system

Abstract

Provide a system. [Solution] A means of obtaining data from the official website; A means for analyzing the acquired data and extracting text data; A means for analyzing the extracted text data using natural language processing and constructing legal data; a means for storing the analyzed legal data in a database; means for receiving a query from a user and retrieving relevant legal data; A means of simplifying and summarizing search results using natural language processing; A means for providing simplified and summarized search results to users; A system including:

Description

[Technical Field]

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

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

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

[0004] Current laws and regulations are difficult to understand, making it difficult for both the general public and experts to quickly understand them. Furthermore, laws and regulations are frequently revised and new enforcement regulations are added, requiring information updates. This requires a lot of time and effort to interpret the laws and regulations, and there is a risk of misunderstandings and misinterpretations. Furthermore, the need to check the relevant laws and regulations individually makes it even more difficult to gain an overall understanding. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system that automatically acquires data from official websites and analyzes, saves, searches, and simplifies legal data. Specifically, the system includes means for acquiring data from official websites, means for analyzing the acquired data to extract text data, means for analyzing the extracted text data using natural language processing to construct legal data, means for saving the analyzed legal data in a database, means for receiving queries from users and searching for related legal data, means for simplifying and summarizing search results using natural language processing, and means for providing simplified and summarized search results to users. This system enables quick and accurate understanding of laws and regulations, and automatically links related laws and regulations to each other, making it easier to grasp the overall legal information.

[0006] An "official website" is an official internet source published by a particular organization or institution.

[0007] A "means for acquiring data" is a method or device for automatically capturing data from a particular source.

[0008] The "means for analyzing and extracting text data" refers to a method or device for analyzing acquired data and extracting necessary text information.

[0009] "Natural language processing" is a technology that allows computers to understand, interpret, and manipulate human language.

[0010] The "means for constructing legal data" is a method or device for generating a database of legal data based on analyzed text data.

[0011] A "database" is a collection of information that is stored and managed in an organized format.

[0012] A "user query" is an inquiry that a user enters into a system to search for or retrieve information.

[0013] The "means for searching relevant legal data" refers to a method or device for searching corresponding legal data from within a database based on a user's query.

[0014] "Means for simplifying and summarizing search results" means a method or device for summarizing the retrieved legal data into an easy-to-understand format and extracting the main points.

[0015] "Means for providing to users" refers to a method or device that provides analyzed and summarized information so that users can access it. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] ---

[0038] This invention is a system that automatically acquires data from official websites and includes a series of processes to analyze, store, search, and simplify the data. The system's program processing and specific examples are explained below.

[0039] This system mainly includes a server, a terminal, and a user. The server is mainly responsible for acquiring, analyzing, storing, searching, and simplifying data, while the terminal provides an interface with the user.

[0040] Data acquisition and analysis

[0041] The server has a list of URLs for official government ministry websites. Based on this list, it periodically retrieves the HTML content of each website. The server sends an HTTP request and stores the returned HTML data in local storage.

[0042] The server then parses the saved HTML content and extracts the required text data. This step uses a scraping library (e.g. BeautifulSoup, Scrapy).

[0043] The server inputs the extracted text data into a natural language processing engine, which analyzes and identifies each item (title, article, clause number, link to related laws, etc.), thereby constructing legal data.

[0044] Data storage

[0045] The server stores the analyzed legal data in a database with a schema that comprehensively stores legal titles, articles, clause numbers, and links to related legal documents.

[0046] Processing user requests

[0047] The user enters information about a specific law into a search form in the browser on their device. For example, they enter the query "Article 32 of the Labor Standards Act."

[0048] The terminal sends a request containing the user's input to the server. The terminal makes this request using the HTTP protocol.

[0049] The server extracts the query from the received request and searches the database.

[0050] Simplifying and providing search results

[0051] The server retrieves legal data that matches the query from the database. For example, it retrieves the content of "Article 32 of the Labor Standards Act" from the database.

[0052] The server uses a natural language processing engine to simplify and summarize the retrieved legal data, which is summarized as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[0053] The server generates a response including the summarized legal data and sends it to the terminal.

[0054] The terminal displays the search results received from the server to the user, who can then check them through an interface that displays the summarized content in an intuitive and easy-to-understand manner.

[0055] Specific examples

[0056] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following specific processing will occur.

[0057] 1. The user searches for "Labor Standards Act Article 32" on their device and sends a request.

[0058] 2. The server receives the request and retrieves data related to "Article 32 of the Labor Standards Act" from the database.

[0059] 3. The server simplifies the data it has obtained and creates a summary version.

[0060] 4. The server sends a summary of the information back to the terminal, which displays it to the user.

[0061] This allows users to quickly and easily understand the content of the legislation.

[0062] The processing flow will be explained below.

[0063] ---

[0064] Step 1:

[0065] The server has a list of URLs for official government ministry websites, and periodically sends HTTP requests to each site on this list to retrieve its HTML content.

[0066] Step 2:

[0067] The server stores the acquired HTML content in local storage.

[0068] Step 3:

[0069] The server parses the saved HTML content and extracts the required text data using a scraping library (e.g. BeautifulSoup, Scrapy).

[0070] Step 4:

[0071] The server inputs the extracted text data into a natural language processing engine to analyze and identify legal data, such as titles, articles, clause numbers, and links to related laws and regulations.

[0072] Step 5:

[0073] The server stores the analyzed legal data in a database whose schema is designed to comprehensively store information such as titles, articles, clause numbers, and links to related laws and regulations.

[0074] Step 6:

[0075] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[0076] Step 7:

[0077] The terminal sends a request including the user's input to the server using the HTTP protocol.

[0078] Step 8:

[0079] The server extracts the query from the received request and searches the database.

[0080] Step 9:

[0081] The server retrieves legal data that matches the query from the database, for example, the content of "Article 32 of the Labor Standards Act."

[0082] Step 10:

[0083] The server uses a natural language processing engine to simplify and summarize the search results, for example, summarizing them as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[0084] Step 11:

[0085] The server generates a response including the summarized legal data and sends it to the terminal.

[0086] Step 12:

[0087] The terminal displays the search results received from the server to the user, displaying the summarized content in an intuitive and easy-to-understand format.

[0088] ---

[0089] In this way, a system has been created that allows users to quickly understand the content of laws and regulations through many processing steps.

[0090] Example 1

[0091] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0092] In today's information society, the amount of legal information available from official websites is enormous, making it difficult to quickly and accurately retrieve and analyze the information needed. This has led to a growing need for systems that efficiently retrieve and simplify the specific legal information users require. There is also a need for a method to effectively manage the retrieved and analyzed legal data and present it to users in an easy-to-understand manner.

[0093] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0094] In this invention, the server includes means for acquiring data from official websites, means for analyzing the acquired data and extracting text data, means for analyzing the extracted text data using natural language processing to construct legal data, means for storing the analyzed legal data in a storage device, means for receiving queries from users and searching for relevant legal data, means for simplifying and summarizing search results using a generative AI model, means for generating prompt sentences and inputting them into the generative AI model to generate summarized search results, and means for providing the simplified and summarized search results to users, thereby enabling users to quickly and accurately acquire necessary legal information and easily understand its content.

[0095] An "official website" is an official information site on the Internet operated by a government, public institution, company, etc.

[0096] "Data" means information that is collected, stored, and analyzed electronically.

[0097] "Means" are the specific methods or processes used to achieve a goal.

[0098] "Acquisition" refers to the act of gathering necessary data or information.

[0099] "Analysis" is the process of breaking down data and information into an understandable form and clarifying its meaning and structure.

[0100] "Text data" refers to information data expressed as characters.

[0101] "Natural language processing" is a technology that allows computers to understand, analyze, and generate human language.

[0102] "Legal data" means data that contains information about laws and regulations.

[0103] A "storage device" is an electronic device or system for long-term retention of data.

[0104] A "query" is a request or question used to retrieve information from a database or the like.

[0105] "Search" is the process of finding specific information.

[0106] A "generative AI model" is an artificial intelligence model that automatically generates new information or summaries from input data.

[0107] "Simplification" refers to putting complex information into a simple form to make it easier to understand.

[0108] A summary is a short summary of the main points or content.

[0109] A "prompt" is an instruction or question that is input into a generative AI model.

[0110] "User" means an individual or organization that uses this system.

[0111] This invention is a system that includes a series of processes to automatically acquire data from official websites and analyze, store, search, and simplify it. The system mainly includes a server, a terminal, and a user, and the specific role of each component is described in detail below.

[0112] Data acquisition and analysis

[0113] The server loads a configuration file called "urls.txt" that contains a list of URLs for official government ministry websites. This list contains the URLs for each site to be harvested. The server uses the Python requests library to send HTTP requests to these URLs and receives the returned HTML data. The received HTML data is saved in the "raw_data" folder in local storage.

[0114] The server sequentially reads the saved HTML files and uses the Python scraping library BeautifulSoup to extract the necessary text data, such as the title and article text of the law.

[0115] The server inputs the extracted text data into a natural language processing engine (e.g., Spacy or NLTK), analyzes each item (title, article, clause number, link to related legislation, etc.), and converts it into structured legal data.

[0116] Data storage

[0117] The server stores the analyzed legal data in an SQL database (e.g., MySQL (registered trademark), PostgreSQL). The database schema is designed to efficiently store the title, article, clause number, and link to related laws for each law.

[0118] Processing user requests

[0119] The user enters a query about a specific law into a search form on the device's browser, for example, "Article 32 of the Labor Standards Act," and clicks the search button.

[0120] The terminal sends an HTTP request containing the query entered by the user to the server, which sends data containing the query in JSON format to the server.

[0121] The server extracts the query from the received request, constructs an SQL query, and searches the database. For example, it executes the SQL statement "SELECT FROM Laws and Regulations WHERE title = 'Labor Standards Act Article 32'".

[0122] Simplifying and providing search results

[0123] The server retrieves legal data from the database that matches the query, including detailed information such as the full text of the legal document.

[0124] The server uses a generative AI model (e.g., GPT-4 (registered trademark)) to simplify and summarize the acquired legal data. For example, by inputting a prompt sentence such as "Please summarize Article 32 of the Labor Standards Act" into the generative AI model, summarized information is generated.

[0125] The server generates an HTTP response including the summarized legal data and transmits it to the terminal. For example, the server transmits the generated summary to the terminal and displays it to the user.

[0126] The terminal analyzes the response received from the server and converts it into HTML for display. A summary of the legal content is displayed through an interface that is intuitive and easy for the user to understand.

[0127] Specific examples

[0128] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following prompt sentence is input to the generative AI model:

[0129] Please summarize Article 32 of the Labor Standards Act.

[0130] This prompt allows the generative AI model to summarize the law, and the generated information is quickly provided to the user.

[0131] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0132] Step 1:

[0133] The server reads the configuration file "urls.txt," which contains a list of URLs for official government ministry websites. This allows it to obtain the URLs of the sites to be retrieved as input. The server then uses the Python requests library to send an HTTP request to each URL. This action returns HTML data for each website as output. This HTML data is saved in the "raw_data" folder in local storage.

[0134] Step 2:

[0135] The server sequentially reads the HTML files stored in the "raw_data" folder in local storage. It uses the BeautifulSoup library to extract the necessary text data (e.g., law titles, article text, etc.) from the HTML files. The input is the HTML files, and the output is the extracted text data.

[0136] Step 3:

[0137] The server inputs the extracted text data into a natural language processing engine (e.g., Spacy or NLTK). The natural language processing engine analyzes the text data and identifies each item (title, article, clause number, link to related laws, etc.). The output of this process is structured legal data. For example, it is categorized into "legal title," "article," "clause number," etc.

[0138] Step 4:

[0139] The server stores the parsed legal data in an SQL database (e.g., MySQL, PostgreSQL). The input is structured legal data, which is stored in the corresponding tables in the database. The output is the data held in the database.

[0140] Step 5:

[0141] The user enters a query about a specific law into a search form on the device's browser. For example, they enter "Labor Standards Act Article 32." The user's input query is the input for this step, and when the user presses the search button, the query is sent to the server. This query is used in the next step.

[0142] Step 6:

[0143] The terminal sends an HTTP request containing the query entered by the user to the server. The input is the user's query, and the output is the HTTP request sent to the server.

[0144] Step 7:

[0145] The server extracts the query from the received HTTP request, constructs an SQL query, and searches the database. For example, it executes the SQL statement "SELECT FROM Laws and Regulations WHERE title = 'Labor Standards Act Article 32'". The input is the user's query and the database, and the output is the relevant law data.

[0146] Step 8:

[0147] The server retrieves legal data from the database and generates a prompt sentence for the generative AI model (e.g., GPT-4) and inputs it. The prompt sentence is "Please summarize Article 32 of the Labor Standards Act." The input is the legal data and the prompt sentence, and the generative AI model outputs the summarized information.

[0148] Step 9:

[0149] The server generates an HTTP response containing summary information obtained from the generative AI model and sends it to the terminal. The input is the generated summary information, and the output is the HTTP response sent to the terminal.

[0150] Step 10:

[0151] The terminal analyzes the response received from the server and converts it into HTML for display. This allows the user to see a summary of the legal content in an intuitively easy-to-understand format. The input is the response from the server, and the output is the summary information displayed on the browser.

[0152] Through the above process, users can quickly and accurately obtain specific legal information and easily understand its contents.

[0153] (Application example 1)

[0154] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0155] In modern society, it is important to effectively acquire and analyze large amounts of information from official information disclosure sites and provide it in a form that users can easily understand. In particular, information such as laws, news, and articles is often difficult for users to understand, so accurate information collection and effective information provision are required. The objective of this invention is to provide a system that efficiently acquires, analyzes, stores, searches, and summarizes information from official information disclosure sites, and provides information to users quickly and intuitively.

[0156] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0157] In this invention, the server includes means for acquiring data from official information disclosure sites, means for analyzing the acquired data and extracting text data, means for analyzing the extracted text data using natural language processing and constructing information data, means for storing the analyzed information data in a database, means for receiving queries from users and searching for related information data, means for simplifying and summarizing search results using natural language processing, means for providing the simplified and summarized search results to the user, and means for using a generative AI model to generate summaries of the information data. This makes it possible to efficiently analyze the vast amount of information acquired from official information disclosure sites and quickly provide it to users in a form that is intuitively understandable.

[0158] An "official information disclosure site" is a website operated by a government agency or other official organization to disclose information.

[0159] "Means of retrieving data" refers to the methods and technologies used to retrieve HTML content from a specified URL.

