output is the HTTP request to the server.

[0805] Step 8:

[0806] The server searches the database based on the received search query and retrieves information on matching candidates. It extracts data using SQL WHERE statements and filtering functions. The input is an HTTP request to the server, and the output is information on matching candidates.

[0807] Step 9:

[0808] The terminal displays candidate information received from the server to the user in a list format. React.js and Vue.js components are used here. The input is candidate information from the server, and the output is the display of candidate information to the user.

[0809] Step 10:

[0810] Users compare information on multiple candidates. Specifically, they use filter options and display formats to compare each candidate's policies and track record in detail. The input is the candidate information displayed on the terminal, and the output is the user's comparison results.

[0811] Step 11:

[0812] The server continuously collects information on the activities of elected representatives. It collects data from public institution websites and social media using scraping and APIs. The input is the URL of each site or API, and the output is the collected activity data of the representatives.

[0813] Step 12:

[0814] The server updates the database with the collected information on the activities of the legislators. Database operations are performed using libraries such as SQLAlchemy. The input is the data collected in step 11, and the output is the updated database.

[0815] Step 13:

[0816] Users can view the latest information on the activities of elected representatives through their devices if they wish to check the status of their activities after being elected. The input is the user's viewing request, and the output is the latest activity information of the representative displayed on the device.

[0817] (Application Example 1)

[0818] Next, we will explain Application Example 1. In the following explanation, 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."

[0819] Modern political information is vast, making it difficult for voters to obtain the information they need quickly and accurately. Furthermore, many voters struggle to keep track of their politicians' activities after an election. Meanwhile, in today's world of widespread autonomous vehicles, there is a need for a system that allows for efficient acquisition of political information while on the go. This invention aims to solve this problem and promote political participation by providing voters with relevant information in a timely manner.

[0820] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0821] In this invention, the server includes means for collecting information, means for analyzing information, means for registering in a database, means for a user interface, means for proposing candidates, means for comparing candidates, means for tracking the activities of legislators, means for coordinating with an in-vehicle display unit, and means for displaying candidate information on the in-vehicle display unit based on the interests of general users. This allows voters to easily obtain information on policies and candidates of interest to them, even while on the move, and to make appropriate decisions.

[0822] "Information gathering methods" refer to means of regularly collecting data from election management agencies, local government websites, parliamentary proceedings data, candidates' social media, blogs, election information websites, and general social media.

[0823] "Information analysis means" refers to methods that apply natural language processing algorithms to collected data to extract and classify information such as candidates' names, policies, and statements from text data.

[0824] A "database registration method" is a means of converting the analyzed information into a structured format (e.g., JSON, XML) and registering it in a database.

[0825] A "user interface means" refers to a means by which users can access an intuitive user interface through a web browser or dedicated application, and utilize search fields and filter options to select policies or electoral districts of interest.

[0826] A "candidate suggestion method" is a system that, when a user inputs specific interests or policies, retrieves information on suitable candidates from a database based on that information and selects candidates with a high degree of suitability.

[0827] A "candidate comparison tool" is a means by which users can select multiple candidates and compare and display their respective policies and track records.

[0828] A "method for tracking the activities of elected officials" refers to a means of continuously collecting information on the activities of officials who have been elected after an election and updating a database accordingly.

[0829] An "in-vehicle display unit" is hardware that functions as a display device inside an autonomous vehicle.

[0830] "Means for displaying candidate information on in-vehicle display units based on the interests of general users" refers to means of displaying information related to policies and elections that users are interested in, in an appropriate format, on in-vehicle display units.

[0831] The present invention is a system that works in conjunction with an in-vehicle display unit to provide political information to users of autonomous vehicles. This system includes means for collecting information, means for analyzing information, means for registering in a database, means for a user interface, means for proposing candidates, means for comparing candidates, means for tracking the activities of legislators, means for working in conjunction with an in-vehicle display unit, and means for displaying candidate information on the in-vehicle display unit based on the interests of general users.

[0832] hardware

[0833] The system uses the following hardware:

[0834] Server: A central computer that performs tasks such as data collection, analysis, and registration in databases.

[0835] In-vehicle display unit: A device for displaying information inside an autonomous vehicle.

[0836] User terminals (smartphones and tablets): Used for auxiliary operations and checking information.

[0837] software

[0838] The system uses the following software:

[0839] Flask: A lightweight web application framework for Python

[0840] spaCy and NLTK: Libraries for implementing natural language processing algorithms

[0841] PostgreSQL: A relational database for storing analysis results.

[0842] Data processing and data calculation

[0843] 1. Information Gathering Methods: The server regularly collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media, blogs, election information websites, and general social media. Scraping techniques and public APIs are used for data collection.

[0844] 2. Information Analysis Method: The server applies natural language processing (NLP) algorithms to the collected data to extract and classify information such as candidates' names, policies, and statements from the text data. Libraries such as spaCy and NLTK are used. This analysis enables the analysis of statements and sentiments regarding specific policies.

[0845] 3. Database Registration Method: The parsed information is converted into a structured format and stored in a PostgreSQL database. This indexes the information, making it quickly searchable and retrievalable.

[0846] 4. User Interface: Search fields and filter options are provided via a web browser or dedicated app for users to select policies and electoral districts of interest. This allows users to intuitively manipulate information. The user interface is integrated with the in-car display unit.

[0847] 5. Candidate Suggestion Method: When a user enters specific interests or policies into the in-car display unit, that information is sent to a server, which retrieves information on suitable candidates from the database. This allows the system to suggest the most suitable candidates based on the user's interests.

[0848] 6. Candidate Comparison Method: Users can select multiple candidates and compare their policies and track records. This makes it easier for users to compare and consider their options.

[0849] 7. Tracking Legislative Activities: After the election, the server continuously collects information on the activities of elected legislators (parliamentary attendance rate, number of bills introduced, social media posts, etc.) and updates the database. Users can view the latest information through the in-car display unit.

[0850] Specific example

[0851] When a user enters "environmental policy" and "childcare support" as areas of interest into the in-car display unit, the server retrieves relevant candidate information from the database and displays a list of the candidates' past statements and specific policy achievements. This allows users to efficiently obtain information even while on the go.

[0852] Examples of prompts for a generative AI model:

[0853] "Please search for information on candidates related to environmental policy and childcare support. You can choose from the following candidates:"

[0854] Candidate A: Achievements in environmental policy and initiatives in childcare support.

[0855] Candidate B: Number of statements on environmental policy, specific achievements in childcare support.

[0856] Please compare and select the most suitable candidate.

[0857] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0858] Step 1:

[0859] The server collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media, blogs, election information sites, and general social media. It uses scraping techniques and public APIs for data collection, and continuously collects data according to a specific time schedule. Inputs are the URLs and API keys of each information source, and output is the collected source data.

[0860] Step 2:

[0861] The server applies natural language processing (NLP) algorithms to the collected data, extracting and classifying information such as candidates' names, policies, and statements from the text data. Specifically, it uses libraries such as spaCy and NLTK to parse the text and perform sentiment analysis. The input is the original text data collected in step 1, and the output is the analyzed structured data (in text format).

[0862] Step 3:

[0863] The server converts the parsed information into a structured format (e.g., JSON, XML) and registers it in a PostgreSQL database. This indexes the information so that it can be quickly searched and retrieved. The input is the structured data obtained in step 2, and the output is the information registered in the database.

[0864] Step 4:

[0865] The user operates the in-car display unit or smartphone and inputs information through search fields and filter options to select policies or electoral districts of interest. This generates a search query based on those interests. The input is the search criteria entered by the user into the device, and the output is the generated search query.

[0866] Step 5:

[0867] The server receives a search query submitted by the user and retrieves information on suitable candidates from the database. The input is the search query from step 4, and the output is a list of candidate information that matches the search query.

[0868] Step 6:

[0869] The server converts the acquired candidate information into a format suitable for the user interface and sends it to the in-car display unit or smartphone. The input is the candidate information list acquired in step 5, and the output is the information in the appropriate format to be displayed in the GUI (Graphical User Interface).

[0870] Step 7:

[0871] The user views a list of candidates' policies and achievements via an in-car display unit or smartphone, and selects and compares multiple candidates. The input is the candidate information displayed in step 6, and the output is the information of the compared candidates.

[0872] Step 8:

[0873] The server periodically collects information on the activities of elected legislators (such as parliamentary attendance rates, number of bills introduced, and social media posts) even after the election, and updates the database. Inputs are the URLs and API keys of each information source, and output is the updated information on the legislators' activities.

[0874] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0875] The present invention is a system that includes information gathering means, information analysis means, database registration means, user interface means, candidate suggestion means, candidate comparison means, legislator activity tracking means, and an emotion engine. As an example of implementing this system, the following program configuration and specific processing steps are described.

[0876] Information gathering and analysis

[0877] The server periodically collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media (e.g., Twitter, Facebook), blogs, election information sites, and general social media (e.g., YouTube, Instagram). This collection is performed according to a specified schedule using web scraping techniques and APIs.

[0878] The server analyzes the collected data using natural language processing (NLP) algorithms, extracting and classifying necessary information (candidate names, policies, statements, emotional tone, etc.) from the text data. The analyzed data is then converted into a structured format (e.g., JSON, XML).

[0879] Registration to the database

[0880] The server registers the analyzed and structured data into a database. This database stores indexed information such as each candidate's basic information, policy statements, past achievements, and electoral district information.

[0881] Providing a user interface

[0882] The device provides an intuitive user interface via a web browser or dedicated app upon user access. This interface includes search fields and filter options based on interests, allowing users to select policies or constituencies of interest.

[0883] Introducing an emotional engine

[0884] The device uses an emotion engine to recognize emotions from the text and voice input by the user. The emotion engine analyzes the user's input text and voice to determine the type of emotion (e.g., joy, anger, sadness, etc.). Based on this determination, the server selects the most suitable candidate information.

[0885] Comparison of candidates' proposals

[0886] Users input specific interests or policies, and their emotions at the time are also recorded. The device sends this input data to the server in real time.

[0887] The server searches the database for the most suitable candidates based on the user's input information and sentiments. The search results list candidates ranked by relevance, matching the entered interests, policies, and sentiments.

[0888] The server sends search results to the device, providing information on suitable candidates, taking sentiment into consideration. The device displays a list of candidates, including their photos, names, political affiliations, policies, and track records, in an easily understandable format.

[0889] Tracking parliamentary activities

[0890] After the election, the server continuously collects information on the activities of the elected representatives. In addition to the information gathering process before the election, it regularly updates the database with activity information such as parliamentary attendance rates, the number of bills introduced, and statements made on social media.

[0891] If a user wants to check the activities of their elected officials after an election, the latest information will be displayed on their device. This includes the latest reports on the officials' activities, transcripts of their speeches in parliament, and recent social media posts.

[0892] Specific example

[0893] For example, suppose a user has a particular interest in environmental policy and enters information on this topic. Simultaneously, if the server recognizes the user's emotions (e.g., "anxiety") from the input, it searches its database for candidates who can provide information that alleviates anxiety (e.g., candidates with policies that appeal to a sense of security). As a result, a list of candidates who are proactive in environmental policy but also propose policies that reduce the user's anxiety is displayed.

[0894] Thus, this system allows voters to quickly and easily obtain information about candidates and make appropriate decisions. Furthermore, by customizing information while considering the user's emotions, it provides a more personalized experience. In addition, it contributes to the healthy functioning of democracy by allowing voters to continue following the activities of their representatives even after the election.

[0895] The following describes the processing flow.

[0896] Step 1:

[0897] The server periodically collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media, blogs, election information sites, and general social media. This collection is performed using web scraping techniques and APIs, and is carried out according to a specified schedule.

[0898] Step 2:

[0899] The server processes the collected data using natural language processing (NLP) algorithms to extract and analyze necessary information from the text data (such as the candidate's name, policies, statements, and emotional tone). The analyzed data is then converted into a structured format (such as JSON or XML).

[0900] Step 3:

[0901] The server registers the analyzed and structured data into a database. This database stores indexed information such as each candidate's basic information, policy statements, past achievements, and electoral district information.

[0902] Step 4:

[0903] The device provides a user interface via a web browser or dedicated app upon user access. This user interface includes search fields and filter options based on interests, allowing users to select policies or constituencies of interest.

[0904] Step 5:

[0905] Users input their interests, desired policies, specific electoral districts, and candidate names. The device transmits this input data to the server in real time. Additionally, an emotion engine analyzes the user's emotions from their input data and voice, and transmits that information to the server as well.

[0906] Step 6:

[0907] The server searches the database for suitable candidates based on the user's input and analyzed sentiment data. The search results list candidates ranked by relevance, matching the user's interests, policies, and sentiments.

[0908] Step 7:

[0909] The server sends search results to the terminal. This includes basic information about the candidate, a list of policies, past statements, and achievements. It also provides information on suitable candidates, taking sentiment into consideration.

[0910] Step 8:

[0911] The device displays search results to the user visually. Candidates' photos, names, political affiliations, policies, and achievements are presented in a list format that allows for quick and easy viewing. Suggestions based on sentiment data are also highlighted.

[0912] Step 9:

[0913] If a user wants to select multiple candidates from the displayed list and perform a detailed comparison, they can choose a comparison option.

[0914] Step 10:

[0915] The device displays a comparative view of the policies, achievements, and statements of multiple selected candidates. A visual comparison table is generated for each item, making it easy for users to understand the differences.

[0916] Step 11:

[0917] After the election, the server continuously collects information on the activities of the elected representatives. In addition to the information gathering process before the election, it updates the database with information such as parliamentary attendance rates, the number of bills introduced, and social media posts.

[0918] Step 12:

[0919] If users want to check the activities of their elected officials after the election, the latest information will be displayed on their devices. This includes the latest reports on their activities, transcripts of their speeches in parliament, and their latest social media posts.

[0920] Through these steps, voters can quickly and easily obtain information about candidates and make informed decisions. By customizing the information to take into account emotions such as anxieties and interests, a more personalized experience is provided. Furthermore, voters can continue to follow the activities of their representatives after the election, contributing to the healthy functioning of democracy.

[0921] (Example 2)

[0922] Next, we will describe Example 2. 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."

[0923] Traditional election candidate information systems lacked sufficient information collection and analysis, making it difficult for voters to quickly and accurately obtain detailed information on specific policies or candidates of interest. Furthermore, they failed to consider voter sentiment, resulting in a lack of personalized user experiences. Additionally, post-election tracking of legislative activities was neglected, potentially hindering the healthy functioning of democracy.

[0924] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0925] In this invention, the server includes information gathering means, information analysis means, database registration means, user interface means, candidate suggestion means, candidate comparison means, legislator activity tracking means, emotion recognition means, data structuring means, and recommendation result display means. This enables voters to quickly obtain candidate information and provides personalized information that takes emotions into consideration. Furthermore, it enables continuous tracking of legislator activities after elections, supporting the healthy operation of democracy.

[0926] "Information gathering methods" refer to means of regularly collecting data from election management agencies, local government websites, parliamentary proceedings data, candidates' social media, blogs, election information websites, and general social media.

[0927] "Information analysis means" refers to means of analyzing data collected using natural language processing algorithms, extracting and classifying textual information, and further determining emotional tone from the collected data.

[0928] A "database registration method" is a means of registering analyzed and structured data in a database, indexing it, and storing it.

[0929] "User interface means" refers to means that provide an intuitive interface when a user accesses something, and includes search fields and filter options.

[0930] A "candidate suggestion method" is a means of searching for the most suitable candidates from a database based on user input information and sentiment, ranking them by suitability, and listing them.

[0931] A "candidate comparison method" is a means of displaying search results in a user-friendly format, allowing users to compare multiple candidates.

[0932] A "method for tracking parliamentary activities" refers to a means of continuously collecting information on the activities of elected members of parliament after an election and updating the database accordingly.

[0933] An "emotion recognition method" is a means of recognizing and analyzing emotions from text or voice input by the user.

[0934] A "data structuring method" is a means of converting parsed information into a structured format (e.g., JSON, XML).

[0935] A "recommendation result display method" is a means of displaying candidate information, based on the results of sentiment recognition, in a user-friendly format.

[0936] This invention provides a system that enables voters to quickly and accurately obtain information on election candidates and support more appropriate decision-making. This system includes information gathering means, information analysis means, database registration means, user interface means, candidate suggestion means, candidate comparison means, legislator activity tracking means, sentiment recognition means, data structuring means, and recommendation result display means. The specific implementation methods for each means are described below.

[0937] Information gathering and analysis

[0938] The server periodically collects data from election management agencies, local government websites, council broadcast data, candidate social media, blogs, election information sites, and general social media. This data collection is performed using Python's BeautifulSoup and various APIs. For example, it uses the Twitter API to collect tweets with the hashtag "election2023". Cron jobs are also used to perform periodic data collection.

[0939] The server analyzes the collected data using spaCy, a natural language processing (NLP) module. It extracts information such as the candidate's name, policies, statements, and emotional tone from the collected text data. Furthermore, NLTK's VADER is used for sentiment analysis to determine the emotional tone of the statements. For example, the statement "This bill is absolutely necessary" is analyzed as "positive."

[0940] Registration to the database

[0941] The server converts the analyzed data into JSON format and registers it in a MySQL database. The database stores indexed information such as each candidate's basic information, policy statements, past achievements, and electoral district information. For example, information such as "Name: Candidate A, Policy: Environmental protection, Emotion: Positive" is registered.

[0942] Providing a user interface

[0943] The device displays an interface developed with React when a user accesses the site via a web browser. Users can search for information on policies and candidates of interest through a search bar and filter options. For example, if a user selects "environmental policy" as their area of ​​interest, candidates related to that policy will be displayed.

[0944] Introducing an emotional engine

[0945] The device retrieves text entered by the user and sends it to an emotion engine (e.g., Google Cloud Natural Language API). For example, if the user enters "I'm worried about the future of the Earth," that text is sent to the emotion engine.

[0946] The server receives the analysis results from the emotion engine, and if the user's emotion is recognized as "anxiety," it uses that information to supplement the search criteria.

[0947] Comparison of candidates' proposals

[0948] The server searches a MySQL database for the most suitable candidates based on the user's interests and emotional information. For example, based on "environmental policy" and "anxiety," it identifies candidates with environmental policies that appeal to a sense of security.

[0949] The search results are sorted in a ranking format, with the datasets prepared in order of relevance. The server sends the search results to the device, which displays them in a user-friendly format. For example, a list of candidates' photos, names, political affiliations, policies, and achievements might be displayed.