[0160] "Means for extracting text data" refers to methods and technologies for analyzing and extracting the necessary text information from HTML content.

[0161] "Means of analysis using natural language processing" refers to methods and technologies that use natural language processing technology to analyze text data and identify and extract specific information items.

[0162] "Means for constructing information data" refers to methods and technologies for generating structured data for storage in a database based on analyzed text data.

[0163] "Means of storing in a database" refers to the methods and technologies for storing and managing analyzed information data using a database management system.

[0164] "Means for receiving and searching queries" refers to methods and techniques for searching a database for relevant information based on user input.

[0165] "Means for simplifying and summarizing search results" refers to methods and technologies for concisely summarizing searched information using natural language processing technology.

[0166] "Means of providing to users" refers to the methods and technologies used to display and provide search results through a user-friendly interface.

[0167] "Means of using a generative AI model" refers to methods or technologies that use a generative AI model to summarize or generate information.

[0168] This invention is a system that automatically acquires data from official information disclosure sites, analyzes and stores it, and simplifies and summarizes relevant information in response to user queries. Specifically, it is composed of a server, a terminal, and a user.

[0169] The server first retrieves data from the official information disclosure website using an HTTP request and saves the retrieved HTML content in local storage.The server then uses a scraping library such as BeautifulSoup or Scrapy to parse the HTML content and extract the required text data.

[0170] The extracted text data is then input into a natural language processing engine, which uses, for example, a transformers library to analyze and identify each piece of text data and construct information data, including the title, text, related links, etc.

[0171] The server also stores the analyzed information data in a database, which uses, for example, SQLite and has a schema for efficiently storing and managing the information data.

[0172] When a user inputs a query via a device (smartphone, smart glasses, head-mounted display, etc.), the device sends the query to the server. The server searches the database based on the received query to retrieve relevant information data. At this time, it uses a generative AI model to simplify and summarize the search results.

[0173] The generated summary is then sent to the terminal using the HTTP protocol and provided to the user, allowing the user to quickly and intuitively understand the official information.

[0174] Specific examples

[0175] For example, when a user inputs a request such as "I want to know about the latest labor law revisions" and clicks the search button, the following specific processing is carried out.

[0176] 1. The user searches for "latest labor law changes" on their device and submits a request.

[0177] 2. The server receives the request and retrieves the relevant data from the database.

[0178] 3. The server simplifies the data it receives and creates a summary version, using a generative AI model.

[0179] 4. The server sends a summary of the information back to the terminal, which displays it to the user.

[0180] Prompt Sentence Examples

[0181] Write a Python program to automatically simplify complex text in news articles and news articles. This program will include functions to retrieve HTML from a specified URL, extract the text from it, and generate and save a simple summary.

[0182] In this way, it is possible to efficiently analyze the vast amount of information obtained from official information disclosure sites and quickly provide it to users in a format that they can intuitively understand.

[0183] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0184] Step 1:

[0185] Data Acquisition: The server acquires data from the official information disclosure site. Specifically, the server sends an HTTP request and receives the returned HTML content. This acquires HTML data from the official information disclosure site. The input is a list of site URLs, and the output is HTML data.

[0186] Step 2:

[0187] Extracting text data: The server analyzes the acquired HTML data and extracts the necessary text data. Specifically, it extracts text from HTML using a scraping library such as BeautifulSoup or Scrapy. The input of this step is HTML data, and the output is the extracted text data.

[0188] Step 3:

[0189] Analysis by natural language processing: The server inputs the extracted text data into a natural language processing engine to construct information data. For example, it uses the transformers library to identify and extract titles and body text from the text. The input of this step is the extracted text data, and the output is the constructed information data.

[0190] Step 4:

[0191] Data storage: The server stores the analyzed information data in a database. Specifically, SQLite is used to efficiently store and manage the information data. The input of this step is the information data, and the output is the stored database entry.

[0192] Step 5:

[0193] Receiving a query: The user inputs and sends a query to search for specific information through the terminal. For example, they input a query such as "Information on the latest labor law revisions." The input of this step is the query entered by the user, and the output is an HTTP request to the server.

[0194] Step 6:

[0195] Database search: The server searches the database based on the received query to obtain relevant information data. The input is the user's query, and the output is the search result information data.

[0196] Step 7:

[0197] Summarization using a generative AI model: The server uses a generative AI model to simplify and summarize the information data in the search results. Specifically, it uses a generative AI model to shorten long text. The input for this step is the information data in the search results, and the output is the summarized information.

[0198] Step 8:

[0199] Providing summary results: The server sends the summarized information to the user's terminal, which then displays it to the user. The input is the summarized information data, and the output is the display on the user's terminal.

[0200] This allows users to quickly and intuitively understand official information.

[0201] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0202] ---

[0203] This invention is a system that automatically acquires data from official websites, analyzes, saves, searches, and simplifies it, and combines it with an emotion engine that recognizes the user's emotions. The system's program processing and specific examples are explained below.

[0204] This system mainly includes a server, a terminal, and a user. The server is mainly responsible for data acquisition, analysis, storage, retrieval, simplification, and emotion recognition, while the terminal provides an interface with the user.

[0205] Data acquisition and analysis

[0206] The server has a list of URLs for official government ministry websites, and periodically sends HTTP requests to each site on this list to retrieve its HTML content.

[0207] The server stores the acquired HTML content in local storage.

[0208] The server parses the saved HTML content and extracts the required text data. This step uses a scraping library (e.g. BeautifulSoup, Scrapy).

[0209] The server inputs the extracted text data into a natural language processing engine to analyze and identify legal data, such as titles, articles, clause numbers, and links to related laws and regulations.

[0210] Data storage

[0211] The server stores the analyzed legal data in a database whose schema is designed to comprehensively store information such as titles, articles, clause numbers, and links to related laws and regulations.

[0212] Processing user requests

[0213] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[0214] The terminal sends a request including the user's input to the server using the HTTP protocol.

[0215] The server extracts the query from the received request and searches the database.

[0216] Simplifying and providing search results

[0217] The server retrieves legal data that matches the query from the database, for example, the content of "Article 32 of the Labor Standards Act."

[0218] The server uses a natural language processing engine to simplify and summarize the search results, for example, summarizing them as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[0219] The server generates a response including the summarized legal data and sends it to the terminal.

[0220] The terminal displays the search results received from the server to the user, displaying the summarized content in an intuitive and easy-to-understand format.

[0221] emotion recognition

[0222] The server uses an emotion engine to recognize emotions from the user's input queries and operations.

[0223] Based on the user's emotions recognized by the emotion engine, the server takes measures such as making the summary of the legal text more concise and displaying additional diagrams and tables if the user is feeling stressed.

[0224] If the server recognizes that the user's sentiment is negative, it will provide additional support information, FAQs, and even relevant consultation service information to help the user understand relevant laws and regulations.

[0225] Specific examples

[0226] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following specific processing will occur.

[0227] 1. The user searches for "Labor Standards Act Article 32" on their device and sends a request.

[0228] 2. The server receives the request and retrieves data related to "Article 32 of the Labor Standards Act" from the database.

[0229] 3. The server simplifies the data it has obtained and creates a summary version.

[0230] 4. The server recognizes the user's emotions and adds supporting information for interpretation if it determines that the user is feeling anxious, for example.

[0231] 5. The server sends the summary and supporting information back to the terminal, which displays it to the user.

[0232] This allows users to quickly and easily understand the content of the legislation and receive additional support if needed.

[0233] ---

[0234] The processing flow will be explained below.

[0235] ---

[0236] Step 1:

[0237] The server has a list of URLs for official government ministry websites, and periodically sends HTTP requests to each site on this list to retrieve its HTML content.

[0238] Step 2:

[0239] The server stores the acquired HTML content in local storage.

[0240] Step 3:

[0241] The server parses the saved HTML content and extracts the required text data using a scraping library (e.g. BeautifulSoup, Scrapy).

[0242] Step 4:

[0243] The server passes the extracted text data to a natural language processing engine, which analyzes and identifies the legal data, specifically identifying the legal title, article, clause number, and links to related laws.

[0244] Step 5:

[0245] The server stores the analyzed legal data in a database that comprehensively stores legal titles, articles, clause numbers, links to related laws, and other information in an easily accessible format.

[0246] Step 6:

[0247] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[0248] Step 7:

[0249] The terminal sends an HTTP request including the user's input to the server.

[0250] Step 8:

[0251] The server extracts the query from the received request and searches the database, for example, for data related to "Article 32 of the Labor Standards Act."

[0252] Step 9:

[0253] The server retrieves legal data matching the query from the database, including specific legal provisions and related information.

[0254] Step 10:

[0255] The server uses a natural language processing engine to simplify and summarize the search results, for example, summarizing them as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[0256] Step 11:

[0257] The server uses an emotion engine to recognize emotions from the user's input queries and operations. The emotion engine analyzes the user's query content and operation history.

[0258] Step 12:

[0259] The server customizes the search results based on the user's emotions as recognized by the emotion engine, for example, making the search results more concise and adding diagrams and tables if the user is feeling stressed.

[0260] Step 13:

[0261] Based on emotion recognition, if a negative emotion is detected, the server will provide support information, FAQs, consultation service information, etc. to help users understand relevant laws and regulations.

[0262] Step 14:

[0263] The server generates a response including the simplified and summarized legal data and additional supporting information and transmits it to the terminal.

[0264] Step 15:

[0265] The terminal displays the response received from the server to the user, displaying summarized legal data, related information, and additional supporting information in an intuitive and easy-to-understand format.

[0266] ---

[0267] In this way, through many concrete steps, users can quickly and easily understand the content of the law and receive additional support if necessary.

[0268] Example 2

[0269] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0270] Conventional legal information search systems make it difficult for users to quickly and easily understand the content of laws and regulations and related information. In addition, they provide uniform information without considering the user's emotional state, which means that users who are particularly stressed cannot receive sufficient support.

[0271] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for acquiring data from an official website, a means for analyzing the acquired data and extracting text data, and a means for analyzing the extracted text data using natural language processing to construct legal data. This allows legal information desired by the user to be quickly and easily searched for and provided, and further enables flexible provision of information according to the user's emotional state.

[0272] ---

[0273] An "official website" is an information dissemination medium on the Internet operated by a government agency or official organization.

[0274] "Means of acquiring data" refers to the process or method of collecting data from external sources via the Internet.

[0275] "Means of analyzing and extracting text data" refers to a method of using a program to decipher and analyze the content of the acquired data and extract the necessary information.

[0276] "Natural language processing" refers to techniques and means by which computers understand, interpret, and generate human language.

[0277] "Legal data" refers to official documents such as laws, rules, regulations, and safety guidelines, as well as information about their contents.

[0278] A "database" is a system for efficiently storing, searching, and managing large amounts of data.

[0279] A "query" refers to a command or question that commands a database to search for or manipulate information.

[0280] Simplification and summarization techniques are techniques and methods for reducing information-rich data into shorter, more understandable forms.

[0281] An "emotion recognition engine" is a system that analyzes and determines a user's emotional state from their input and operation data.

[0282] "Means for adding or modifying information to be provided according to emotions" refers to a method for appropriately adjusting the information to be provided based on the user's emotional state and adding necessary support.

[0283] This invention is a system that automatically acquires data from official websites, analyzes, saves, searches, and simplifies it, and combines it with an emotion engine that recognizes user emotions. The system's program processing and specific examples are described below. This system mainly includes a server, a terminal, and a user. The server is primarily responsible for data acquisition, analysis, storage, search, simplification, and emotion recognition, while the terminal provides the interface with the user.

[0284] Data acquisition and analysis

[0285] The server maintains a list of official website URLs, and periodically sends HTTP requests to each site on this list to retrieve their HTML content.

[0286] The server stores the acquired HTML content in local storage.

[0287] The server parses the saved HTML content and extracts the necessary text data. This step uses a scraping library (e.g. BeautifulSoup, Scrapy).

[0288] The server inputs the extracted text data into a natural language processing engine (e.g., SpaCy or the BERT model) to analyze and identify the legal data. As a result of the analysis, the title, article, clause number, and links to related laws and regulations are identified.

[0289] Data storage

[0290] The server stores the analyzed legal data in a database (e.g., PostgreSQL). The database schema is designed to structure and store data with attributes such as title, article, clause number, and links to related laws and regulations.

[0291] Processing user requests

[0292] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[0293] The terminal sends a request including the user's input to the server using the HTTP protocol.

[0294] The server extracts the query from the received request and searches the database.

[0295] Simplifying and providing search results

[0296] The server retrieves legal data that matches the query from the database, for example, the content of "Article 32 of the Labor Standards Act."

[0297] The server uses a natural language processing engine to simplify and summarize the search results, for example, summarizing them as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[0298] The server generates a response including the summarized legal data and sends it to the terminal.

[0299] The terminal displays the search results received from the server to the user, displaying the summarized content in an intuitive and easy-to-understand format.

[0300] emotion recognition

[0301] The server uses an emotion recognition engine (e.g., Sentiment Analysis API) to recognize emotions from the user's input queries and actions.

[0302] Based on the user's emotions recognized by the emotion engine, the server takes measures such as making the summary of the legal text more concise and displaying additional diagrams and tables if the user is feeling stressed.

[0303] If the server recognizes the user's emotions as negative, it will provide additional support information and FAQs to help them understand the relevant laws and regulations, as well as information on relevant consultation centers.

[0304] Specific examples

[0305] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following specific processing will occur.

[0306] 1. The user searches for "Labor Standards Act Article 32" on their device and sends a request.

[0307] 2. The server receives the request and retrieves data related to "Article 32 of the Labor Standards Act" from the database.

[0308] 3. The server simplifies the data it has obtained and creates a summary version.

[0309] 4. The server recognizes the user's emotions and adds supporting information for interpretation if it determines that the user is feeling anxious, for example.

[0310] 5. The server sends the summary and supporting information back to the terminal, which displays it to the user.

[0311] Prompt Sentence Examples

[0312] Below is a concrete example of a prompt sentence to be entered into this system.

[0313] 1. "Please tell me about Article 32 of the Labor Standards Act."

[0314] 2. "Please provide information about the maximum working hours."

[0315] This allows users to quickly and easily understand the content of the legislation and provides additional support if needed.

[0316] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0317] ---

[0318] Step 1:

[0319] Data Acquisition

[0320] The server maintains a list of official website URLs, and periodically sends HTTP requests to each site on this list to retrieve their HTML content.