[0950] Tracking parliamentary activities

[0951] After an election, the server periodically collects information on the legislators' activities, such as attendance rates, the number of bills introduced, and their social media activity. For example, it uses AWS Lambda to periodically call an API to retrieve the latest data on legislators and update the database.

[0952] If users want to check the activities of their elected officials after an election, they can view the latest information through their devices. For example, the content of recent statements made in parliament and attendance rates will be displayed.

[0953] Specific example

[0954] For example, if a user is particularly interested in environmental policy and enters the following prompt: "I'm worried about the future of the planet. Which candidate is proactive on environmental policy?", the emotion "anxiety" is recognized. As a result, the server searches for a candidate who instills a sense of reassurance, and candidate B is displayed at the top of the list. In this way, the user can quickly and easily obtain appropriate candidate information.

[0955] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0956] Step 1:

[0957] The server collects data from election management agency and local government websites, candidate social media, blogs, election information sites, and general social media. Specifically, it uses Python's BeautifulSoup and the APIs of each platform. For example, BeautifulSoup is used to extract candidate statements related to environmental policy from web pages. This collection is performed periodically by a Cron job. The input is website URLs or API endpoints, and the output is raw data.

[0958] Step 2:

[0959] The server analyzes the collected raw data using natural language processing (NLP) algorithms. Specifically, it uses spaCy to extract candidate names, policies, and statements from text data. It also uses NLTK's VADER for sentiment analysis to determine the emotional tone of a statement. For example, from the statement "I believe environmental protection is important," it extracts "environmental protection" as a policy and determines the emotional tone to be "positive." The input is raw data, and the output is analyzed data.

[0960] Step 3:

[0961] The server converts the parsed data into JSON format and registers it in the database. Specifically, it uses a MySQL database and applies indexes to enable fast searching. For example, it structures and stores data in the format "Name: Candidate A, Policy: Environmental Protection, Sentiment: Positive". The input is the parsed data, and the output is the data stored in the database.

[0962] Step 4:

[0963] The device provides an intuitive interface for users to access. Specifically, a web application developed with React is displayed, allowing users to search for information on policies and candidates of interest through a search bar and filter options. For example, typing "environmental policy" into the search bar will display a list of candidates related to that policy. The input is the user's search query, and the output is filtered candidate information.

[0964] Step 5:

[0965] The device sends the text entered by the user to the emotion engine. Specifically, it uses the Google Cloud Natural Language API or IBM Watson to analyze the text and determine the type of emotion. For example, if the user enters "I'm worried about the future of the Earth," that text is sent to the emotion engine, and the emotional tone is determined to be "anxious." The input is the user's text, and the output is the emotion analysis result.

[0966] Step 6:

[0967] The server searches for the most suitable candidate from the database based on the user's input information and sentiment information. Specifically, it identifies the most suitable candidate from the MySQL database based on "environmental policy" and "anxiety." For example, the database might detect that "Candidate B" proposes policies that provide a sense of security. The input is the user's interests and sentiment information, and the output is a list of candidates.

[0968] Step 7:

[0969] The server sends the search results to the terminal. The terminal displays the search results in a user-friendly format. Specifically, it lists the candidates' photos, names, political affiliations, policies they advocate, and past achievements. For example, "Candidate B, environmental protection, positive policies" might appear at the top. The input is a list of candidates, and the output is a list displayed on the user interface.

[0970] Step 8:

[0971] After the election, the server periodically collects information on the activities of legislators and updates the database. Specifically, it uses AWS Lambda and API Gateway to periodically retrieve information from various data sources, such as parliamentary attendance rates, the number of bills introduced, and social media posts. For example, "the number of bills recently introduced by legislator A" is collected. The input is periodic API calls, and the output is the latest legislator data.

[0972] Step 9:

[0973] If a user wants to check the activities of a legislator after an election, the latest information will be displayed on their device. Specifically, a dashboard developed with React will display the legislator's recent statements and activities. For example, "Legal Legislator B's latest statements" can be viewed. The input is the user's request, and the output is the latest legislator activity information.

[0974] (Application Example 2)

[0975] Next, we will explain application example 2. In the following explanation, 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."

[0976] Current election information systems are cumbersome, requiring users to consult multiple sources to obtain policy and candidate information of interest. Furthermore, they struggle to attract interest in elections because they cannot suggest candidate information or election-related products tailored to users' emotions and interests.

[0977] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0978] In this invention, the server includes means for collecting information, means for analyzing information, means for registering in a database, means for a user interface, means for suggesting candidates, means for comparing candidates, means for tracking the activities of legislators, an emotion engine, means for providing election-related products and candidate information in a virtual store, and means for suggesting election-related products based on emotions and interests. This allows users to quickly obtain information on policies and candidates of interest through a single system, and to receive personalized information and product suggestions tailored to their emotions and interests.

[0979] "Information gathering methods" refer to means of collecting election-related information from election management agencies, local government websites, parliamentary proceedings data, candidates' social media, blogs, election information websites, and general social media.

[0980] "Information analysis means" refers to means of analyzing collected information using natural language processing algorithms to extract and classify necessary text information.

[0981] A "database registration method" is a means of registering analyzed information in a structured format into a database, indexing it, and storing it.

[0982] A "user interface means" is a means of providing an intuitive interface that users can operate, allowing them to input or search for information of interest.

[0983] A "candidate suggestion method" is a means of searching a database for and suggesting the most suitable candidate information based on the user's input information and emotions.

[0984] A "candidate comparison tool" is a means of displaying a list of information such as the policies advocated, track record, and sentiment analysis results of multiple candidates, allowing users to compare their information.

[0985] A "method for tracking parliamentary activities" refers to a system for continuously collecting information on the activities of elected members of parliament after the election and updating a database accordingly.

[0986] An "emotion engine" is an engine that analyzes emotions from user input text and voice, and provides data and product suggestions based on the results.

[0987] "Means of providing election-related goods and candidate information in a virtual store" refers to means of displaying election-related goods (e.g., posters, books, merchandise, etc.) and candidate information within a virtual store, making them easily accessible to users.

[0988] "A means of suggesting election-related products based on emotions and interests" refers to a means of suggesting the most suitable election-related products within a virtual store based on the user's emotions and interests.

[0989] A description of embodiments for carrying out the present invention will be provided.

[0990] System Configuration

[0991] First, as a means of gathering information, the server collects data from election management agencies, local government websites, council broadcast data, candidates' social media, blogs, election information sites, and general social media. This collection is performed periodically using web scraping techniques and APIs.

[0992] Hardware and software

[0993] Server: Performs information gathering, analysis, and database management.

[0994] Smartphones / Tablets: Provide a user interface

[0995] software:

[0996] Python: Implementation of an information analysis program

[0997] Requests: Data collection using HTTP communication

[0998] JSON: Data parsing and storage

[0999] NLP Library: Implementation of Natural Language Processing Algorithms

[1000] Emotion analysis engine: Extracts and analyzes user emotions.

[1001] Data analysis and registration

[1002] The server analyzes the collected data using natural language processing (NLP) algorithms, extracting necessary information from the text data (e.g., candidate names, policies, statements, emotional tone) and converting it into a structured format (e.g., JSON, XML). The analyzed data is indexed and stored in a database.

[1003] User Interface and Emotion Engine

[1004] When a user enters information via their device, the emotion engine analyzes that input to understand their emotions and provides relevant information based on their interests. Specifically, using a smartphone or tablet, users can input policies or topics they are interested in, and their emotions are automatically analyzed. This input data is transmitted to the server in real time.

[1005] Comparison with candidate proposals

[1006] The server searches its database for the most suitable candidates based on the user's input and sentiments. The search results list candidates ranked by relevance, matching the user's interests, policies, and sentiments. Information on the listed candidates is displayed through the user interface. Users can see a candidate's photo, name, political affiliation, policies, and track record at a glance.

[1007] Applications in virtual stores

[1008] The server also provides election-related merchandise and candidate information within the virtual store. For example, if a user is interested in "environmental policy," related posters, books, and merchandise will be displayed in the virtual store. Based on the user's emotions (e.g., "seeking reassurance"), the most suitable products will be suggested.

[1009] Examples of prompt statements

[1010] For example, if the user enters the following prompt:

[1011] I'm looking for election-related merchandise. I'm interested in "environmental policy," so please display posters and related products from candidates who advocate for environmentally conscious policies. Also, please provide candidate recommendations based on the user's sentiment (e.g., "I want to feel safe").

[1012] In this way, the system provides appropriate data based on users' interests and emotions, facilitating the acquisition of election-related information and suggesting election-related products and services. This enhances users' understanding of and interest in elections, supporting better decision-making.

[1013] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1014] Step 1:

[1015] The server regularly collects information from election management agencies, local government websites, parliamentary broadcast data, candidate social media accounts, blogs, election information sites, and general social media. It receives target website URLs and social media account information as input, retrieves data using web scraping techniques and APIs, and stores it as text data. The output is raw data.

[1016] Step 2:

[1017] The server analyzes the collected raw data using natural language processing (NLP) algorithms. Specifically, it extracts and classifies candidates' names, policies, statements, and emotional tone from the text data. Using the raw data obtained in step 1 as input, NLP extracts the necessary information as structured data. The output is structured data in JSON or XML format.

[1018] Step 3:

[1019] The server registers the analyzed and structured data into a database. This process indexes extracted candidate information, policies, sentiment tones, etc., making it efficiently searchable. It accepts structured data as input and stores it in the database. The output is an indexed database entry.