[0321] Input: URL list

[0322] Output: HTML content retrieved from each site

[0323] What happens: The server cycles through the list of URLs, sends an HTTP request to each URL, and collects the returned HTML content.

[0324] Step 2:

[0325] Saving HTML Content

[0326] The server saves the retrieved HTML content in local storage, where the destination is specified as a specific directory.

[0327] Input: HTML content

[0328] Output: HTML file saved in local storage

[0329] Specific operation: The server writes and saves the retrieved HTML content as a file on the local disk.

[0330] Step 3:

[0331] Parsing HTML content

[0332] The server uses the BeautifulSoup library to parse the stored HTML content and extract the required text data.

[0333] Input: Saved HTML file

[0334] Output: Extracted text data (title of law, article, clause number, link to related law, etc.)

[0335] What it does: The server uses BeautifulSoup to parse HTML content and extract legal information from specific tags and class names.

[0336] Step 4:

[0337] Input to a natural language processing engine

[0338] The server inputs the extracted text data into a natural language processing engine (e.g., SpaCy or the BERT model) to analyze and identify legal data.

[0339] Input: Extracted text data

[0340] Output: Analysis results (structure of laws and regulations, links to related laws, etc.)

[0341] Specific operation: The server supplies the text data to a natural language processing engine, which then uses the engine to identify the structure and attributes of the legislation.

[0342] Step 5:

[0343] Saving to a database

[0344] The server stores the parsed legal data in a database (e.g., PostgreSQL).

[0345] Input: Parsed legal data

[0346] Output: Legal data stored in a database

[0347] Specific operation: The server inserts and stores the analysis results into the appropriate tables and columns in the database.

[0348] Step 6:

[0349] User search input

[0350] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[0351] Input: User query (e.g., "Labor Standards Act Article 32")

[0352] Output: The search request submitted

[0353] Specific operation: The user enters a query into the input form on the browser and clicks the search button to submit the request.

[0354] Step 7:

[0355] Submitting a Request

[0356] The terminal sends a request including the user's input to the server using the HTTP protocol.

[0357] Input: User query

[0358] Output: Request sent to the server

[0359] Specific operation: The device sends the entered query to the server as an HTTP request.

[0360] Step 8:

[0361] Query extraction and database search

[0362] The server extracts the query from the received request and searches the database.

[0363] Input: The request sent

[0364] Output: Legal data retrieved from the database

[0365] What happens: The server parses the request, gets the query, and then executes the query against the database.

[0366] Step 9:

[0367] Data Acquisition and Summarization

[0368] The server retrieves legal data that matches the query from the database.

[0369] Input: The database query that was executed

[0370] Output: Retrieved legal data

[0371] Specific operation: The server retrieves the relevant legal data from the database and summarizes it using a natural language processing engine.

[0372] Step 10:

[0373] Generating and Sending a Response

[0374] The server generates the generated summary data as a response and transmits it to the terminal.

[0375] Input: Obtained legal data

[0376] Output: A response containing summarized statute data

[0377] Specific operation: The server generates summarized legal data as an HTTP response and sends it to the terminal.

[0378] Step 11:

[0379] Displaying search results

[0380] The terminal displays the summary data received from the server to the user.

[0381] Input: The response sent by the server

[0382] Output: Summary data displayed to the user

[0383] Specific operation: The device displays the received summary data in the browser in an appropriate format.

[0384] Step 12:

[0385] User Emotion Recognition

[0386] The server uses an emotion recognition engine to recognize emotions from the user's input query and operation data.

[0387] Input: User query and operation data

[0388] Output: User's emotional state

[0389] Specific operation: The server inputs the query and operation log into the emotion recognition engine and analyzes the user's emotional state.

[0390] Step 13:

[0391] Emotion-based responses

[0392] The server tailors the information it provides based on the user's emotional state, adding support information and relevant helpline information as needed.

[0393] Input: User's emotional state

[0394] Output: Adjusted information

[0395] Specific operation: The server takes into account the emotion recognition results and generates a response including additional support information and FAQs, which it then sends to the device.

[0396] The above is the specific processing flow of this system.

[0397] (Application example 2)

[0398] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0399] Conventional security information systems manually retrieve and analyze information from official websites, making it difficult to quickly obtain and provide the latest information to users. Furthermore, they do not provide appropriate information based on the user's emotions or psychological state, which can lead to anxiety and stress. To address this issue, there is a demand for a system that allows users to quickly obtain appropriate security information with peace of mind.

[0400] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0401] In this invention, the server includes means for acquiring data from official websites, means for analyzing the acquired data to extract text data, means for analyzing the extracted text data using natural language processing to construct legal data, and means for recognizing user emotions and providing related information, thereby enabling users to quickly acquire the latest and most relevant security information with peace of mind.

[0402] An "official website" is a website officially operated by a government agency, public organization, or other entity with the purpose of providing reliable information.

[0403] A "means for obtaining data" is a means that has the function of sending an HTTP request and receiving data such as HTML content from a specified URL.

[0404] "Means for analyzing data and extracting text data" refers to the use of scraping or text analysis techniques to filter unnecessary information from the acquired data and extract the necessary text data.

[0405] "Means of analyzing using natural language processing and constructing legal data" refers to a means of using a natural language processing engine to analyze the titles, articles, clause numbers, related links, etc. of legal data from text data to generate legal data.

[0406] "Means for recognizing a user's emotions and providing related information" refers to means that uses an emotion recognition engine to analyze emotions from the user's input and operations and provide content and support information that is adapted to those emotions.

[0407] "Means of storing in a database" refers to the use of technologies and tools to structure the analyzed legal data and store it in a database.

[0408] The "means for receiving a query and searching for relevant legal data" refers to a means for receiving a search request from a user, searching the database based on that request, and obtaining relevant legal data.

[0409] "Means for simplifying and summarizing using natural language processing" refers to a means of concisely summarizing acquired legal data using a natural language processing engine and providing it in summary form.

[0410] "Means for providing to the user" refers to means including interfaces and UI components for conveying search results and summary information to the user.

[0411] This invention is a system that automatically acquires data from official websites, analyzes, stores, and searches the data, and combines it with an emotion engine that recognizes user emotions. The system mainly includes a server, a terminal, and a user. The server is primarily responsible for data acquisition, analysis, storage, search, simplification, and emotion recognition, while the terminal provides the interface with the user.

[0412] Data acquisition and analysis

[0413] The server has a list of URLs for reliable official websites. Based on this list, it periodically sends HTTP requests to each site to obtain their HTML content. The obtained HTML content is saved in local storage, and the saved HTML content is parsed to extract the required text data. This step uses a scraping library (e.g., BeautifulSoup, Scrapy).

[0414] The text data is input into a natural language processing engine (e.g., TextBlob, spaCy) and constructed as legal data, which identifies titles, articles, clause numbers, links to related laws, etc.

[0415] Data storage

[0416] The server stores the analyzed legal data in a database whose schema is designed to comprehensively store information such as titles, articles, clause numbers, and links to related laws and regulations.

[0417] Processing user requests

[0418] A user uses a browser on their device to enter information about a specific law into a search form. For example, they might enter "Article 32 of the Labor Standards Act." The device then sends a request containing the user's input to the server using the HTTP protocol. The server then extracts the query from the received request and searches its database.

[0419] Simplifying and providing search results

[0420] The server retrieves legal data that matches the query from the database. For example, it retrieves the content of "Article 32 of the Labor Standards Act." The server then uses a natural language processing engine to simplify and summarize the search results. For example, it may summarize the results as "Article 32 of the Labor Standards Act includes a provision that working hours not exceed eight hours per day." The server then generates a response containing the summarized legal data and sends it to the terminal. The terminal then displays the search results received from the server to the user. The summarized content is displayed in an intuitive and easy-to-understand format.

[0421] emotion recognition

[0422] The server uses an emotion engine to recognize emotions from the user's input queries and operations. Based on the user's emotions recognized by the emotion engine (e.g., IBM Watson® Tone Analyzer, Microsoft® Azure® Emotion API), measures such as making the summary of the legal text more concise and displaying additional figures and tables may be taken if the user is feeling stressed. Furthermore, if the user's emotions are recognized as negative, additional support information, FAQs, and even information on relevant consultation centers may be provided to help the user understand the relevant laws and regulations.

[0423] Specific examples

[0424] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following specific process takes place: The user searches for "Article 32 of the Labor Standards Act" on their device and sends a request. The server receives the request and retrieves data related to "Article 32 of the Labor Standards Act" from a database. The server simplifies the retrieved data and creates a summary. The server recognizes the user's emotions, and if it determines that the user is feeling anxious, for example, it adds supporting information for interpretation. The server returns the summary and supporting information to the device, which displays it to the user.

[0425] This allows users to quickly and easily understand the content of the legislation and receive additional support if needed.

[0426] Prompt Sentence Examples

[0427] "Please briefly explain the latest security alerts. Use emotion recognition to provide a clear, concise explanation that will not alarm users and include any necessary countermeasures."

[0428] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0429] Step 1:

[0430] The server retrieves the data from the official website.

[0431] Input: Official website URL list.

[0432] How it works: The server periodically sends HTTP requests to retrieve HTML content.

[0433] Output: The retrieved HTML content.

[0434] Step 2:

[0435] The server analyzes the acquired data and extracts text data.

[0436] Input: The retrieved HTML content.

[0437] What it does: Use a scraping library such as BeautifulSoup to extract the required text data from the HTML content.

[0438] Output: The extracted text data.

[0439] Step 3:

[0440] The server analyzes the extracted text data using natural language processing and constructs legal data.

[0441] Input: Extracted text data.

[0442] How it works: It uses a natural language processing engine such as TextBlob or spaCy to parse the text data and construct the statute title, article, clause number, related links, etc.

[0443] Output: Constructed legal data.

[0444] Step 4:

[0445] The server stores the analyzed legal data in a database.

[0446] Input: Constructed legal data.

[0447] What it does: Executes SQL queries to a database and stores legal data.

[0448] Output: Legislation data stored in a database.

[0449] Step 5:

[0450] The user uses the device's browser to search for information about a particular law.

[0451] Input: User's search query (e.g., "Labor Standards Act Article 32").

[0452] What happens: A user fills out a search form and clicks the search button.

[0453] Output: A search request is sent to the server.

[0454] Step 6:

[0455] The server receives queries from users and retrieves relevant legal data.

[0456] Input: The user's search query.

[0457] How it works: Runs a search query against the database to retrieve relevant legal data.

[0458] Output: Retrieved legal data.

[0459] Step 7:

[0460] The server simplifies and summarizes the search results using natural language processing.

[0461] Input: Retrieved legal data.

[0462] How it works: Uses a natural language processing engine to concisely summarize legal data.

[0463] Output: Simplified and summarized statutory data.

[0464] Step 8:

[0465] The server recognizes emotions from the user's input queries and operations.

[0466] Input: User search queries and operation logs.

[0467] How it works: Analyzes the user's emotions using an emotion engine (e.g., IBM Watson Tone Analyzer).

[0468] Output: User's emotion evaluation result.

[0469] Step 9:

[0470] The server provides relevant information based on the user's emotions.

[0471] Input: User's emotion evaluation result.

[0472] What it does: Add support information, FAQs, and helpline information that adapts to the user's emotions.

[0473] Output: Response data including auxiliary information.

[0474] Step 10:

[0475] The server provides simplified and summarized search results to the user.

[0476] Input: Simplified and summarized statutory data, and response data including supporting information.

[0477] Operation: Generates response data and sends it to the device.

[0478] Output: Simplified and summarized statutory data and supporting information displayed to the user.

[0479] Step 11:

[0480] The terminal displays the search results received from the server to the user.

[0481] Input: Response data from the server.

[0482] What it does: Visually displays the data received by the device.

[0483] Output: Search results and supporting information displayed to the user.

[0484] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0485] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0486] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0487] [Second embodiment]

[0488] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0489] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0490] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0491] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0492] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0493] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0494] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0495] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0496] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0497] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0498] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0499] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0500] ---

[0501] This invention is a system that automatically acquires data from official websites and includes a series of processes to analyze, store, search, and simplify the data. The system's program processing and specific examples are explained below.

[0502] This system mainly includes a server, a terminal, and a user. The server is mainly responsible for acquiring, analyzing, storing, searching, and simplifying data, while the terminal provides an interface with the user.

[0503] Data acquisition and analysis

[0504] The server has a list of URLs for official government ministry websites. Based on this list, it periodically retrieves the HTML content of each website. The server sends an HTTP request and stores the returned HTML data in local storage.

[0505] The server then parses the saved HTML content and extracts the required text data. This step uses a scraping library (e.g. BeautifulSoup, Scrapy).

[0506] The server inputs the extracted text data into a natural language processing engine, which analyzes and identifies each item (title, article, clause number, link to related laws, etc.), thereby constructing legal data.

[0507] Data storage

[0508] The server stores the analyzed legal data in a database with a schema that comprehensively stores legal titles, articles, clause numbers, and links to related legal documents.

[0509] Processing user requests

[0510] The user enters information about a specific law into a search form in the browser on their device. For example, they enter the query "Article 32 of the Labor Standards Act."

[0511] The terminal sends a request containing the user's input to the server. The terminal makes this request using the HTTP protocol.

[0512] The server extracts the query from the received request and searches the database.

[0513] Simplifying and providing search results

[0514] The server retrieves legal data that matches the query from the database. For example, it retrieves the content of "Article 32 of the Labor Standards Act" from the database.

[0515] The server uses a natural language processing engine to simplify and summarize the retrieved legal data, which is summarized as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[0516] The server generates a response including the summarized legal data and sends it to the terminal.

[0517] The terminal displays the search results received from the server to the user, who can then check them through an interface that displays the summarized content in an intuitive and easy-to-understand manner.

[0518] Specific examples

[0519] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following specific processing will occur.

[0520] 1. The user searches for "Labor Standards Act Article 32" on their device and sends a request.

[0521] 2. The server receives the request and retrieves data related to "Article 32 of the Labor Standards Act" from the database.

[0522] 3. The server simplifies the data it has obtained and creates a summary version.

[0523] 4. The server sends a summary of the information back to the terminal, which displays it to the user.

[0524] This allows users to quickly and easily understand the content of the legislation.

[0525] The processing flow will be explained below.

[0526] ---

[0527] Step 1:

[0528] The server has a list of URLs for official government ministry websites, and periodically sends HTTP requests to each site on this list to retrieve its HTML content.

[0529] Step 2:

[0530] The server stores the acquired HTML content in local storage.

[0531] Step 3:

[0532] The server parses the saved HTML content and extracts the required text data using a scraping library (e.g. BeautifulSoup, Scrapy).