[1020] Step 4:

[1021] Users input policies and themes of interest via devices such as smartphones and tablets. The user's input data is analyzed by an emotion engine, which determines emotions from the input text and audio. The input consists of the user's text or voice, and the emotion analysis algorithm identifies the emotional tone. The output is the analyzed emotion data.

[1022] Step 5:

[1023] The server searches the database for the most suitable candidates based on the user's input and sentiment. It receives user interest and sentiment data as input and searches the database for matching candidates. These search results are ranked by relevance, and the output is a list of the most suitable candidates.

[1024] Step 6:

[1025] The search results are displayed immediately through the user interface (UI). The displayed information includes the candidate's photo, name, political affiliation, policies, and past achievements. Candidate information from search results is received as input and presented to the user in an intuitive UI format. The output is a visually displayed list of candidates.

[1026] Step 7:

[1027] In virtual stores offering election-related merchandise and candidate information, the system also suggests relevant products based on the user's emotions and interests. It uses data on the user's emotions and interests as input to find and suggest the most suitable products. The output displays a list of election-related products and reasons for recommendation.

[1028] In this way, by linking the server, terminal, and emotion engine, a system is realized that allows users to efficiently obtain election information and related products.

[1029] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1030] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1031] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1032] [Fourth Embodiment]

[1033] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1034] As shown in Figure 7, the 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.

[1035] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1036] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1037] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1038] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1039] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1040] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1041] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1042] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1043] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1044] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1045] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1046] The present invention is a system that includes information gathering means, information analysis means, database registration means, user interface means, candidate proposal means, candidate comparison means, and legislator activity tracking means. As an example of implementing this system, the following program configuration and specific processing steps will be described.

[1047] Information gathering and analysis

[1048] The server first periodically collects data from election management agencies, local government websites, council broadcast data, candidates' social media (e.g., Twitter, Facebook), blogs, election information sites, and general social media (e.g., YouTube, Instagram). This data collection uses scraping techniques and APIs.

[1049] The server applies natural language processing (NLP) algorithms to the collected data, extracting and classifying information such as candidates' names, policies, and statements from the text data. For example, it might extract "statements on environmental policy" and analyze the positive / negative sentiment associated with those statements.

[1050] Registration to the database

[1051] The server converts the analyzed information into a structured format (e.g., JSON, XML) and registers it in the database. The database stores indexed information on each candidate, ranging from basic information (name, age, political party affiliation, etc.) to policy-specific statements and activity history.

[1052] Providing a user interface

[1053] The device provides an intuitive user interface via a web browser or dedicated app when accessed by the user. Users can utilize search fields and filter options to select policies or constituencies of interest.

[1054] Comparison of candidates' proposals

[1055] When a user enters specific interests or policies, the device sends that information to a server, which then retrieves information on suitable candidates from a database. For example, a user interested in environmental issues or education would search for candidates with experience in those areas.

[1056] The server selects highly suitable candidates based on user input and sends a list of them to the terminal. The terminal displays a list of candidates' photos, summaries, and policy positions. Users can also select multiple candidates and compare their policies and track records.

[1057] Tracking parliamentary activities

[1058] After the election, the server continuously collects information on the activities of the elected representatives. For example, it updates the database with information such as parliamentary attendance rates, the number of bills introduced, and the content of their social media posts.

[1059] If users want to check the activities of elected representatives after their election, they can view the latest information through their devices. This allows users to continuously monitor the performance of the representatives they voted for.

[1060] Specific example

[1061] This example shows a user's search for candidates based on their particular interest in environmental policy. The user enters information emphasizing "environmental policy" and "childcare support," and the server searches its database for matching candidates. As a result, a list of candidates with a proven track record in environmental policy and a strong commitment to childcare support is displayed. Each candidate's past statements and specific policy achievements are also shown, allowing the user to compare and select the most suitable candidate.

[1062] Thus, this system not only allows voters to easily obtain information about candidates and support appropriate decision-making, but also enables them to follow the activities of their representatives even after the election. This promotes voter participation in politics and contributes to the healthy functioning of democracy.

[1063] The following describes the processing flow.

[1064] Step 1:

[1065] The server collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media, blogs, election information sites, and general social media. This collection uses web scraping techniques and APIs, and data is retrieved periodically according to a specified schedule.

[1066] Step 2:

[1067] The server processes the collected data using natural language processing (NLP) algorithms to extract and analyze necessary information from the text data (such as candidate names, policies, statements, and emotional tone). The analyzed data is then converted into a structured format (such as JSON or XML).

[1068] Step 3:

[1069] The server registers the analyzed and structured data into a database. The database stores and indexes basic information about each candidate, their statements on specific policies, past performance, and electoral district information.

[1070] Step 4:

[1071] The device provides a user interface via a web browser or dedicated app when accessed by the user. The user interface includes search fields and filter options based on interests, allowing users to select policies or constituencies of interest.

[1072] Step 5:

[1073] The user enters their interests, desired policies, specific electoral districts, and candidate names. The device then transmits this input data to the server in real time.

[1074] Step 6:

[1075] The server searches the database for suitable candidates based on the user's input. The search results list candidates ranked by their relevance to the entered interests and policies.

[1076] Step 7:

[1077] The server sends the search results to the terminal. This includes the candidate's basic information, policy list, past statements, and track record.

[1078] Step 8:

[1079] The device displays search results visually to the user. Candidates' photos, names, political affiliations, policies, and achievements are displayed in a list format that can be understood at a glance.

[1080] Step 9:

[1081] If a user wants to select multiple candidates from the displayed list and perform a detailed comparison, they can choose a comparison option.

[1082] Step 10:

[1083] The device displays a comparative view of the policies, achievements, and statements of multiple selected candidates. A visual comparison table is generated for each item, making it easy for users to understand the differences.

[1084] Step 11:

[1085] After the election, the server continuously collects information on the activities of the elected representatives. In addition to the information collection process described above, attendance rates in parliament, the number of bills introduced, and statements made on social media are also tracked.

[1086] Step 12:

[1087] If a user wants to check the activities of their elected officials after an election, the latest information will be displayed on their device. The data includes the latest reports on the officials' activities, records of their speeches in parliament, and recent social media posts.

[1088] Through these steps, voters can quickly and easily obtain information about candidates and make informed decisions. Furthermore, they can continue to follow the activities of their representatives after the election, contributing to the healthy functioning of democracy.

[1089] (Example 1)

[1090] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1091] Modern voters struggle to efficiently and accurately obtain the information necessary to choose the right candidate in an election. Relying on traditional media or specific sources often results in biased or insufficient information. Furthermore, there are limited means to continuously follow the activities of elected officials after the election. As a result, it is difficult for voters to actively participate in politics and support the healthy functioning of democracy.

[1092] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1093] In this invention, the server includes means for collecting information, means for analyzing information, means for registering in a database, means for a user interface, means for proposing candidates, means for comparing candidates, means for tracking the activities of legislators, means for searching for information from a database based on user input and selecting candidates with a high degree of suitability, means for collecting data using scraping techniques or APIs, means for analyzing text data by applying natural language processing algorithms, converting it into a structured format and registering it in a database, means for providing an intuitive user interface, and means for continuously collecting information on the activities of legislators after their election and updating the database. This makes it possible for voters to quickly and efficiently obtain detailed information on candidates and to continuously monitor the activities of legislators even after the election.

[1094] "Information gathering methods" refer to means of collecting data from public institution websites, parliamentary broadcast data, social media, and blogs.

[1095] "Information analysis means" refers to means for analyzing data collected using natural language processing algorithms, and for extracting and classifying textual information.

[1096] A "database registration method" is a means of converting analyzed information into a structured format and registering it in a database.

[1097] A "user interface means" is a means of providing an intuitive user interface that allows users to search for and compare information.

[1098] A "candidate suggestion method" is a means of searching for information from a database based on user input and selecting candidates with a high degree of suitability.

[1099] A "candidate comparison tool" is a means that allows users to compare information on multiple candidates in detail.

[1100] "Methods for tracking parliamentary activities" refer to means for continuously collecting information on the activities of elected members of parliament and updating the database accordingly.

[1101] "Web scraping" is a technique for automatically obtaining data from websites.

[1102] "API" stands for Application Programming Interface, and refers to an interface that enables data exchange between different software programs.

[1103] A "natural language processing algorithm" is an algorithm that analyzes text data and interprets, understands, and generates human language.

[1104] A "structured format" is a format that arranges data according to specific rules, making it easier to analyze mechanically. Examples include JSON and XML.

[1105] The present invention is a system that includes information gathering means, information analysis means, database registration means, user interface means, candidate proposal means, candidate comparison means, and legislator activity tracking means. As an example of implementing the present invention, the following program configuration and specific processing steps will be described.

[1106] Information gathering and analysis

[1107] The server first periodically collects data from government websites, parliamentary broadcast data, social media (e.g., Twitter, Facebook), blogs, election information sites, and general social media (e.g., YouTube, Instagram). This collection utilizes scraping techniques using Python's Scrapy library and Beautiful Soup, as well as APIs from each data source.

[1108] The server applies natural language processing (NLP) algorithms to the collected data. It analyzes text data using Python's NLTK library and spaCy. Specifically, it extracts and classifies information such as candidates' names, policies, and statements. For example, it extracts statements related to environmental policy and analyzes positive / negative sentiment based on specific keywords.