[0533] Step 4:

[0534] The server inputs the extracted text data into a natural language processing engine to analyze and identify legal data, such as titles, articles, clause numbers, and links to related laws and regulations.

[0535] Step 5:

[0536] The server stores the analyzed legal data in a database whose schema is designed to comprehensively store information such as titles, articles, clause numbers, and links to related laws and regulations.

[0537] Step 6:

[0538] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[0539] Step 7:

[0540] The terminal sends a request including the user's input to the server using the HTTP protocol.

[0541] Step 8:

[0542] The server extracts the query from the received request and searches the database.

[0543] Step 9:

[0544] The server retrieves legal data that matches the query from the database, for example, the content of "Article 32 of the Labor Standards Act."

[0545] Step 10:

[0546] The server uses a natural language processing engine to simplify and summarize the search results, for example, summarizing them as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[0547] Step 11:

[0548] The server generates a response including the summarized legal data and sends it to the terminal.

[0549] Step 12:

[0550] The terminal displays the search results received from the server to the user, displaying the summarized content in an intuitive and easy-to-understand format.

[0551] ---

[0552] In this way, a system has been created that allows users to quickly understand the content of laws and regulations through many processing steps.

[0553] Example 1

[0554] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0555] In today's information society, the amount of legal information available from official websites is enormous, making it difficult to quickly and accurately retrieve and analyze the information needed. This has led to a growing need for systems that efficiently retrieve and simplify the specific legal information users require. There is also a need for a method to effectively manage the retrieved and analyzed legal data and present it to users in an easy-to-understand manner.

[0556] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0557] In this invention, the server includes means for acquiring data from official websites, means for analyzing the acquired data and extracting text data, means for analyzing the extracted text data using natural language processing to construct legal data, means for storing the analyzed legal data in a storage device, means for receiving queries from users and searching for relevant legal data, means for simplifying and summarizing search results using a generative AI model, means for generating prompt sentences and inputting them into the generative AI model to generate summarized search results, and means for providing the simplified and summarized search results to users, thereby enabling users to quickly and accurately acquire necessary legal information and easily understand its content.

[0558] An "official website" is an official information site on the Internet operated by a government, public institution, company, etc.

[0559] "Data" means information that is collected, stored, and analyzed electronically.

[0560] "Means" are the specific methods or processes used to achieve a goal.

[0561] "Acquisition" refers to the act of gathering necessary data or information.

[0562] "Analysis" is the process of breaking down data and information into an understandable form and clarifying its meaning and structure.

[0563] "Text data" refers to information data expressed as characters.

[0564] "Natural language processing" is a technology that allows computers to understand, analyze, and generate human language.

[0565] "Legal data" means data that contains information about laws and regulations.

[0566] A "storage device" is an electronic device or system for long-term retention of data.

[0567] A "query" is a request or question used to retrieve information from a database or the like.

[0568] "Search" is the process of finding specific information.

[0569] A "generative AI model" is an artificial intelligence model that automatically generates new information or summaries from input data.

[0570] "Simplification" refers to putting complex information into a simple form to make it easier to understand.

[0571] A summary is a short summary of the main points or content.

[0572] A "prompt" is an instruction or question that is input into a generative AI model.

[0573] "User" means an individual or organization that uses this system.

[0574] This invention is a system that includes a series of processes to automatically acquire data from official websites and analyze, store, search, and simplify it. The system mainly includes a server, a terminal, and a user, and the specific role of each component is described in detail below.

[0575] Data acquisition and analysis

[0576] The server loads a configuration file called "urls.txt" that contains a list of URLs for official government ministry websites. This list contains the URLs for each site to be harvested. The server uses the Python requests library to send HTTP requests to these URLs and receives the returned HTML data. The received HTML data is saved in the "raw_data" folder in local storage.

[0577] The server sequentially reads the saved HTML files and uses the Python scraping library BeautifulSoup to extract the necessary text data, such as the title and article text of the law.

[0578] The server inputs the extracted text data into a natural language processing engine (e.g., Spacy or NLTK), analyzes each item (title, article, clause number, link to related legislation, etc.), and converts it into structured legal data.

[0579] Data storage

[0580] The server stores the analyzed legal data in an SQL database (e.g., MySQL, PostgreSQL). The database schema is designed to efficiently store the title, article, clause number, and link to related laws for each legal entity.

[0581] Processing user requests

[0582] The user enters a query about a specific law into a search form on the device's browser, for example, "Article 32 of the Labor Standards Act," and clicks the search button.

[0583] The terminal sends an HTTP request containing the query entered by the user to the server, which sends data containing the query in JSON format to the server.

[0584] The server extracts the query from the received request, constructs an SQL query, and searches the database. For example, it executes the SQL statement "SELECT FROM Laws and Regulations WHERE title = 'Labor Standards Act Article 32'".

[0585] Simplifying and providing search results

[0586] The server retrieves legal data from the database that matches the query, including detailed information such as the full text of the legal document.

[0587] The server uses a generative AI model (e.g., GPT-4) to simplify and summarize the acquired legal data. For example, by inputting a prompt such as "Please summarize Article 32 of the Labor Standards Act" into the generative AI model, summarized information is generated.

[0588] The server generates an HTTP response including the summarized legal data and transmits it to the terminal. For example, the server transmits the generated summary to the terminal and displays it to the user.

[0589] The terminal analyzes the response received from the server and converts it into HTML for display. A summary of the legal content is displayed through an interface that is intuitive and easy for the user to understand.

[0590] Specific examples

[0591] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following prompt sentence is input to the generative AI model:

[0592] Please summarize Article 32 of the Labor Standards Act.

[0593] This prompt allows the generative AI model to summarize the law, and the generated information is quickly provided to the user.

[0594] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0595] Step 1:

[0596] The server reads the configuration file "urls.txt," which contains a list of URLs for official government ministry websites. This allows it to obtain the URLs of the sites to be retrieved as input. The server then uses the Python requests library to send an HTTP request to each URL. This action returns HTML data for each website as output. This HTML data is saved in the "raw_data" folder in local storage.

[0597] Step 2:

[0598] The server sequentially reads the HTML files stored in the "raw_data" folder in local storage. It uses the BeautifulSoup library to extract the necessary text data (e.g., law titles, article text, etc.) from the HTML files. The input is the HTML files, and the output is the extracted text data.

[0599] Step 3:

[0600] The server inputs the extracted text data into a natural language processing engine (e.g., Spacy or NLTK). The natural language processing engine analyzes the text data and identifies each item (title, article, clause number, link to related laws, etc.). The output of this process is structured legal data. For example, it is categorized into "legal title," "article," "clause number," etc.

[0601] Step 4:

[0602] The server stores the parsed legal data in an SQL database (e.g., MySQL, PostgreSQL). The input is structured legal data, which is stored in the corresponding tables in the database. The output is the data held in the database.

[0603] Step 5:

[0604] The user enters a query about a specific law into a search form on the device's browser. For example, they enter "Labor Standards Act Article 32." The user's input query is the input for this step, and when the user presses the search button, the query is sent to the server. This query is used in the next step.

[0605] Step 6:

[0606] The terminal sends an HTTP request containing the query entered by the user to the server. The input is the user's query, and the output is the HTTP request sent to the server.

[0607] Step 7:

[0608] The server extracts the query from the received HTTP request, constructs an SQL query, and searches the database. For example, it executes the SQL statement "SELECT FROM Laws and Regulations WHERE title = 'Labor Standards Act Article 32'". The input is the user's query and the database, and the output is the relevant law data.

[0609] Step 8:

[0610] The server retrieves legal data from the database and generates a prompt sentence for the generative AI model (e.g., GPT-4) and inputs it. The prompt sentence is "Please summarize Article 32 of the Labor Standards Act." The input is the legal data and the prompt sentence, and the generative AI model outputs the summarized information.

[0611] Step 9:

[0612] The server generates an HTTP response containing summary information obtained from the generative AI model and sends it to the terminal. The input is the generated summary information, and the output is the HTTP response sent to the terminal.

[0613] Step 10:

[0614] The terminal analyzes the response received from the server and converts it into HTML for display. This allows the user to see a summary of the legal content in an intuitively easy-to-understand format. The input is the response from the server, and the output is the summary information displayed on the browser.

[0615] Through the above process, users can quickly and accurately obtain specific legal information and easily understand its contents.

[0616] (Application example 1)

[0617] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0618] In modern society, it is important to effectively acquire and analyze large amounts of information from official information disclosure sites and provide it in a form that users can easily understand. In particular, information such as laws, news, and articles is often difficult for users to understand, so accurate information collection and effective information provision are required. The objective of this invention is to provide a system that efficiently acquires, analyzes, stores, searches, and summarizes information from official information disclosure sites, and provides information to users quickly and intuitively.

[0619] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0620] In this invention, the server includes means for acquiring data from official information disclosure sites, means for analyzing the acquired data and extracting text data, means for analyzing the extracted text data using natural language processing and constructing information data, means for storing the analyzed information data in a database, means for receiving queries from users and searching for related information data, means for simplifying and summarizing search results using natural language processing, means for providing the simplified and summarized search results to the user, and means for using a generative AI model to generate summaries of the information data. This makes it possible to efficiently analyze the vast amount of information acquired from official information disclosure sites and quickly provide it to users in a form that is intuitively understandable.

[0621] An "official information disclosure site" is a website operated by a government agency or other official organization to disclose information.

[0622] "Means of retrieving data" refers to the methods and technologies used to retrieve HTML content from a specified URL.

[0623] "Means for extracting text data" refers to methods and technologies for analyzing and extracting the necessary text information from HTML content.

[0624] "Means of analysis using natural language processing" refers to methods and technologies that use natural language processing technology to analyze text data and identify and extract specific information items.

[0625] "Means for constructing information data" refers to methods and technologies for generating structured data for storage in a database based on analyzed text data.

[0626] "Means of storing in a database" refers to the methods and technologies for storing and managing analyzed information data using a database management system.

[0627] "Means for receiving and searching queries" refers to methods and techniques for searching a database for relevant information based on user input.

[0628] "Means for simplifying and summarizing search results" refers to methods and technologies for concisely summarizing searched information using natural language processing technology.

[0629] "Means of providing to users" refers to the methods and technologies used to display and provide search results through a user-friendly interface.

[0630] "Means of using a generative AI model" refers to methods or technologies that use a generative AI model to summarize or generate information.

[0631] This invention is a system that automatically acquires data from official information disclosure sites, analyzes and stores it, and simplifies and summarizes relevant information in response to user queries. Specifically, it is composed of a server, a terminal, and a user.

[0632] The server first retrieves data from the official information disclosure website using an HTTP request and saves the retrieved HTML content in local storage.The server then uses a scraping library such as BeautifulSoup or Scrapy to parse the HTML content and extract the required text data.

[0633] The extracted text data is then input into a natural language processing engine, which uses, for example, a transformers library to analyze and identify each piece of text data and construct information data, including the title, text, related links, etc.

[0634] The server also stores the analyzed information data in a database, which uses, for example, SQLite and has a schema for efficiently storing and managing the information data.

[0635] When a user inputs a query via a device (smartphone, smart glasses, head-mounted display, etc.), the device sends the query to the server. The server searches the database based on the received query to retrieve relevant information data. At this time, it uses a generative AI model to simplify and summarize the search results.

[0636] The generated summary is then sent to the terminal using the HTTP protocol and provided to the user, allowing the user to quickly and intuitively understand the official information.

[0637] Specific examples

[0638] For example, when a user inputs a request such as "I want to know about the latest labor law revisions" and clicks the search button, the following specific processing is carried out.

[0639] 1. The user searches for "latest labor law changes" on their device and submits a request.

[0640] 2. The server receives the request and retrieves the relevant data from the database.

[0641] 3. The server simplifies the data it receives and creates a summary version, using a generative AI model.

[0642] 4. The server sends a summary of the information back to the terminal, which displays it to the user.

[0643] Prompt Sentence Examples

[0644] Write a Python program to automatically simplify complex text in news articles and news articles. This program will include functions to retrieve HTML from a specified URL, extract the text from it, and generate and save a simple summary.

[0645] In this way, it is possible to efficiently analyze the vast amount of information obtained from official information disclosure sites and quickly provide it to users in a format that they can intuitively understand.

[0646] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0647] Step 1:

[0648] Data Acquisition: The server acquires data from the official information disclosure site. Specifically, the server sends an HTTP request and receives the returned HTML content. This acquires HTML data from the official information disclosure site. The input is a list of site URLs, and the output is HTML data.

[0649] Step 2:

[0650] Extracting text data: The server analyzes the acquired HTML data and extracts the necessary text data. Specifically, it extracts text from HTML using a scraping library such as BeautifulSoup or Scrapy. The input of this step is HTML data, and the output is the extracted text data.

[0651] Step 3:

[0652] Analysis by natural language processing: The server inputs the extracted text data into a natural language processing engine to construct information data. For example, it uses the transformers library to identify and extract titles and body text from the text. The input of this step is the extracted text data, and the output is the constructed information data.

[0653] Step 4:

[0654] Data storage: The server stores the analyzed information data in a database. Specifically, SQLite is used to efficiently store and manage the information data. The input of this step is the information data, and the output is the stored database entry.

[0655] Step 5:

[0656] Receiving a query: The user inputs and sends a query to search for specific information through the terminal. For example, they input a query such as "Information on the latest labor law revisions." The input of this step is the query entered by the user, and the output is an HTTP request to the server.

[0657] Step 6:

[0658] Database search: The server searches the database based on the received query to obtain relevant information data. The input is the user's query, and the output is the search result information data.

[0659] Step 7:

[0660] Summarization using a generative AI model: The server uses a generative AI model to simplify and summarize the information data in the search results. Specifically, it uses a generative AI model to shorten long text. The input for this step is the information data in the search results, and the output is the summarized information.

[0661] Step 8:

[0662] Providing summary results: The server sends the summarized information to the user's terminal, which then displays it to the user. The input is the summarized information data, and the output is the display on the user's terminal.

[0663] This allows users to quickly and intuitively understand official information.

[0664] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0665] ---

[0666] This invention is a system that automatically acquires data from official websites, analyzes, saves, searches, and simplifies it, and combines it with an emotion engine that recognizes the user's emotions. The system's program processing and specific examples are explained below.

[0667] This system mainly includes a server, a terminal, and a user. The server is mainly responsible for data acquisition, analysis, storage, retrieval, simplification, and emotion recognition, while the terminal provides an interface with the user.