[1109] Registration to the database

[1110] The server converts the parsed information into a structured format (e.g., JSON, XML) and registers it in the database. MySQL or PostgreSQL are used as the database. Specifically, SQLAlchemy is used to connect to the database and insert data into the appropriate tables.

[1111] Providing a user interface

[1112] The device provides an intuitive user interface via a web browser or dedicated app when accessed by the user. React.js or Vue.js are used for the frontend, while Django or Flask are used for managing the API in the backend.

[1113] Users can enter their areas of interest, such as policies or constituencies, into the search field and use filter options to narrow down candidates based on specific criteria.

[1114] Comparison of candidates' proposals

[1115] When a user enters specific interests or policies, the terminal sends that information to the server. The communication protocol used here is HTTP.

[1116] The server queries the database based on user input to retrieve information on highly suitable candidates. It extracts data using SQL WHERE statements and filtering functions.

[1117] The device displays this information in a list format. Users can compare the policies and track records of multiple candidates in detail.

[1118] Tracking parliamentary activities

[1119] The server continuously collects information on the activities of elected representatives and updates the database. This includes parliamentary attendance rates, the number of bills introduced, and statements made on social media. This data is also collected via scraping and APIs.

[1120] If users want to check the activities of elected representatives after their election, they can view the latest information through their devices.

[1121] Specific example

[1122] If a user has a particular interest in environmental policy, this example illustrates how they can enter that information to search for candidates.

[1123] First, the user enters information emphasizing "environmental policy" and "childcare support" into the search field. Next, the device sends this information to the server. The server queries the database for candidates that match these criteria and retrieves the results. The device displays a list of candidates with a proven track record in environmental policy and a proactive stance on childcare support, along with detailed information on each candidate's past statements and specific policy achievements. Based on this information, the user can compare candidates and select the appropriate one.

[1124] The following is an example of a prompt statement:

[1125] "I am interested in environmental policy. Could you recommend any candidates with a proven track record in environmental policy?"

[1126] "Please search for candidates who are proactive in supporting childcare."

[1127] Thus, this system not only allows voters to easily obtain information about candidates and support appropriate decision-making, but also enables them to follow the activities of their representatives even after the election. This promotes voter participation in politics and contributes to the healthy functioning of democracy.

[1128] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1129] Step 1:

[1130] The server collects data from government websites, parliamentary broadcast data, social media, and blogs. Specifically, it performs web scraping using Python's Scrapy library and Beautiful Soup, and retrieves data using APIs. The input is the URL of each site or API, and the output is the collected raw data.

[1131] Step 2:

[1132] The server analyzes the collected raw data using natural language processing (NLP) algorithms. Using Python's NLTK library and spaCy, it extracts and classifies information such as candidate names, policies, and statements from the text data. The input is the raw data collected in step 1, and the output is structured data as a result of the analysis.

[1133] Step 3:

[1134] The server converts the parsed structured data into formats such as JSON or XML. It uses Python's json library or xml.etree.ElementTree. The input is the data parsed in step 2, and the output is the data converted into a structured format.

[1135] Step 4:

[1136] The server registers the converted structured data into a database. MySQL or PostgreSQL is used as the database, and SQLAlchemy is used for database operations. The input is the data converted in step 3, and the output is the data registered in the database.

[1137] Step 5:

[1138] The device provides an intuitive user interface via a web browser or dedicated app when accessed by the user. React.js or Vue.js are used for the frontend. Input is the user's access request, and output is the display of the interface.

[1139] Step 6:

[1140] Users enter the policies or electoral districts they are interested in into the search field. For example, they might enter keywords such as "environmental policy" or "childcare support." The input is the user's search query, and the output is the search request to the device.

[1141] Step 7:

[1142] The terminal sends the search query entered by the user to the server. The communication protocol used here is HTTP. The input is the search query entered by the user in step 6, and the output is the HTTP request to the server.

[1143] Step 8:

[1144] The server searches the database based on the received search query and retrieves information on matching candidates. It extracts data using SQL WHERE statements and filtering functions. The input is an HTTP request to the server, and the output is information on matching candidates.

[1145] Step 9:

[1146] The terminal displays candidate information received from the server to the user in a list format. React.js and Vue.js components are used here. The input is candidate information from the server, and the output is the display of candidate information to the user.

[1147] Step 10:

[1148] Users compare information on multiple candidates. Specifically, they use filter options and display formats to compare each candidate's policies and track record in detail. The input is the candidate information displayed on the terminal, and the output is the user's comparison results.

[1149] Step 11:

[1150] The server continuously collects information on the activities of elected representatives. It collects data from public institution websites and social media using scraping and APIs. The input is the URL of each site or API, and the output is the collected activity data of the representatives.

[1151] Step 12:

[1152] The server updates the database with the collected information on the activities of the legislators. Database operations are performed using libraries such as SQLAlchemy. The input is the data collected in step 11, and the output is the updated database.

[1153] Step 13:

[1154] Users can view the latest information on the activities of elected representatives through their devices if they wish to check the status of their activities after being elected. The input is the user's viewing request, and the output is the latest activity information of the representative displayed on the device.

[1155] (Application Example 1)

[1156] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1157] Modern political information is vast, making it difficult for voters to obtain the information they need quickly and accurately. Furthermore, many voters struggle to keep track of their politicians' activities after an election. Meanwhile, in today's world of widespread autonomous vehicles, there is a need for a system that allows for efficient acquisition of political information while on the go. This invention aims to solve this problem and promote political participation by providing voters with relevant information in a timely manner.

[1158] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1159] In this invention, the server includes means for collecting information, means for analyzing information, means for registering in a database, means for a user interface, means for proposing candidates, means for comparing candidates, means for tracking the activities of legislators, means for coordinating with an in-vehicle display unit, and means for displaying candidate information on the in-vehicle display unit based on the interests of general users. This allows voters to easily obtain information on policies and candidates of interest to them, even while on the move, and to make appropriate decisions.

[1160] "Information gathering methods" refer to means of regularly collecting data from election management agencies, local government websites, parliamentary proceedings data, candidates' social media, blogs, election information websites, and general social media.

[1161] "Information analysis means" refers to methods that apply natural language processing algorithms to collected data to extract and classify information such as candidates' names, policies, and statements from text data.

[1162] A "database registration method" is a means of converting the analyzed information into a structured format (e.g., JSON, XML) and registering it in a database.

[1163] A "user interface means" refers to a means by which users can access an intuitive user interface through a web browser or dedicated application, and utilize search fields and filter options to select policies or electoral districts of interest.

[1164] A "candidate suggestion method" is a system that, when a user inputs specific interests or policies, retrieves information on suitable candidates from a database based on that information and selects candidates with a high degree of suitability.

[1165] A "candidate comparison tool" is a means by which users can select multiple candidates and compare and display their respective policies and track records.

[1166] A "method for tracking the activities of elected officials" refers to a means of continuously collecting information on the activities of officials who have been elected after an election and updating a database accordingly.

[1167] An "in-vehicle display unit" is hardware that functions as a display device inside an autonomous vehicle.

[1168] "Means for displaying candidate information on in-vehicle display units based on the interests of general users" refers to means of displaying information related to policies and elections that users are interested in, in an appropriate format, on in-vehicle display units.

[1169] The present invention is a system that works in conjunction with an in-vehicle display unit to provide political information to users of autonomous vehicles. This system includes means for collecting information, means for analyzing information, means for registering in a database, means for a user interface, means for proposing candidates, means for comparing candidates, means for tracking the activities of legislators, means for working in conjunction with an in-vehicle display unit, and means for displaying candidate information on the in-vehicle display unit based on the interests of general users.

[1170] hardware

[1171] The system uses the following hardware:

[1172] Server: A central computer that performs tasks such as data collection, analysis, and registration in databases.

[1173] In-vehicle display unit: A device for displaying information inside an autonomous vehicle.

[1174] User terminals (smartphones and tablets): Used for auxiliary operations and checking information.

[1175] software

[1176] The system uses the following software:

[1177] Flask: A lightweight web application framework for Python

[1178] spaCy and NLTK: Libraries for implementing natural language processing algorithms

[1179] PostgreSQL: A relational database for storing analysis results.

[1180] Data processing and data calculation

[1181] 1. Information Gathering Methods: The server regularly collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media, blogs, election information websites, and general social media. Scraping techniques and public APIs are used for data collection.

[1182] 2. Information Analysis Method: The server applies natural language processing (NLP) algorithms to the collected data to extract and classify information such as candidates' names, policies, and statements from the text data. Libraries such as spaCy and NLTK are used. This analysis enables the analysis of statements and sentiments regarding specific policies.

[1183] 3. Database Registration Method: The parsed information is converted into a structured format and stored in a PostgreSQL database. This indexes the information, making it quickly searchable and retrievalable.

[1184] 4. User Interface: Search fields and filter options are provided via a web browser or dedicated app for users to select policies and electoral districts of interest. This allows users to intuitively manipulate information. The user interface is integrated with the in-car display unit.

[1185] 5. Candidate Suggestion Method: When a user enters specific interests or policies into the in-car display unit, that information is sent to a server, which retrieves information on suitable candidates from the database. This allows the system to suggest the most suitable candidates based on the user's interests.

[1186] 6. Candidate Comparison Method: Users can select multiple candidates and compare their policies and track records. This makes it easier for users to compare and consider their options.

[1187] 7. Tracking Legislative Activities: After the election, the server continuously collects information on the activities of elected legislators (parliamentary attendance rate, number of bills introduced, social media posts, etc.) and updates the database. Users can view the latest information through the in-car display unit.

[1188] Specific example

[1189] When a user enters "environmental policy" and "childcare support" as areas of interest into the in-car display unit, the server retrieves relevant candidate information from the database and displays a list of the candidates' past statements and specific policy achievements. This allows users to efficiently obtain information even while on the go.

[1190] Examples of prompts for a generative AI model:

[1191] "Please search for information on candidates related to environmental policy and childcare support. You can choose from the following candidates:"

[1192] Candidate A: Achievements in environmental policy and initiatives in childcare support.

[1193] Candidate B: Number of statements on environmental policy, specific achievements in childcare support.

[1194] Please compare and select the most suitable candidate.

[1195] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1196] Step 1:

[1197] The server collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media, blogs, election information sites, and general social media. It uses scraping techniques and public APIs for data collection, and continuously collects data according to a specific time schedule. Inputs are the URLs and API keys of each information source, and output is the collected source data.

[1198] Step 2:

[1199] The server applies natural language processing (NLP) algorithms to the collected data, extracting and classifying information such as candidates' names, policies, and statements from the text data. Specifically, it uses libraries such as spaCy and NLTK to parse the text and perform sentiment analysis. The input is the original text data collected in step 1, and the output is the analyzed structured data (in text format).

[1200] Step 3:

[1201] The server converts the parsed information into a structured format (e.g., JSON, XML) and registers it in a PostgreSQL database. This indexes the information so that it can be quickly searched and retrieved. The input is the structured data obtained in step 2, and the output is the information registered in the database.

[1202] Step 4:

[1203] The user operates the in-car display unit or smartphone and inputs information through search fields and filter options to select policies or electoral districts of interest. This generates a search query based on those interests. The input is the search criteria entered by the user into the device, and the output is the generated search query.

[1204] Step 5:

[1205] The server receives a search query submitted by the user and retrieves information on suitable candidates from the database. The input is the search query from step 4, and the output is a list of candidate information that matches the search query.

[1206] Step 6:

[1207] The server converts the acquired candidate information into a format suitable for the user interface and sends it to the in-car display unit or smartphone. The input is the candidate information list acquired in step 5, and the output is the information in the appropriate format to be displayed in the GUI (Graphical User Interface).

[1208] Step 7:

[1209] The user views a list of candidates' policies and achievements via an in-car display unit or smartphone, and selects and compares multiple candidates. The input is the candidate information displayed in step 6, and the output is the information of the compared candidates.

[1210] Step 8:

[1211] The server periodically collects information on the activities of elected legislators (such as parliamentary attendance rates, number of bills introduced, and social media posts) even after the election, and updates the database. Inputs are the URLs and API keys of each information source, and output is the updated information on the legislators' activities.

[1212] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1213] The present invention is a system that includes information gathering means, information analysis means, database registration means, user interface means, candidate suggestion means, candidate comparison means, legislator activity tracking means, and an emotion engine. As an example of implementing this system, the following program configuration and specific processing steps are described.

[1214] Information gathering and analysis

[1215] The server periodically collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media (e.g., Twitter, Facebook), blogs, election information sites, and general social media (e.g., YouTube, Instagram). This collection is performed according to a specified schedule using web scraping techniques and APIs.

[1216] The server analyzes the collected data using natural language processing (NLP) algorithms, extracting and classifying necessary information (candidate names, policies, statements, emotional tone, etc.) from the text data. The analyzed data is then converted into a structured format (e.g., JSON, XML).

[1217] Registration to the database

[1218] The server registers the analyzed and structured data into a database. This database stores indexed information such as each candidate's basic information, policy statements, past achievements, and electoral district information.

[1219] Providing a user interface

[1220] The device provides an intuitive user interface via a web browser or dedicated app upon user access. This interface includes search fields and filter options based on interests, allowing users to select policies or constituencies of interest.

[1221] Introducing an emotional engine

[1222] The device uses an emotion engine to recognize emotions from the text and voice input by the user. The emotion engine analyzes the user's input text and voice to determine the type of emotion (e.g., joy, anger, sadness, etc.). Based on this determination, the server selects the most suitable candidate information.

[1223] Comparison of candidates' proposals

[1224] Users input specific interests or policies, and their emotions at the time are also recorded. The device sends this input data to the server in real time.

[1225] The server searches the database for the most suitable candidates based on the user's input information and sentiments. The search results list candidates ranked by relevance, matching the entered interests, policies, and sentiments.

[1226] The server sends search results to the device, providing information on suitable candidates, taking sentiment into consideration. The device displays a list of candidates, including their photos, names, political affiliations, policies, and track records, in an easily understandable format.

[1227] Tracking parliamentary activities

[1228] After the election, the server continuously collects information on the activities of the elected representatives. In addition to the information gathering process before the election, it regularly updates the database with activity information such as parliamentary attendance rates, the number of bills introduced, and statements made on social media.

[1229] If a user wants to check the activities of their elected officials after an election, the latest information will be displayed on their device. This includes the latest reports on the officials' activities, transcripts of their speeches in parliament, and recent social media posts.

[1230] Specific example

[1231] For example, suppose a user has a particular interest in environmental policy and enters information on this topic. Simultaneously, if the server recognizes the user's emotions (e.g., "anxiety") from the input, it searches its database for candidates who can provide information that alleviates anxiety (e.g., candidates with policies that appeal to a sense of security). As a result, a list of candidates who are proactive in environmental policy but also propose policies that reduce the user's anxiety is displayed.

[1232] Thus, this system allows voters to quickly and easily obtain information about candidates and make appropriate decisions. Furthermore, by customizing information while considering the user's emotions, it provides a more personalized experience. In addition, it contributes to the healthy functioning of democracy by allowing voters to continue following the activities of their representatives even after the election.

[1233] The following describes the processing flow.

[1234] Step 1:

[1235] The server periodically collects data from election management agencies, local government websites, parliamentary broadcast data, candidate social media, blogs, election information sites, and general social media. This collection is performed using web scraping techniques and APIs, and is carried out according to a specified schedule.

[1236] Step 2:

[1237] The server processes the collected data using natural language processing (NLP) algorithms to extract and analyze necessary information from the text data (such as the candidate's name, policies, statements, and emotional tone). The analyzed data is then converted into a structured format (such as JSON or XML).

[1238] Step 3:

[1239] The server registers the analyzed and structured data into a database. This database stores indexed information such as each candidate's basic information, policy statements, past achievements, and electoral district information.

[1240] Step 4:

[1241] The device provides a user interface via a web browser or dedicated app upon user access. This user interface includes search fields and filter options based on interests, allowing users to select policies or constituencies of interest.

[1242] Step 5:

[1243] Users input their interests, desired policies, specific electoral districts, and candidate names. The device transmits this input data to the server in real time. Additionally, an emotion engine analyzes the user's emotions from their input data and voice, and transmits that information to the server as well.

[1244] Step 6:

[1245] The server searches the database for suitable candidates based on the user's input and analyzed sentiment data. The search results list candidates ranked by relevance, matching the user's interests, policies, and sentiments.

[1246] Step 7:

[1247] The server sends search results to the terminal. This includes basic information about the candidate, a list of policies, past statements, and achievements. It also provides information on suitable candidates, taking sentiment into consideration.

[1248] Step 8:

[1249] The device displays search results to the user visually. Candidates' photos, names, political affiliations, policies, and achievements are presented in a list format that allows for quick and easy viewing. Suggestions based on sentiment data are also highlighted.

[1250] Step 9:

[1251] If a user wants to select multiple candidates from the displayed list and perform a detailed comparison, they can choose a comparison option.

[1252] Step 10:

[1253] The device displays a comparative view of the policies, achievements, and statements of multiple selected candidates. A visual comparison table is generated for each item, making it easy for users to understand the differences.

[1254] Step 11:

[1255] After the election, the server continuously collects information on the activities of the elected representatives. In addition to the information gathering process before the election, it updates the database with information such as parliamentary attendance rates, the number of bills introduced, and social media posts.

[1256] Step 12:

[1257] If users want to check the activities of their elected officials after the election, the latest information will be displayed on their devices. This includes the latest reports on their activities, transcripts of their speeches in parliament, and their latest social media posts.

[1258] Through these steps, voters can quickly and easily obtain information about candidates and make informed decisions. By customizing the information to take into account emotions such as anxieties and interests, a more personalized experience is provided. Furthermore, voters can continue to follow the activities of their representatives after the election, contributing to the healthy functioning of democracy.

[1259] (Example 2)

[1260] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1261] Traditional election candidate information systems lacked sufficient information collection and analysis, making it difficult for voters to quickly and accurately obtain detailed information on specific policies or candidates of interest. Furthermore, they failed to consider voter sentiment, resulting in a lack of personalized user experiences. Additionally, post-election tracking of legislative activities was neglected, potentially hindering the healthy functioning of democracy.

[1262] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1263] In this invention, the server includes information gathering means, information analysis means, database registration means, user interface means, candidate suggestion means, candidate comparison means, legislator activity tracking means, emotion recognition means, data structuring means, and recommendation result display means. This enables voters to quickly obtain candidate information and provides personalized information that takes emotions into consideration. Furthermore, it enables continuous tracking of legislator activities after elections, supporting the healthy operation of democracy.