[0668] Data acquisition and analysis

[0669] The server has a list of URLs for official government ministry websites, and periodically sends HTTP requests to each site on this list to retrieve its HTML content.

[0670] The server stores the acquired HTML content in local storage.

[0671] The server parses the saved HTML content and extracts the required text data. This step uses a scraping library (e.g. BeautifulSoup, Scrapy).

[0672] The server inputs the extracted text data into a natural language processing engine to analyze and identify legal data, such as titles, articles, clause numbers, and links to related laws and regulations.

[0673] Data storage

[0674] The server stores the analyzed legal data in a database whose schema is designed to comprehensively store information such as titles, articles, clause numbers, and links to related laws and regulations.

[0675] Processing user requests

[0676] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[0677] The terminal sends a request including the user's input to the server using the HTTP protocol.

[0678] The server extracts the query from the received request and searches the database.

[0679] Simplifying and providing search results

[0680] The server retrieves legal data that matches the query from the database, for example, the content of "Article 32 of the Labor Standards Act."

[0681] The server uses a natural language processing engine to simplify and summarize the search results, for example, summarizing them as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[0682] The server generates a response including the summarized legal data and sends it to the terminal.

[0683] The terminal displays the search results received from the server to the user, displaying the summarized content in an intuitive and easy-to-understand format.

[0684] emotion recognition

[0685] The server uses an emotion engine to recognize emotions from the user's input queries and operations.

[0686] Based on the user's emotions recognized by the emotion engine, the server takes measures such as making the summary of the legal text more concise and displaying additional diagrams and tables if the user is feeling stressed.

[0687] If the server recognizes that the user's sentiment is negative, it will provide additional support information, FAQs, and even relevant consultation service information to help the user understand relevant laws and regulations.

[0688] Specific examples

[0689] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following specific processing will occur.

[0690] 1. The user searches for "Labor Standards Act Article 32" on their device and sends a request.

[0691] 2. The server receives the request and retrieves data related to "Article 32 of the Labor Standards Act" from the database.

[0692] 3. The server simplifies the data it has obtained and creates a summary version.

[0693] 4. The server recognizes the user's emotions and adds supporting information for interpretation if it determines that the user is feeling anxious, for example.

[0694] 5. The server sends the summary and supporting information back to the terminal, which displays it to the user.

[0695] This allows users to quickly and easily understand the content of the legislation and receive additional support if needed.

[0696] ---

[0697] The processing flow will be explained below.

[0698] ---

[0699] Step 1:

[0700] The server has a list of URLs for official government ministry websites, and periodically sends HTTP requests to each site on this list to retrieve its HTML content.

[0701] Step 2:

[0702] The server stores the acquired HTML content in local storage.

[0703] Step 3:

[0704] The server parses the saved HTML content and extracts the required text data using a scraping library (e.g. BeautifulSoup, Scrapy).

[0705] Step 4:

[0706] The server passes the extracted text data to a natural language processing engine, which analyzes and identifies the legal data, specifically identifying the legal title, article, clause number, and links to related laws.

[0707] Step 5:

[0708] The server stores the analyzed legal data in a database that comprehensively stores legal titles, articles, clause numbers, links to related laws, and other information in an easily accessible format.

[0709] Step 6:

[0710] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[0711] Step 7:

[0712] The terminal sends an HTTP request including the user's input to the server.

[0713] Step 8:

[0714] The server extracts the query from the received request and searches the database, for example, for data related to "Article 32 of the Labor Standards Act."

[0715] Step 9:

[0716] The server retrieves legal data matching the query from the database, including specific legal provisions and related information.

[0717] Step 10:

[0718] The server uses a natural language processing engine to simplify and summarize the search results, for example, summarizing them as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[0719] Step 11:

[0720] The server uses an emotion engine to recognize emotions from the user's input queries and operations. The emotion engine analyzes the user's query content and operation history.

[0721] Step 12:

[0722] The server customizes the search results based on the user's emotions as recognized by the emotion engine, for example, making the search results more concise and adding diagrams and tables if the user is feeling stressed.

[0723] Step 13:

[0724] Based on emotion recognition, if a negative emotion is detected, the server will provide support information, FAQs, consultation service information, etc. to help users understand relevant laws and regulations.

[0725] Step 14:

[0726] The server generates a response including the simplified and summarized legal data and additional supporting information and transmits it to the terminal.

[0727] Step 15:

[0728] The terminal displays the response received from the server to the user, displaying summarized legal data, related information, and additional supporting information in an intuitive and easy-to-understand format.

[0729] ---

[0730] In this way, through many concrete steps, users can quickly and easily understand the content of the law and receive additional support if necessary.

[0731] Example 2

[0732] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0733] Conventional legal information search systems make it difficult for users to quickly and easily understand the content of laws and regulations and related information. In addition, they provide uniform information without considering the user's emotional state, which means that users who are particularly stressed cannot receive sufficient support.

[0734] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for acquiring data from an official website, a means for analyzing the acquired data and extracting text data, and a means for analyzing the extracted text data using natural language processing to construct legal data. This allows legal information desired by the user to be quickly and easily searched for and provided, and further enables flexible provision of information according to the user's emotional state.

[0735] ---

[0736] An "official website" is an information dissemination medium on the Internet operated by a government agency or official organization.

[0737] "Means of acquiring data" refers to the process or method of collecting data from external sources via the Internet.

[0738] "Means of analyzing and extracting text data" refers to a method of using a program to decipher and analyze the content of the acquired data and extract the necessary information.

[0739] "Natural language processing" refers to techniques and means by which computers understand, interpret, and generate human language.

[0740] "Legal data" refers to official documents such as laws, rules, regulations, and safety guidelines, as well as information about their contents.

[0741] A "database" is a system for efficiently storing, searching, and managing large amounts of data.

[0742] A "query" refers to a command or question that commands a database to search for or manipulate information.

[0743] Simplification and summarization techniques are techniques and methods for reducing information-rich data into shorter, more understandable forms.

[0744] An "emotion recognition engine" is a system that analyzes and determines a user's emotional state from their input and operation data.

[0745] "Means for adding or modifying information to be provided according to emotions" refers to a method for appropriately adjusting the information to be provided based on the user's emotional state and adding necessary support.

[0746] This invention is a system that automatically acquires data from official websites, analyzes, saves, searches, and simplifies it, and combines it with an emotion engine that recognizes user emotions. The system's program processing and specific examples are described below. This system mainly includes a server, a terminal, and a user. The server is primarily responsible for data acquisition, analysis, storage, search, simplification, and emotion recognition, while the terminal provides the interface with the user.

[0747] Data acquisition and analysis

[0748] The server maintains a list of official website URLs, and periodically sends HTTP requests to each site on this list to retrieve their HTML content.

[0749] The server stores the acquired HTML content in local storage.

[0750] The server parses the saved HTML content and extracts the necessary text data. This step uses a scraping library (e.g. BeautifulSoup, Scrapy).

[0751] The server inputs the extracted text data into a natural language processing engine (e.g., SpaCy or the BERT model) to analyze and identify the legal data. As a result of the analysis, the title, article, clause number, and links to related laws and regulations are identified.

[0752] Data storage

[0753] The server stores the analyzed legal data in a database (e.g., PostgreSQL). The database schema is designed to structure and store data with attributes such as title, article, clause number, and links to related laws and regulations.

[0754] Processing user requests

[0755] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[0756] The terminal sends a request including the user's input to the server using the HTTP protocol.

[0757] The server extracts the query from the received request and searches the database.

[0758] Simplifying and providing search results

[0759] The server retrieves legal data that matches the query from the database, for example, the content of "Article 32 of the Labor Standards Act."

[0760] The server uses a natural language processing engine to simplify and summarize the search results, for example, summarizing them as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[0761] The server generates a response including the summarized legal data and sends it to the terminal.

[0762] The terminal displays the search results received from the server to the user, displaying the summarized content in an intuitive and easy-to-understand format.

[0763] emotion recognition

[0764] The server uses an emotion recognition engine (e.g., Sentiment Analysis API) to recognize emotions from the user's input queries and actions.

[0765] Based on the user's emotions recognized by the emotion engine, the server takes measures such as making the summary of the legal text more concise and displaying additional diagrams and tables if the user is feeling stressed.

[0766] If the server recognizes the user's emotions as negative, it will provide additional support information and FAQs to help them understand the relevant laws and regulations, as well as information on relevant consultation centers.

[0767] Specific examples

[0768] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following specific processing will occur.

[0769] 1. The user searches for "Labor Standards Act Article 32" on their device and sends a request.

[0770] 2. The server receives the request and retrieves data related to "Article 32 of the Labor Standards Act" from the database.

[0771] 3. The server simplifies the data it has obtained and creates a summary version.

[0772] 4. The server recognizes the user's emotions and adds supporting information for interpretation if it determines that the user is feeling anxious, for example.

[0773] 5. The server sends the summary and supporting information back to the terminal, which displays it to the user.

[0774] Prompt Sentence Examples

[0775] Below is a concrete example of a prompt sentence to be entered into this system.

[0776] 1. "Please tell me about Article 32 of the Labor Standards Act."

[0777] 2. "Please provide information about the maximum working hours."

[0778] This allows users to quickly and easily understand the content of the legislation and provides additional support if needed.

[0779] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0780] ---

[0781] Step 1:

[0782] Data Acquisition

[0783] The server maintains a list of official website URLs, and periodically sends HTTP requests to each site on this list to retrieve their HTML content.

[0784] Input: URL list

[0785] Output: HTML content retrieved from each site

[0786] What happens: The server cycles through the list of URLs, sends an HTTP request to each URL, and collects the returned HTML content.

[0787] Step 2:

[0788] Saving HTML Content

[0789] The server saves the retrieved HTML content in local storage, where the destination is specified as a specific directory.

[0790] Input: HTML content

[0791] Output: HTML file saved in local storage

[0792] Specific operation: The server writes and saves the retrieved HTML content as a file on the local disk.

[0793] Step 3:

[0794] Parsing HTML content

[0795] The server uses the BeautifulSoup library to parse the stored HTML content and extract the required text data.

[0796] Input: Saved HTML file

[0797] Output: Extracted text data (title of law, article, clause number, link to related law, etc.)

[0798] What it does: The server uses BeautifulSoup to parse HTML content and extract legal information from specific tags and class names.

[0799] Step 4:

[0800] Input to a natural language processing engine

[0801] The server inputs the extracted text data into a natural language processing engine (e.g., SpaCy or the BERT model) to analyze and identify legal data.

[0802] Input: Extracted text data

[0803] Output: Analysis results (structure of laws and regulations, links to related laws, etc.)

[0804] Specific operation: The server supplies the text data to a natural language processing engine, which then uses the engine to identify the structure and attributes of the legislation.

[0805] Step 5:

[0806] Saving to a database

[0807] The server stores the parsed legal data in a database (e.g., PostgreSQL).

[0808] Input: Parsed legal data

[0809] Output: Legal data stored in a database

[0810] Specific operation: The server inserts and stores the analysis results into the appropriate tables and columns in the database.

[0811] Step 6:

[0812] User search input

[0813] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[0814] Input: User query (e.g., "Labor Standards Act Article 32")

[0815] Output: The search request submitted

[0816] Specific operation: The user enters a query into the input form on the browser and clicks the search button to submit the request.

[0817] Step 7:

[0818] Submitting a Request

[0819] The terminal sends a request including the user's input to the server using the HTTP protocol.

[0820] Input: User query

[0821] Output: Request sent to the server

[0822] Specific operation: The device sends the entered query to the server as an HTTP request.

[0823] Step 8:

[0824] Query extraction and database search

[0825] The server extracts the query from the received request and searches the database.

[0826] Input: The request sent

[0827] Output: Legal data retrieved from the database

[0828] What happens: The server parses the request, gets the query, and then executes the query against the database.

[0829] Step 9:

[0830] Data Acquisition and Summarization

[0831] The server retrieves legal data that matches the query from the database.

[0832] Input: The database query that was executed

[0833] Output: Retrieved legal data

[0834] Specific operation: The server retrieves the relevant legal data from the database and summarizes it using a natural language processing engine.

[0835] Step 10:

[0836] Generating and Sending a Response

[0837] The server generates the generated summary data as a response and transmits it to the terminal.

[0838] Input: Obtained legal data

[0839] Output: A response containing summarized statute data

[0840] Specific operation: The server generates summarized legal data as an HTTP response and sends it to the terminal.

[0841] Step 11:

[0842] Displaying search results

[0843] The terminal displays the summary data received from the server to the user.

[0844] Input: The response sent by the server

[0845] Output: Summary data displayed to the user

[0846] Specific operation: The device displays the received summary data in the browser in an appropriate format.

[0847] Step 12:

[0848] User Emotion Recognition

[0849] The server uses an emotion recognition engine to recognize emotions from the user's input query and operation data.

[0850] Input: User query and operation data

[0851] Output: User's emotional state

[0852] Specific operation: The server inputs the query and operation log into the emotion recognition engine and analyzes the user's emotional state.

[0853] Step 13:

[0854] Emotion-based responses

[0855] The server tailors the information it provides based on the user's emotional state, adding support information and relevant helpline information as needed.

[0856] Input: User's emotional state

[0857] Output: Adjusted information

[0858] Specific operation: The server takes into account the emotion recognition results and generates a response including additional support information and FAQs, which it then sends to the device.

[0859] The above is the specific processing flow of this system.

[0860] (Application example 2)

[0861] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0862] Conventional security information systems manually retrieve and analyze information from official websites, making it difficult to quickly obtain and provide the latest information to users. Furthermore, they do not provide appropriate information based on the user's emotions or psychological state, which can lead to anxiety and stress. To address this issue, there is a demand for a system that allows users to quickly obtain appropriate security information with peace of mind.

[0863] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0864] In this invention, the server includes means for acquiring data from official websites, means for analyzing the acquired data to extract text data, means for analyzing the extracted text data using natural language processing to construct legal data, and means for recognizing user emotions and providing related information, thereby enabling users to quickly acquire the latest and most relevant security information with peace of mind.

[0865] An "official website" is a website officially operated by a government agency, public organization, or other entity with the purpose of providing reliable information.

[0866] A "means for obtaining data" is a means that has the function of sending an HTTP request and receiving data such as HTML content from a specified URL.

[0867] "Means for analyzing data and extracting text data" refers to the use of scraping or text analysis techniques to filter unnecessary information from the acquired data and extract the necessary text data.