[1264] "Information gathering methods" refer to means of regularly collecting data from election management agencies, local government websites, parliamentary proceedings data, candidates' social media, blogs, election information websites, and general social media.

[1265] "Information analysis means" refers to means of analyzing data collected using natural language processing algorithms, extracting and classifying textual information, and further determining emotional tone from the collected data.

[1266] A "database registration method" is a means of registering analyzed and structured data in a database, indexing it, and storing it.

[1267] "User interface means" refers to means that provide an intuitive interface when a user accesses something, and includes search fields and filter options.

[1268] A "candidate suggestion method" is a means of searching for the most suitable candidates from a database based on user input information and sentiment, ranking them by suitability, and listing them.

[1269] A "candidate comparison method" is a means of displaying search results in a user-friendly format, allowing users to compare multiple candidates.

[1270] A "method for tracking parliamentary activities" refers to a means of continuously collecting information on the activities of elected members of parliament after an election and updating the database accordingly.

[1271] An "emotion recognition method" is a means of recognizing and analyzing emotions from text or voice input by the user.

[1272] A "data structuring method" is a means of converting parsed information into a structured format (e.g., JSON, XML).

[1273] A "recommendation result display method" is a means of displaying candidate information, based on the results of sentiment recognition, in a user-friendly format.

[1274] This invention provides a system that enables voters to quickly and accurately obtain information on election candidates and support more appropriate decision-making. This system includes information gathering means, information analysis means, database registration means, user interface means, candidate suggestion means, candidate comparison means, legislator activity tracking means, sentiment recognition means, data structuring means, and recommendation result display means. The specific implementation methods for each means are described below.

[1275] Information gathering and analysis

[1276] The server periodically collects data from election management agencies, local government websites, council broadcast data, candidate social media, blogs, election information sites, and general social media. This data collection is performed using Python's BeautifulSoup and various APIs. For example, it uses the Twitter API to collect tweets with the hashtag "election2023". Cron jobs are also used to perform periodic data collection.

[1277] The server analyzes the collected data using spaCy, a natural language processing (NLP) module. It extracts information such as the candidate's name, policies, statements, and emotional tone from the collected text data. Furthermore, NLTK's VADER is used for sentiment analysis to determine the emotional tone of the statements. For example, the statement "This bill is absolutely necessary" is analyzed as "positive."

[1278] Registration to the database

[1279] The server converts the analyzed data into JSON format and registers it in a MySQL database. The database stores indexed information such as each candidate's basic information, policy statements, past achievements, and electoral district information. For example, information such as "Name: Candidate A, Policy: Environmental protection, Emotion: Positive" is registered.

[1280] Providing a user interface

[1281] The device displays an interface developed with React when a user accesses the site via a web browser. Users can search for information on policies and candidates of interest through a search bar and filter options. For example, if a user selects "environmental policy" as their area of ​​interest, candidates related to that policy will be displayed.

[1282] Introducing an emotional engine

[1283] The device retrieves text entered by the user and sends it to an emotion engine (e.g., Google Cloud Natural Language API). For example, if the user enters "I'm worried about the future of the Earth," that text is sent to the emotion engine.

[1284] The server receives the analysis results from the emotion engine, and if the user's emotion is recognized as "anxiety," it uses that information to supplement the search criteria.

[1285] Comparison of candidates' proposals

[1286] The server searches a MySQL database for the most suitable candidates based on the user's interests and emotional information. For example, based on "environmental policy" and "anxiety," it identifies candidates with environmental policies that appeal to a sense of security.

[1287] The search results are sorted in a ranking format, with the datasets prepared in order of relevance. The server sends the search results to the device, which displays them in a user-friendly format. For example, a list of candidates' photos, names, political affiliations, policies, and achievements might be displayed.

[1288] Tracking parliamentary activities

[1289] After an election, the server periodically collects information on the legislators' activities, such as attendance rates, the number of bills introduced, and their social media activity. For example, it uses AWS Lambda to periodically call an API to retrieve the latest data on legislators and update the database.

[1290] If users want to check the activities of their elected officials after an election, they can view the latest information through their devices. For example, the content of recent statements made in parliament and attendance rates will be displayed.

[1291] Specific example

[1292] For example, if a user is particularly interested in environmental policy and enters the following prompt: "I'm worried about the future of the planet. Which candidate is proactive on environmental policy?", the emotion "anxiety" is recognized. As a result, the server searches for a candidate who instills a sense of reassurance, and candidate B is displayed at the top of the list. In this way, the user can quickly and easily obtain appropriate candidate information.

[1293] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1294] Step 1:

[1295] The server collects data from election management agency and local government websites, candidate social media, blogs, election information sites, and general social media. Specifically, it uses Python's BeautifulSoup and the APIs of each platform. For example, BeautifulSoup is used to extract candidate statements related to environmental policy from web pages. This collection is performed periodically by a Cron job. The input is website URLs or API endpoints, and the output is raw data.

[1296] Step 2:

[1297] The server analyzes the collected raw data using natural language processing (NLP) algorithms. Specifically, it uses spaCy to extract candidate names, policies, and statements from text data. It also uses NLTK's VADER for sentiment analysis to determine the emotional tone of a statement. For example, from the statement "I believe environmental protection is important," it extracts "environmental protection" as a policy and determines the emotional tone to be "positive." The input is raw data, and the output is analyzed data.

[1298] Step 3:

[1299] The server converts the parsed data into JSON format and registers it in the database. Specifically, it uses a MySQL database and applies indexes to enable fast searching. For example, it structures and stores data in the format "Name: Candidate A, Policy: Environmental Protection, Sentiment: Positive". The input is the parsed data, and the output is the data stored in the database.

[1300] Step 4:

[1301] The device provides an intuitive interface for users to access. Specifically, a web application developed with React is displayed, allowing users to search for information on policies and candidates of interest through a search bar and filter options. For example, typing "environmental policy" into the search bar will display a list of candidates related to that policy. The input is the user's search query, and the output is filtered candidate information.

[1302] Step 5:

[1303] The device sends the text entered by the user to the emotion engine. Specifically, it uses the Google Cloud Natural Language API or IBM Watson to analyze the text and determine the type of emotion. For example, if the user enters "I'm worried about the future of the Earth," that text is sent to the emotion engine, and the emotional tone is determined to be "anxious." The input is the user's text, and the output is the emotion analysis result.

[1304] Step 6:

[1305] The server searches for the most suitable candidate from the database based on the user's input information and sentiment information. Specifically, it identifies the most suitable candidate from the MySQL database based on "environmental policy" and "anxiety." For example, the database might detect that "Candidate B" proposes policies that provide a sense of security. The input is the user's interests and sentiment information, and the output is a list of candidates.

[1306] Step 7:

[1307] The server sends the search results to the terminal. The terminal displays the search results in a user-friendly format. Specifically, it lists the candidates' photos, names, political affiliations, policies they advocate, and past achievements. For example, "Candidate B, environmental protection, positive policies" might appear at the top. The input is a list of candidates, and the output is a list displayed on the user interface.

[1308] Step 8:

[1309] After the election, the server periodically collects information on the activities of legislators and updates the database. Specifically, it uses AWS Lambda and API Gateway to periodically retrieve information from various data sources, such as parliamentary attendance rates, the number of bills introduced, and social media posts. For example, "the number of bills recently introduced by legislator A" is collected. The input is periodic API calls, and the output is the latest legislator data.

[1310] Step 9:

[1311] If a user wants to check the activities of a legislator after an election, the latest information will be displayed on their device. Specifically, a dashboard developed with React will display the legislator's recent statements and activities. For example, "Legal Legislator B's latest statements" can be viewed. The input is the user's request, and the output is the latest legislator activity information.

[1312] (Application Example 2)

[1313] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1314] Current election information systems are cumbersome, requiring users to consult multiple sources to obtain policy and candidate information of interest. Furthermore, they struggle to attract interest in elections because they cannot suggest candidate information or election-related products tailored to users' emotions and interests.

[1315] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1316] In this invention, the server includes means for collecting information, means for analyzing information, means for registering in a database, means for a user interface, means for suggesting candidates, means for comparing candidates, means for tracking the activities of legislators, an emotion engine, means for providing election-related products and candidate information in a virtual store, and means for suggesting election-related products based on emotions and interests. This allows users to quickly obtain information on policies and candidates of interest through a single system, and to receive personalized information and product suggestions tailored to their emotions and interests.

[1317] "Information gathering methods" refer to means of collecting election-related information from election management agencies, local government websites, parliamentary proceedings data, candidates' social media, blogs, election information websites, and general social media.

[1318] "Information analysis means" refers to means of analyzing collected information using natural language processing algorithms to extract and classify necessary text information.

[1319] A "database registration method" is a means of registering analyzed information in a structured format into a database, indexing it, and storing it.

[1320] A "user interface means" is a means of providing an intuitive interface that users can operate, allowing them to input or search for information of interest.

[1321] A "candidate suggestion method" is a means of searching a database for and suggesting the most suitable candidate information based on the user's input information and emotions.

[1322] A "candidate comparison tool" is a means of displaying a list of information such as the policies advocated, track record, and sentiment analysis results of multiple candidates, allowing users to compare their information.

[1323] A "method for tracking parliamentary activities" refers to a system for continuously collecting information on the activities of elected members of parliament after the election and updating a database accordingly.