[0868] "Means of analyzing using natural language processing and constructing legal data" refers to a means of using a natural language processing engine to analyze the titles, articles, clause numbers, related links, etc. of legal data from text data to generate legal data.

[0869] "Means for recognizing a user's emotions and providing related information" refers to means that uses an emotion recognition engine to analyze emotions from the user's input and operations and provide content and support information that is adapted to those emotions.

[0870] "Means of storing in a database" refers to the use of technologies and tools to structure the analyzed legal data and store it in a database.

[0871] The "means for receiving a query and searching for relevant legal data" refers to a means for receiving a search request from a user, searching the database based on that request, and obtaining relevant legal data.

[0872] "Means for simplifying and summarizing using natural language processing" refers to a means of concisely summarizing acquired legal data using a natural language processing engine and providing it in summary form.

[0873] "Means for providing to the user" refers to means including interfaces and UI components for conveying search results and summary information to the user.

[0874] This invention is a system that automatically acquires data from official websites, analyzes, stores, and searches the data, and combines it with an emotion engine that recognizes user emotions. The system mainly includes a server, a terminal, and a user. The server is primarily responsible for data acquisition, analysis, storage, search, simplification, and emotion recognition, while the terminal provides the interface with the user.

[0875] Data acquisition and analysis

[0876] The server has a list of URLs for reliable official websites. Based on this list, it periodically sends HTTP requests to each site to obtain their HTML content. The obtained HTML content is saved in local storage, and the saved HTML content is parsed to extract the required text data. This step uses a scraping library (e.g., BeautifulSoup, Scrapy).

[0877] The text data is input into a natural language processing engine (e.g., TextBlob, spaCy) and constructed as legal data, which identifies titles, articles, clause numbers, links to related laws, etc.

[0878] Data storage

[0879] The server stores the analyzed legal data in a database whose schema is designed to comprehensively store information such as titles, articles, clause numbers, and links to related laws and regulations.

[0880] Processing user requests

[0881] A user uses a browser on their device to enter information about a specific law into a search form. For example, they might enter "Article 32 of the Labor Standards Act." The device then sends a request containing the user's input to the server using the HTTP protocol. The server then extracts the query from the received request and searches its database.

[0882] Simplifying and providing search results

[0883] The server retrieves legal data that matches the query from the database. For example, it retrieves the content of "Article 32 of the Labor Standards Act." The server then uses a natural language processing engine to simplify and summarize the search results. For example, it may summarize the results as "Article 32 of the Labor Standards Act includes a provision that working hours not exceed eight hours per day." The server then generates a response containing the summarized legal data and sends it to the terminal. The terminal then displays the search results received from the server to the user. The summarized content is displayed in an intuitive and easy-to-understand format.

[0884] emotion recognition

[0885] The server uses an emotion engine to recognize emotions from the user's input queries and operations. Based on the user's emotions recognized by the emotion engine (e.g., IBM Watson Tone Analyzer, Microsoft Azure Emotion API), measures such as making the summary of the legal text more concise and displaying additional figures and tables may be taken if the user is feeling stressed. Furthermore, if the user's emotions are recognized as negative, additional support information, FAQs, and even information on relevant consultation centers may be provided to help the user understand the relevant laws and regulations.

[0886] Specific examples

[0887] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following specific process takes place: The user searches for "Article 32 of the Labor Standards Act" on their device and sends a request. The server receives the request and retrieves data related to "Article 32 of the Labor Standards Act" from a database. The server simplifies the retrieved data and creates a summary. The server recognizes the user's emotions, and if it determines that the user is feeling anxious, for example, it adds supporting information for interpretation. The server returns the summary and supporting information to the device, which displays it to the user.

[0888] This allows users to quickly and easily understand the content of the legislation and receive additional support if needed.

[0889] Prompt Sentence Examples

[0890] "Please briefly explain the latest security alerts. Use emotion recognition to provide a clear, concise explanation that will not alarm users and include any necessary countermeasures."

[0891] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0892] Step 1:

[0893] The server retrieves the data from the official website.

[0894] Input: Official website URL list.

[0895] How it works: The server periodically sends HTTP requests to retrieve HTML content.

[0896] Output: The retrieved HTML content.

[0897] Step 2:

[0898] The server analyzes the acquired data and extracts text data.

[0899] Input: The retrieved HTML content.

[0900] What it does: Use a scraping library such as BeautifulSoup to extract the required text data from the HTML content.

[0901] Output: The extracted text data.

[0902] Step 3:

[0903] The server analyzes the extracted text data using natural language processing and constructs legal data.

[0904] Input: Extracted text data.

[0905] How it works: It uses a natural language processing engine such as TextBlob or spaCy to parse the text data and construct the statute title, article, clause number, related links, etc.

[0906] Output: Constructed legal data.

[0907] Step 4:

[0908] The server stores the analyzed legal data in a database.

[0909] Input: Constructed legal data.

[0910] What it does: Executes SQL queries to a database and stores legal data.

[0911] Output: Legislation data stored in a database.

[0912] Step 5:

[0913] The user uses the device's browser to search for information about a particular law.

[0914] Input: User's search query (e.g., "Labor Standards Act Article 32").

[0915] What happens: A user fills out a search form and clicks the search button.

[0916] Output: A search request is sent to the server.

[0917] Step 6:

[0918] The server receives queries from users and retrieves relevant legal data.

[0919] Input: The user's search query.

[0920] How it works: Runs a search query against the database to retrieve relevant legal data.

[0921] Output: Retrieved legal data.

[0922] Step 7:

[0923] The server simplifies and summarizes the search results using natural language processing.

[0924] Input: Retrieved legal data.

[0925] How it works: Uses a natural language processing engine to concisely summarize legal data.

[0926] Output: Simplified and summarized statutory data.

[0927] Step 8:

[0928] The server recognizes emotions from the user's input queries and operations.

[0929] Input: User search queries and operation logs.

[0930] How it works: Analyzes the user's emotions using an emotion engine (e.g., IBM Watson Tone Analyzer).

[0931] Output: User's emotion evaluation result.

[0932] Step 9:

[0933] The server provides relevant information based on the user's emotions.

[0934] Input: User's emotion evaluation result.

[0935] What it does: Add support information, FAQs, and helpline information that adapts to the user's emotions.

[0936] Output: Response data including auxiliary information.

[0937] Step 10:

[0938] The server provides simplified and summarized search results to the user.

[0939] Input: Simplified and summarized statutory data, and response data including supporting information.

[0940] Operation: Generates response data and sends it to the device.

[0941] Output: Simplified and summarized statutory data and supporting information displayed to the user.

[0942] Step 11:

[0943] The terminal displays the search results received from the server to the user.

[0944] Input: Response data from the server.

[0945] What it does: Visually displays the data received by the device.

[0946] Output: Search results and supporting information displayed to the user.

[0947] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0948] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0949] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0950] [Third embodiment]

[0951] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0952] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0953] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0954] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0955] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0956] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0957] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0958] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0959] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0960] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0961] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0962] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0963] ---

[0964] This invention is a system that automatically acquires data from official websites and includes a series of processes to analyze, store, search, and simplify the data. The system's program processing and specific examples are explained below.

[0965] This system mainly includes a server, a terminal, and a user. The server is mainly responsible for acquiring, analyzing, storing, searching, and simplifying data, while the terminal provides an interface with the user.

[0966] Data acquisition and analysis

[0967] The server has a list of URLs for official government ministry websites. Based on this list, it periodically retrieves the HTML content of each website. The server sends an HTTP request and stores the returned HTML data in local storage.

[0968] The server then parses the saved HTML content and extracts the required text data. This step uses a scraping library (e.g. BeautifulSoup, Scrapy).

[0969] The server inputs the extracted text data into a natural language processing engine, which analyzes and identifies each item (title, article, clause number, link to related laws, etc.), thereby constructing legal data.

[0970] Data storage

[0971] The server stores the analyzed legal data in a database with a schema that comprehensively stores legal titles, articles, clause numbers, and links to related legal documents.

[0972] Processing user requests

[0973] The user enters information about a specific law into a search form in the browser on their device. For example, they enter the query "Article 32 of the Labor Standards Act."

[0974] The terminal sends a request containing the user's input to the server. The terminal makes this request using the HTTP protocol.

[0975] The server extracts the query from the received request and searches the database.

[0976] Simplifying and providing search results

[0977] The server retrieves legal data that matches the query from the database. For example, it retrieves the content of "Article 32 of the Labor Standards Act" from the database.

[0978] The server uses a natural language processing engine to simplify and summarize the retrieved legal data, which is summarized as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[0979] The server generates a response including the summarized legal data and sends it to the terminal.

[0980] The terminal displays the search results received from the server to the user, who can then check them through an interface that displays the summarized content in an intuitive and easy-to-understand manner.

[0981] Specific examples

[0982] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following specific processing will occur.

[0983] 1. The user searches for "Labor Standards Act Article 32" on their device and sends a request.

[0984] 2. The server receives the request and retrieves data related to "Article 32 of the Labor Standards Act" from the database.

[0985] 3. The server simplifies the data it has obtained and creates a summary version.

[0986] 4. The server sends a summary of the information back to the terminal, which displays it to the user.

[0987] This allows users to quickly and easily understand the content of the legislation.

[0988] The processing flow will be explained below.

[0989] ---

[0990] Step 1:

[0991] The server has a list of URLs for official government ministry websites, and periodically sends HTTP requests to each site on this list to retrieve its HTML content.

[0992] Step 2:

[0993] The server stores the acquired HTML content in local storage.

[0994] Step 3:

[0995] The server parses the saved HTML content and extracts the required text data using a scraping library (e.g. BeautifulSoup, Scrapy).

[0996] Step 4:

[0997] The server inputs the extracted text data into a natural language processing engine to analyze and identify legal data, such as titles, articles, clause numbers, and links to related laws and regulations.

[0998] Step 5:

[0999] The server stores the analyzed legal data in a database whose schema is designed to comprehensively store information such as titles, articles, clause numbers, and links to related laws and regulations.

[1000] Step 6:

[1001] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[1002] Step 7:

[1003] The terminal sends a request including the user's input to the server using the HTTP protocol.

[1004] Step 8:

[1005] The server extracts the query from the received request and searches the database.

[1006] Step 9:

[1007] The server retrieves legal data that matches the query from the database, for example, the content of "Article 32 of the Labor Standards Act."

[1008] Step 10:

[1009] The server uses a natural language processing engine to simplify and summarize the search results, for example, summarizing them as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[1010] Step 11:

[1011] The server generates a response including the summarized legal data and sends it to the terminal.

[1012] Step 12:

[1013] The terminal displays the search results received from the server to the user, displaying the summarized content in an intuitive and easy-to-understand format.

[1014] ---

[1015] In this way, a system has been created that allows users to quickly understand the content of laws and regulations through many processing steps.

[1016] Example 1

[1017] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1018] In today's information society, the amount of legal information available from official websites is enormous, making it difficult to quickly and accurately retrieve and analyze the information needed. This has led to a growing need for systems that efficiently retrieve and simplify the specific legal information users require. There is also a need for a method to effectively manage the retrieved and analyzed legal data and present it to users in an easy-to-understand manner.

[1019] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1020] In this invention, the server includes means for acquiring data from official websites, means for analyzing the acquired data and extracting text data, means for analyzing the extracted text data using natural language processing to construct legal data, means for storing the analyzed legal data in a storage device, means for receiving queries from users and searching for relevant legal data, means for simplifying and summarizing search results using a generative AI model, means for generating prompt sentences and inputting them into the generative AI model to generate summarized search results, and means for providing the simplified and summarized search results to users, thereby enabling users to quickly and accurately acquire necessary legal information and easily understand its content.

[1021] An "official website" is an official information site on the Internet operated by a government, public institution, company, etc.

[1022] "Data" means information that is collected, stored, and analyzed electronically.

[1023] "Means" are the specific methods or processes used to achieve a goal.

[1024] "Acquisition" refers to the act of gathering necessary data or information.

[1025] "Analysis" is the process of breaking down data and information into an understandable form and clarifying its meaning and structure.

[1026] "Text data" refers to information data expressed as characters.

[1027] "Natural language processing" is a technology that allows computers to understand, analyze, and generate human language.

[1028] "Legal data" means data that contains information about laws and regulations.

[1029] A "storage device" is an electronic device or system for long-term retention of data.

[1030] A "query" is a request or question used to retrieve information from a database or the like.

[1031] "Search" is the process of finding specific information.

[1032] A "generative AI model" is an artificial intelligence model that automatically generates new information or summaries from input data.

[1033] "Simplification" refers to putting complex information into a simple form to make it easier to understand.

[1034] A summary is a short summary of the main points or content.

[1035] A "prompt" is an instruction or question that is input into a generative AI model.

[1036] "User" means an individual or organization that uses this system.

[1037] This invention is a system that includes a series of processes to automatically acquire data from official websites and analyze, store, search, and simplify it. The system mainly includes a server, a terminal, and a user, and the specific role of each component is described in detail below.

[1038] Data acquisition and analysis

[1039] The server loads a configuration file called "urls.txt" that contains a list of URLs for official government ministry websites. This list contains the URLs for each site to be harvested. The server uses the Python requests library to send HTTP requests to these URLs and receives the returned HTML data. The received HTML data is saved in the "raw_data" folder in local storage.

[1040] The server sequentially reads the saved HTML files and uses the Python scraping library BeautifulSoup to extract the necessary text data, such as the title and article text of the law.

[1041] The server inputs the extracted text data into a natural language processing engine (e.g., Spacy or NLTK), analyzes each item (title, article, clause number, link to related legislation, etc.), and converts it into structured legal data.

[1042] Data storage

[1043] The server stores the analyzed legal data in an SQL database (e.g., MySQL, PostgreSQL). The database schema is designed to efficiently store the title, article, clause number, and link to related laws for each legal entity.

[1044] Processing user requests

[1045] The user enters a query about a specific law into a search form on the device's browser, for example, "Article 32 of the Labor Standards Act," and clicks the search button.

[1046] The terminal sends an HTTP request containing the query entered by the user to the server, which sends data containing the query in JSON format to the server.

[1047] The server extracts the query from the received request, constructs an SQL query, and searches the database. For example, it executes the SQL statement "SELECT FROM Laws and Regulations WHERE title = 'Labor Standards Act Article 32'".

[1048] Simplifying and providing search results

[1049] The server retrieves legal data from the database that matches the query, including detailed information such as the full text of the legal document.

[1050] The server uses a generative AI model (e.g., GPT-4) to simplify and summarize the acquired legal data. For example, by inputting a prompt such as "Please summarize Article 32 of the Labor Standards Act" into the generative AI model, summarized information is generated.

[1051] The server generates an HTTP response including the summarized legal data and transmits it to the terminal. For example, the server transmits the generated summary to the terminal and displays it to the user.

[1052] The terminal analyzes the response received from the server and converts it into HTML for display. A summary of the legal content is displayed through an interface that is intuitive and easy for the user to understand.

[1053] Specific examples

[1054] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following prompt sentence is input to the generative AI model:

[1055] Please summarize Article 32 of the Labor Standards Act.

[1056] This prompt allows the generative AI model to summarize the law, and the generated information is quickly provided to the user.

[1057] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1058] Step 1:

[1059] The server reads the configuration file "urls.txt," which contains a list of URLs for official government ministry websites. This allows it to obtain the URLs of the sites to be retrieved as input. The server then uses the Python requests library to send an HTTP request to each URL. This action returns HTML data for each website as output. This HTML data is saved in the "raw_data" folder in local storage.

[1060] Step 2:

[1061] The server sequentially reads the HTML files stored in the "raw_data" folder in local storage. It uses the BeautifulSoup library to extract the necessary text data (e.g., law titles, article text, etc.) from the HTML files. The input is the HTML files, and the output is the extracted text data.

[1062] Step 3:

[1063] The server inputs the extracted text data into a natural language processing engine (e.g., Spacy or NLTK). The natural language processing engine analyzes the text data and identifies each item (title, article, clause number, link to related laws, etc.). The output of this process is structured legal data. For example, it is categorized into "legal title," "article," "clause number," etc.

[1064] Step 4:

[1065] The server stores the parsed legal data in an SQL database (e.g., MySQL, PostgreSQL). The input is structured legal data, which is stored in the corresponding tables in the database. The output is the data held in the database.

[1066] Step 5:

[1067] The user enters a query about a specific law into a search form on the device's browser. For example, they enter "Labor Standards Act Article 32." The user's input query is the input for this step, and when the user presses the search button, the query is sent to the server. This query is used in the next step.

[1068] Step 6:

[1069] The terminal sends an HTTP request containing the query entered by the user to the server. The input is the user's query, and the output is the HTTP request sent to the server.

[1070] Step 7:

[1071] The server extracts the query from the received HTTP request, constructs an SQL query, and searches the database. For example, it executes the SQL statement "SELECT FROM Laws and Regulations WHERE title = 'Labor Standards Act Article 32'". The input is the user's query and the database, and the output is the relevant law data.

[1072] Step 8:

[1073] The server retrieves legal data from the database and generates a prompt sentence for the generative AI model (e.g., GPT-4) and inputs it. The prompt sentence is "Please summarize Article 32 of the Labor Standards Act." The input is the legal data and the prompt sentence, and the generative AI model outputs the summarized information.

[1074] Step 9:

[1075] The server generates an HTTP response containing summary information obtained from the generative AI model and sends it to the terminal. The input is the generated summary information, and the output is the HTTP response sent to the terminal.

[1076] Step 10:

[1077] The terminal analyzes the response received from the server and converts it into HTML for display. This allows the user to see a summary of the legal content in an intuitively easy-to-understand format. The input is the response from the server, and the output is the summary information displayed on the browser.

[1078] Through the above process, users can quickly and accurately obtain specific legal information and easily understand its contents.

[1079] (Application example 1)

[1080] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1081] In modern society, it is important to effectively acquire and analyze large amounts of information from official information disclosure sites and provide it in a form that users can easily understand. In particular, information such as laws, news, and articles is often difficult for users to understand, so accurate information collection and effective information provision are required. The objective of this invention is to provide a system that efficiently acquires, analyzes, stores, searches, and summarizes information from official information disclosure sites, and provides information to users quickly and intuitively.

[1082] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1083] In this invention, the server includes means for acquiring data from official information disclosure sites, means for analyzing the acquired data and extracting text data, means for analyzing the extracted text data using natural language processing and constructing information data, means for storing the analyzed information data in a database, means for receiving queries from users and searching for related information data, means for simplifying and summarizing search results using natural language processing, means for providing the simplified and summarized search results to the user, and means for using a generative AI model to generate summaries of the information data. This makes it possible to efficiently analyze the vast amount of information acquired from official information disclosure sites and quickly provide it to users in a form that is intuitively understandable.

[1084] An "official information disclosure site" is a website operated by a government agency or other official organization to disclose information.

[1085] "Means of retrieving data" refers to the methods and technologies used to retrieve HTML content from a specified URL.

[1086] "Means for extracting text data" refers to methods and technologies for analyzing and extracting the necessary text information from HTML content.

[1087] "Means of analysis using natural language processing" refers to methods and technologies that use natural language processing technology to analyze text data and identify and extract specific information items.

[1088] "Means for constructing information data" refers to methods and technologies for generating structured data for storage in a database based on analyzed text data.

[1089] "Means of storing in a database" refers to the methods and technologies for storing and managing analyzed information data using a database management system.

[1090] "Means for receiving and searching queries" refers to methods and techniques for searching a database for relevant information based on user input.

[1091] "Means for simplifying and summarizing search results" refers to methods and technologies for concisely summarizing searched information using natural language processing technology.

[1092] "Means of providing to users" refers to the methods and technologies used to display and provide search results through a user-friendly interface.

[1093] "Means of using a generative AI model" refers to methods or technologies that use a generative AI model to summarize or generate information.

[1094] This invention is a system that automatically acquires data from official information disclosure sites, analyzes and stores it, and simplifies and summarizes relevant information in response to user queries. Specifically, it is composed of a server, a terminal, and a user.

[1095] The server first retrieves data from the official information disclosure website using an HTTP request and saves the retrieved HTML content in local storage.The server then uses a scraping library such as BeautifulSoup or Scrapy to parse the HTML content and extract the required text data.

[1096] The extracted text data is then input into a natural language processing engine, which uses, for example, a transformers library to analyze and identify each piece of text data and construct information data, including the title, text, related links, etc.

[1097] The server also stores the analyzed information data in a database, which uses, for example, SQLite and has a schema for efficiently storing and managing the information data.

[1098] When a user inputs a query via a device (smartphone, smart glasses, head-mounted display, etc.), the device sends the query to the server. The server searches the database based on the received query to retrieve relevant information data. At this time, it uses a generative AI model to simplify and summarize the search results.

[1099] The generated summary is then sent to the terminal using the HTTP protocol and provided to the user, allowing the user to quickly and intuitively understand the official information.

[1100] Specific examples

[1101] For example, when a user inputs a request such as "I want to know about the latest labor law revisions" and clicks the search button, the following specific processing is carried out.

[1102] 1. The user searches for "latest labor law changes" on their device and submits a request.

[1103] 2. The server receives the request and retrieves the relevant data from the database.

[1104] 3. The server simplifies the data it receives and creates a summary version, using a generative AI model.

[1105] 4. The server sends a summary of the information back to the terminal, which displays it to the user.

[1106] Prompt Sentence Examples

[1107] Write a Python program to automatically simplify complex text in news articles and news articles. This program will include functions to retrieve HTML from a specified URL, extract the text from it, and generate and save a simple summary.

[1108] In this way, it is possible to efficiently analyze the vast amount of information obtained from official information disclosure sites and quickly provide it to users in a format that they can intuitively understand.

[1109] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1110] Step 1:

[1111] Data Acquisition: The server acquires data from the official information disclosure site. Specifically, the server sends an HTTP request and receives the returned HTML content. This acquires HTML data from the official information disclosure site. The input is a list of site URLs, and the output is HTML data.

[1112] Step 2:

[1113] Extracting text data: The server analyzes the acquired HTML data and extracts the necessary text data. Specifically, it extracts text from HTML using a scraping library such as BeautifulSoup or Scrapy. The input of this step is HTML data, and the output is the extracted text data.

[1114] Step 3:

[1115] Analysis by natural language processing: The server inputs the extracted text data into a natural language processing engine to construct information data. For example, it uses the transformers library to identify and extract titles and body text from the text. The input of this step is the extracted text data, and the output is the constructed information data.

[1116] Step 4:

[1117] Data storage: The server stores the analyzed information data in a database. Specifically, SQLite is used to efficiently store and manage the information data. The input of this step is the information data, and the output is the stored database entry.

[1118] Step 5:

[1119] Receiving a query: The user inputs and sends a query to search for specific information through the terminal. For example, they input a query such as "Information on the latest labor law revisions." The input of this step is the query entered by the user, and the output is an HTTP request to the server.

[1120] Step 6:

[1121] Database search: The server searches the database based on the received query to obtain relevant information data. The input is the user's query, and the output is the search result information data.

[1122] Step 7:

[1123] Summarization using a generative AI model: The server uses a generative AI model to simplify and summarize the information data in the search results. Specifically, it uses a generative AI model to shorten long text. The input for this step is the information data in the search results, and the output is the summarized information.

[1124] Step 8:

[1125] Providing summary results: The server sends the summarized information to the user's terminal, which then displays it to the user. The input is the summarized information data, and the output is the display on the user's terminal.

[1126] This allows users to quickly and intuitively understand official information.

[1127] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1128] ---

[1129] This invention is a system that automatically acquires data from official websites, analyzes, saves, searches, and simplifies it, and combines it with an emotion engine that recognizes the user's emotions. The system's program processing and specific examples are explained below.

[1130] This system mainly includes a server, a terminal, and a user. The server is mainly responsible for data acquisition, analysis, storage, retrieval, simplification, and emotion recognition, while the terminal provides an interface with the user.

[1131] Data acquisition and analysis

[1132] The server has a list of URLs for official government ministry websites, and periodically sends HTTP requests to each site on this list to retrieve its HTML content.

[1133] The server stores the acquired HTML content in local storage.

[1134] The server parses the saved HTML content and extracts the required text data. This step uses a scraping library (e.g. BeautifulSoup, Scrapy).

[1135] The server inputs the extracted text data into a natural language processing engine to analyze and identify legal data, such as titles, articles, clause numbers, and links to related laws and regulations.

[1136] Data storage

[1137] The server stores the analyzed legal data in a database whose schema is designed to comprehensively store information such as titles, articles, clause numbers, and links to related laws and regulations.

[1138] Processing user requests

[1139] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[1140] The terminal sends a request including the user's input to the server using the HTTP protocol.

[1141] The server extracts the query from the received request and searches the database.

[1142] Simplifying and providing search results

[1143] The server retrieves legal data that matches the query from the database, for example, the content of "Article 32 of the Labor Standards Act."

[1144] The server uses a natural language processing engine to simplify and summarize the search results, for example, summarizing them as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[1145] The server generates a response including the summarized legal data and sends it to the terminal.

[1146] The terminal displays the search results received from the server to the user, displaying the summarized content in an intuitive and easy-to-understand format.

[1147] emotion recognition

[1148] The server uses an emotion engine to recognize emotions from the user's input queries and operations.

[1149] Based on the user's emotions recognized by the emotion engine, the server takes measures such as making the summary of the legal text more concise and displaying additional diagrams and tables if the user is feeling stressed.

[1150] If the server recognizes that the user's sentiment is negative, it will provide additional support information, FAQs, and even relevant consultation service information to help the user understand relevant laws and regulations.

[1151] Specific examples

[1152] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following specific processing will occur.

[1153] 1. The user searches for "Labor Standards Act Article 32" on their device and sends a request.

[1154] 2. The server receives the request and retrieves data related to "Article 32 of the Labor Standards Act" from the database.

[1155] 3. The server simplifies the data it has obtained and creates a summary version.

[1156] 4. The server recognizes the user's emotions and adds supporting information for interpretation if it determines that the user is feeling anxious, for example.

[1157] 5. The server sends the summary and supporting information back to the terminal, which displays it to the user.

[1158] This allows users to quickly and easily understand the content of the legislation and receive additional support if needed.

[1159] ---

[1160] The processing flow will be explained below.

[1161] ---

[1162] Step 1:

[1163] The server has a list of URLs for official government ministry websites, and periodically sends HTTP requests to each site on this list to retrieve its HTML content.

[1164] Step 2:

[1165] The server stores the acquired HTML content in local storage.

[1166] Step 3:

[1167] The server parses the saved HTML content and extracts the required text data using a scraping library (e.g. BeautifulSoup, Scrapy).

[1168] Step 4:

[1169] The server passes the extracted text data to a natural language processing engine, which analyzes and identifies the legal data, specifically identifying the legal title, article, clause number, and links to related laws.

[1170] Step 5:

[1171] The server stores the analyzed legal data in a database that comprehensively stores legal titles, articles, clause numbers, links to related laws, and other information in an easily accessible format.

[1172] Step 6:

[1173] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[1174] Step 7:

[1175] The terminal sends an HTTP request including the user's input to the server.

[1176] Step 8:

[1177] The server extracts the query from the received request and searches the database, for example, for data related to "Article 32 of the Labor Standards Act."

[1178] Step 9:

[1179] The server retrieves legal data matching the query from the database, including specific legal provisions and related information.

[1180] Step 10:

[1181] The server uses a natural language processing engine to simplify and summarize the search results, for example, summarizing them as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[1182] Step 11:

[1183] The server uses an emotion engine to recognize emotions from the user's input queries and operations. The emotion engine analyzes the user's query content and operation history.

[1184] Step 12:

[1185] The server customizes the search results based on the user's emotions as recognized by the emotion engine, for example, making the search results more concise and adding diagrams and tables if the user is feeling stressed.

[1186] Step 13:

[1187] Based on emotion recognition, if a negative emotion is detected, the server will provide support information, FAQs, consultation service information, etc. to help users understand relevant laws and regulations.

[1188] Step 14:

[1189] The server generates a response including the simplified and summarized legal data and additional supporting information and transmits it to the terminal.

[1190] Step 15:

[1191] The terminal displays the response received from the server to the user, displaying summarized legal data, related information, and additional supporting information in an intuitive and easy-to-understand format.

[1192] ---

[1193] In this way, through many concrete steps, users can quickly and easily understand the content of the law and receive additional support if necessary.

[1194] Example 2

[1195] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1196] Conventional legal information search systems make it difficult for users to quickly and easily understand the content of laws and regulations and related information. In addition, they provide uniform information without considering the user's emotional state, which means that users who are particularly stressed cannot receive sufficient support.

[1197] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for acquiring data from an official website, a means for analyzing the acquired data and extracting text data, and a means for analyzing the extracted text data using natural language processing to construct legal data. This allows legal information desired by the user to be quickly and easily searched for and provided, and further enables flexible provision of information according to the user's emotional state.