[1324] An "emotion engine" is an engine that analyzes emotions from user input text and voice, and provides data and product suggestions based on the results.

[1325] "Means of providing election-related goods and candidate information in a virtual store" refers to means of displaying election-related goods (e.g., posters, books, merchandise, etc.) and candidate information within a virtual store, making them easily accessible to users.

[1326] "A means of suggesting election-related products based on emotions and interests" refers to a means of suggesting the most suitable election-related products within a virtual store based on the user's emotions and interests.

[1327] A description of embodiments for carrying out the present invention will be provided.

[1328] System Configuration

[1329] First, as a means of gathering information, the server collects data from election management agencies, local government websites, council broadcast data, candidates' social media, blogs, election information sites, and general social media. This collection is performed periodically using web scraping techniques and APIs.

[1330] Hardware and software

[1331] Server: Performs information gathering, analysis, and database management.

[1332] Smartphones / Tablets: Provide a user interface

[1333] software:

[1334] Python: Implementation of an information analysis program

[1335] Requests: Data collection using HTTP communication

[1336] JSON: Data parsing and storage

[1337] NLP Library: Implementation of Natural Language Processing Algorithms

[1338] Emotion analysis engine: Extracts and analyzes user emotions.

[1339] Data analysis and registration

[1340] The server analyzes the collected data using natural language processing (NLP) algorithms, extracting necessary information from the text data (e.g., candidate names, policies, statements, emotional tone) and converting it into a structured format (e.g., JSON, XML). The analyzed data is indexed and stored in a database.

[1341] User Interface and Emotion Engine

[1342] When a user enters information via their device, the emotion engine analyzes that input to understand their emotions and provides relevant information based on their interests. Specifically, using a smartphone or tablet, users can input policies or topics they are interested in, and their emotions are automatically analyzed. This input data is transmitted to the server in real time.

[1343] Comparison with candidate proposals

[1344] The server searches its database for the most suitable candidates based on the user's input and sentiments. The search results list candidates ranked by relevance, matching the user's interests, policies, and sentiments. Information on the listed candidates is displayed through the user interface. Users can see a candidate's photo, name, political affiliation, policies, and track record at a glance.

[1345] Applications in virtual stores

[1346] The server also provides election-related merchandise and candidate information within the virtual store. For example, if a user is interested in "environmental policy," related posters, books, and merchandise will be displayed in the virtual store. Based on the user's emotions (e.g., "seeking reassurance"), the most suitable products will be suggested.

[1347] Examples of prompt statements

[1348] For example, if the user enters the following prompt:

[1349] I'm looking for election-related merchandise. I'm interested in "environmental policy," so please display posters and related products from candidates who advocate for environmentally conscious policies. Also, please provide candidate recommendations based on the user's sentiment (e.g., "I want to feel safe").

[1350] In this way, the system provides appropriate data based on users' interests and emotions, facilitating the acquisition of election-related information and suggesting election-related products and services. This enhances users' understanding of and interest in elections, supporting better decision-making.

[1351] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1352] Step 1:

[1353] The server regularly collects information from election management agencies, local government websites, parliamentary broadcast data, candidate social media accounts, blogs, election information sites, and general social media. It receives target website URLs and social media account information as input, retrieves data using web scraping techniques and APIs, and stores it as text data. The output is raw data.

[1354] Step 2:

[1355] The server analyzes the collected raw data using natural language processing (NLP) algorithms. Specifically, it extracts and classifies candidates' names, policies, statements, and emotional tone from the text data. Using the raw data obtained in step 1 as input, NLP extracts the necessary information as structured data. The output is structured data in JSON or XML format.

[1356] Step 3:

[1357] The server registers the analyzed and structured data into a database. This process indexes extracted candidate information, policies, sentiment tones, etc., making it efficiently searchable. It accepts structured data as input and stores it in the database. The output is an indexed database entry.

[1358] Step 4:

[1359] Users input policies and themes of interest via devices such as smartphones and tablets. The user's input data is analyzed by an emotion engine, which determines emotions from the input text and audio. The input consists of the user's text or voice, and the emotion analysis algorithm identifies the emotional tone. The output is the analyzed emotion data.

[1360] Step 5:

[1361] The server searches the database for the most suitable candidates based on the user's input and sentiment. It receives user interest and sentiment data as input and searches the database for matching candidates. These search results are ranked by relevance, and the output is a list of the most suitable candidates.

[1362] Step 6:

[1363] The search results are displayed immediately through the user interface (UI). The displayed information includes the candidate's photo, name, political affiliation, policies, and past achievements. Candidate information from search results is received as input and presented to the user in an intuitive UI format. The output is a visually displayed list of candidates.

[1364] Step 7:

[1365] In virtual stores offering election-related merchandise and candidate information, the system also suggests relevant products based on the user's emotions and interests. It uses data on the user's emotions and interests as input to find and suggest the most suitable products. The output displays a list of election-related products and reasons for recommendation.

[1366] In this way, by linking the server, terminal, and emotion engine, a system is realized that allows users to efficiently obtain election information and related products.

[1367] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1368] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1369] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1370] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1371] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1372] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1373] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1374] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1375] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1376] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1377] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1378] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1379] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1380] 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.

[1381] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1382] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1383] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1384] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1385] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1386] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1387] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1388] The following is further disclosed regarding the embodiments described above.

[1389] (Claim 1)

[1390] Information gathering methods,

[1391] Information analysis means,

[1392] Database registration method,

[1393] User interface means,

[1394] Candidate proposal methods,

[1395] Candidate comparison methods,

[1396] Methods for tracking parliamentary activities,

[1397] A system that includes this.

[1398] (Claim 2)

[1399] The information gathering means is the system according to claim 1, which gathers data from election management agencies, local government websites, parliamentary broadcast data, candidates' social media, blogs, election information websites, and general social media.

[1400] (Claim 3)

[1401] The information analysis means analyzes the collected data using a natural language processing algorithm and extracts and classifies text information according to claim 1.

[1402] (Claim 4)

[1403] The system according to claim 1, wherein the user interface means provides an interface in which the user can select interests, electoral districts, and specific candidates.

[1404] (Claim 5)

[1405] The candidate suggestion means is the system according to claim 1, which searches a database for the most suitable candidate based on the information entered by the user and presents it.

[1406] (Claim 6)

[1407] The candidate comparison means is the system according to claim 1, which compares and displays information such as policies and achievements for multiple candidates selected by the user.

[1408] (Claim 7)

[1409] The system according to claim 1, wherein the means for tracking the activities of legislators periodically collects information on the activities of legislators after the election, and enables users to check the status of legislators' activities even after the election.

[1410] "Example 1"

[1411] (Claim 1)

[1412] Information gathering methods,

[1413] Information analysis means,

[1414] Database registration method,

[1415] User interface means,

[1416] Candidate proposal methods,

[1417] Candidate comparison methods,

[1418] Methods for tracking parliamentary activities,

[1419] Based on user input, the database is searched for information.

[1420] Methods for selecting highly suitable candidates,

[1421] Methods for collecting data using scraping techniques and APIs,

[1422] By applying natural language processing algorithms to analyze text data,

[1423] Methods for converting to a structured format and registering it in a database,

[1424] A means of providing an intuitive user interface,

[1425] A means of continuously collecting information on the activities of elected legislators and updating the database,

[1426] A system that includes this.

[1427] (Claim 2)

[1428] The information gathering means is the system according to claim 1, which collects data from public institution websites, parliamentary broadcast data, social media, and blogs.

[1429] (Claim 3)

[1430] The information analysis means analyzes the collected data using a natural language processing algorithm and extracts and classifies text information according to claim 1.

[1431] "Application Example 1"

[1432] (Claim 1)

[1433] Information gathering methods,

[1434] Information analysis means,

[1435] Database registration method,

[1436] User interface means,

[1437] Candidate proposal methods,

[1438] Candidate comparison methods,

[1439] Methods for tracking parliamentary activities,

[1440] A means of linking with the in-car display unit,

[1441] A means for displaying candidate information on an in-vehicle display unit based on the interests of general users,

[1442] A system that includes this.

[1443] (Claim 2)

[1444] The information gathering means is the system according to claim 1, which gathers data from election management agencies, local government websites, parliamentary broadcast data, candidates' social media, blogs, election information websites, and general social media.

[1445] (Claim 3)

[1446] The information analysis means analyzes the collected data using a natural language processing algorithm and extracts and classifies text information according to claim 1.

[1447] "Example 2 of combining an emotion engine"

[1448] (Claim 1)

[1449] Information gathering methods,

[1450] Information analysis means,

[1451] Database registration method,

[1452] User interface means,

[1453] Candidate...

Claims

[Claim 1] Information gathering means that collect data from election management agencies, local government websites, parliamentary broadcast data, candidates' social media, blogs, election information websites, and general social media, An information analysis means that analyzes data collected using a natural language processing algorithm, and extracts and classifies textual information, Database registration method, A user interface means that provides an interface for users to select their interests, electoral districts, and specific candidates, A candidate suggestion method that searches a database for the most suitable candidate based on the information entered by the user and presents it, A candidate comparison tool that displays information such as policies and achievements for multiple candidates selected by the user, A means of tracking the activities of legislators that regularly collects information on their activities after elections, allowing users to check the status of legislators' activities even after the election. A system that includes this.

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