[1198] ---

[1199] An "official website" is an information dissemination medium on the Internet operated by a government agency or official organization.

[1200] "Means of acquiring data" refers to the process or method of collecting data from external sources via the Internet.

[1201] "Means of analyzing and extracting text data" refers to a method of using a program to decipher and analyze the content of the acquired data and extract the necessary information.

[1202] "Natural language processing" refers to techniques and means by which computers understand, interpret, and generate human language.

[1203] "Legal data" refers to official documents such as laws, rules, regulations, and safety guidelines, as well as information about their contents.

[1204] A "database" is a system for efficiently storing, searching, and managing large amounts of data.

[1205] A "query" refers to a command or question that commands a database to search for or manipulate information.

[1206] Simplification and summarization techniques are techniques and methods for reducing information-rich data into shorter, more understandable forms.

[1207] An "emotion recognition engine" is a system that analyzes and determines a user's emotional state from their input and operation data.

[1208] "Means for adding or modifying information to be provided according to emotions" refers to a method for appropriately adjusting the information to be provided based on the user's emotional state and adding necessary support.

[1209] This invention is a system that automatically acquires data from official websites, analyzes, saves, searches, and simplifies it, and combines it with an emotion engine that recognizes user emotions. The system's program processing and specific examples are described below. This system mainly includes a server, a terminal, and a user. The server is primarily responsible for data acquisition, analysis, storage, search, simplification, and emotion recognition, while the terminal provides the interface with the user.

[1210] Data acquisition and analysis

[1211] The server maintains a list of official website URLs, and periodically sends HTTP requests to each site on this list to retrieve their HTML content.

[1212] The server stores the acquired HTML content in local storage.

[1213] The server parses the saved HTML content and extracts the necessary text data. This step uses a scraping library (e.g. BeautifulSoup, Scrapy).

[1214] The server inputs the extracted text data into a natural language processing engine (e.g., SpaCy or the BERT model) to analyze and identify the legal data. As a result of the analysis, the title, article, clause number, and links to related laws and regulations are identified.

[1215] Data storage

[1216] The server stores the analyzed legal data in a database (e.g., PostgreSQL). The database schema is designed to structure and store data with attributes such as title, article, clause number, and links to related laws and regulations.

[1217] Processing user requests

[1218] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[1219] The terminal sends a request including the user's input to the server using the HTTP protocol.

[1220] The server extracts the query from the received request and searches the database.

[1221] Simplifying and providing search results

[1222] The server retrieves legal data that matches the query from the database, for example, the content of "Article 32 of the Labor Standards Act."

[1223] The server uses a natural language processing engine to simplify and summarize the search results, for example, summarizing them as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[1224] The server generates a response including the summarized legal data and sends it to the terminal.

[1225] The terminal displays the search results received from the server to the user, displaying the summarized content in an intuitive and easy-to-understand format.

[1226] emotion recognition

[1227] The server uses an emotion recognition engine (e.g., Sentiment Analysis API) to recognize emotions from the user's input queries and actions.

[1228] Based on the user's emotions recognized by the emotion engine, the server takes measures such as making the summary of the legal text more concise and displaying additional diagrams and tables if the user is feeling stressed.

[1229] If the server recognizes the user's emotions as negative, it will provide additional support information and FAQs to help them understand the relevant laws and regulations, as well as information on relevant consultation centers.

[1230] Specific examples

[1231] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following specific processing will occur.

[1232] 1. The user searches for "Labor Standards Act Article 32" on their device and sends a request.

[1233] 2. The server receives the request and retrieves data related to "Article 32 of the Labor Standards Act" from the database.

[1234] 3. The server simplifies the data it has obtained and creates a summary version.

[1235] 4. The server recognizes the user's emotions and adds supporting information for interpretation if it determines that the user is feeling anxious, for example.

[1236] 5. The server sends the summary and supporting information back to the terminal, which displays it to the user.

[1237] Prompt Sentence Examples

[1238] Below is a concrete example of a prompt sentence to be entered into this system.

[1239] 1. "Please tell me about Article 32 of the Labor Standards Act."

[1240] 2. "Please provide information about the maximum working hours."

[1241] This allows users to quickly and easily understand the content of the legislation and provides additional support if needed.

[1242] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1243] ---

[1244] Step 1:

[1245] Data Acquisition

[1246] The server maintains a list of official website URLs, and periodically sends HTTP requests to each site on this list to retrieve their HTML content.

[1247] Input: URL list

[1248] Output: HTML content retrieved from each site

[1249] What happens: The server cycles through the list of URLs, sends an HTTP request to each URL, and collects the returned HTML content.

[1250] Step 2:

[1251] Saving HTML Content

[1252] The server saves the retrieved HTML content in local storage, where the destination is specified as a specific directory.

[1253] Input: HTML content

[1254] Output: HTML file saved in local storage

[1255] Specific operation: The server writes and saves the retrieved HTML content as a file on the local disk.

[1256] Step 3:

[1257] Parsing HTML content

[1258] The server uses the BeautifulSoup library to parse the stored HTML content and extract the required text data.

[1259] Input: Saved HTML file

[1260] Output: Extracted text data (title of law, article, clause number, link to related law, etc.)

[1261] What it does: The server uses BeautifulSoup to parse HTML content and extract legal information from specific tags and class names.

[1262] Step 4:

[1263] Input to a natural language processing engine

[1264] The server inputs the extracted text data into a natural language processing engine (e.g., SpaCy or the BERT model) to analyze and identify legal data.

[1265] Input: Extracted text data

[1266] Output: Analysis results (structure of laws and regulations, links to related laws, etc.)

[1267] Specific operation: The server supplies the text data to a natural language processing engine, which then uses the engine to identify the structure and attributes of the legislation.

[1268] Step 5:

[1269] Saving to a database

[1270] The server stores the parsed legal data in a database (e.g., PostgreSQL).

[1271] Input: Parsed legal data

[1272] Output: Legal data stored in a database

[1273] Specific operation: The server inserts and stores the analysis results into the appropriate tables and columns in the database.

[1274] Step 6:

[1275] User search input

[1276] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[1277] Input: User query (e.g., "Labor Standards Act Article 32")

[1278] Output: The search request submitted

[1279] Specific operation: The user enters a query into the input form on the browser and clicks the search button to submit the request.

[1280] Step 7:

[1281] Submitting a Request

[1282] The terminal sends a request including the user's input to the server using the HTTP protocol.

[1283] Input: User query

[1284] Output: Request sent to the server

[1285] Specific operation: The device sends the entered query to the server as an HTTP request.

[1286] Step 8:

[1287] Query extraction and database search

[1288] The server extracts the query from the received request and searches the database.

[1289] Input: The request sent

[1290] Output: Legal data retrieved from the database

[1291] What happens: The server parses the request, gets the query, and then executes the query against the database.

[1292] Step 9:

[1293] Data Acquisition and Summarization

[1294] The server retrieves legal data that matches the query from the database.

[1295] Input: The database query that was executed

[1296] Output: Retrieved legal data

[1297] Specific operation: The server retrieves the relevant legal data from the database and summarizes it using a natural language processing engine.

[1298] Step 10:

[1299] Generating and Sending a Response

[1300] The server generates the generated summary data as a response and transmits it to the terminal.

[1301] Input: Obtained legal data

[1302] Output: A response containing summarized statute data

[1303] Specific operation: The server generates summarized legal data as an HTTP response and sends it to the terminal.

[1304] Step 11:

[1305] Displaying search results

[1306] The terminal displays the summary data received from the server to the user.

[1307] Input: The response sent by the server

[1308] Output: Summary data displayed to the user

[1309] Specific operation: The device displays the received summary data in the browser in an appropriate format.

[1310] Step 12:

[1311] User Emotion Recognition

[1312] The server uses an emotion recognition engine to recognize emotions from the user's input query and operation data.

[1313] Input: User query and operation data

[1314] Output: User's emotional state

[1315] Specific operation: The server inputs the query and operation log into the emotion recognition engine and analyzes the user's emotional state.

[1316] Step 13:

[1317] Emotion-based responses

[1318] The server tailors the information it provides based on the user's emotional state, adding support information and relevant helpline information as needed.

[1319] Input: User's emotional state

[1320] Output: Adjusted information

[1321] Specific operation: The server takes into account the emotion recognition results and generates a response including additional support information and FAQs, which it then sends to the device.

[1322] The above is the specific processing flow of this system.

[1323] (Application example 2)

[1324] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1325] Conventional security information systems manually retrieve and analyze information from official websites, making it difficult to quickly obtain and provide the latest information to users. Furthermore, they do not provide appropriate information based on the user's emotions or psychological state, which can lead to anxiety and stress. To address this issue, there is a demand for a system that allows users to quickly obtain appropriate security information with peace of mind.

[1326] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1327] In this invention, the server includes means for acquiring data from official websites, means for analyzing the acquired data to extract text data, means for analyzing the extracted text data using natural language processing to construct legal data, and means for recognizing user emotions and providing related information, thereby enabling users to quickly acquire the latest and most relevant security information with peace of mind.

[1328] An "official website" is a website officially operated by a government agency, public organization, or other entity with the purpose of providing reliable information.

[1329] A "means for obtaining data" is a means that has the function of sending an HTTP request and receiving data such as HTML content from a specified URL.

[1330] "Means for analyzing data and extracting text data" refers to the use of scraping or text analysis techniques to filter unnecessary information from the acquired data and extract the necessary text data.

[1331] "Means of analyzing using natural language processing and constructing legal data" refers to a means of using a natural language processing engine to analyze the titles, articles, clause numbers, related links, etc. of legal data from text data to generate legal data.

[1332] "Means for recognizing a user's emotions and providing related information" refers to means that uses an emotion recognition engine to analyze emotions from the user's input and operations and provide content and support information that is adapted to those emotions.

[1333] "Means of storing in a database" refers to the use of technologies and tools to structure the analyzed legal data and store it in a database.

[1334] The "means for receiving a query and searching for relevant legal data" refers to a means for receiving a search request from a user, searching the database based on that request, and obtaining relevant legal data.

[1335] "Means for simplifying and summarizing using natural language processing" refers to a means of concisely summarizing acquired legal data using a natural language processing engine and providing it in summary form.

[1336] "Means for providing to the user" refers to means including interfaces and UI components for conveying search results and summary information to the user.

[1337] This invention is a system that automatically acquires data from official websites, analyzes, stores, and searches the data, and combines it with an emotion engine that recognizes user emotions. The system mainly includes a server, a terminal, and a user. The server is primarily responsible for data acquisition, analysis, storage, search, simplification, and emotion recognition, while the terminal provides the interface with the user.

[1338] Data acquisition and analysis

[1339] The server has a list of URLs for reliable official websites. Based on this list, it periodically sends HTTP requests to each site to obtain their HTML content. The obtained HTML content is saved in local storage, and the saved HTML content is parsed to extract the required text data. This step uses a scraping library (e.g., BeautifulSoup, Scrapy).

[1340] The text data is input into a natural language processing engine (e.g., TextBlob, spaCy) and constructed as legal data, which identifies titles, articles, clause numbers, links to related laws, etc.

[1341] Data storage

[1342] The server stores the analyzed legal data in a database whose schema is designed to comprehensively store information such as titles, articles, clause numbers, and links to related laws and regulations.

[1343] Processing user requests

[1344] A user uses a browser on their device to enter information about a specific law into a search form. For example, they might enter "Article 32 of the Labor Standards Act." The device then sends a request containing the user's input to the server using the HTTP protocol. The server then extracts the query from the received request and searches its database.

[1345] Simplifying and providing search results

[1346] The server retrieves legal data that matches the query from the database. For example, it retrieves the content of "Article 32 of the Labor Standards Act." The server then uses a natural language processing engine to simplify and summarize the search results. For example, it may summarize the results as "Article 32 of the Labor Standards Act includes a provision that working hours not exceed eight hours per day." The server then generates a response containing the summarized legal data and sends it to the terminal. The terminal then displays the search results received from the server to the user. The summarized content is displayed in an intuitive and easy-to-understand format.

[1347] emotion recognition

[1348] The server uses an emotion engine to recognize emotions from the user's input queries and operations. Based on the user's emotions recognized by the emotion engine (e.g., IBM Watson Tone Analyzer, Microsoft Azure Emotion API), measures such as making the summary of the legal text more concise and displaying additional figures and tables may be taken if the user is feeling stressed. Furthermore, if the user's emotions are recognized as negative, additional support information, FAQs, and even information on relevant consultation centers may be provided to help the user understand the relevant laws and regulations.

[1349] Specific examples

[1350] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following specific process takes place: The user searches for "Article 32 of the Labor Standards Act" on their device and sends a request. The server receives the request and retrieves data related to "Article 32 of the Labor Standards Act" from a database. The server simplifies the retrieved data and creates a summary. The server recognizes the user's emotions, and if it determines that the user is feeling anxious, for example, it adds supporting information for interpretation. The server returns the summary and supporting information to the device, which displays it to the user.

[1351] This allows users to quickly and easily understand the content of the legislation and receive additional support if needed.

[1352] Prompt Sentence Examples

[1353] "Please briefly explain the latest security alerts. Use emotion recognition to provide a clear, concise explanation that will not alarm users and include any necessary countermeasures."

[1354] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1355] Step 1:

[1356] The server retrieves the data from the official website.

[1357] Input: Official website URL list.

[1358] How it works: The server periodically sends HTTP requests to retrieve HTML content.

[1359] Output: The retrieved HTML content.

[1360] Step 2:

[1361] The server analyzes the acquired data and extracts text data.

[1362] Input: The retrieved HTML content.

[1363] What it does: Use a scraping library such as BeautifulSoup to extract the required text data from the HTML content.

[1364] Output: The extracted text data.

[1365] Step 3:

[1366] The server analyzes the extracted text data using natural language processing and constructs legal data.

[1367] Input: Extracted text data.

[1368] How it works: It uses a natural language processing engine such as TextBlob or spaCy to parse the text data and construct the statute title, article, clause number, related links, etc.

[1369] Output: Constructed legal data.

[1370] Step 4:

[1371] The server stores the analyzed legal data in a database.

[1372] Input: Constructed legal data.

[1373] What it does: Executes SQL queries to a database and stores legal data.

[1374] Output: Legislation data stored in a database.

[1375] Step 5:

[1376] The user uses the device's browser to search for information about a particular law.

[1377] Input: User's search query (e.g., "Labor Standards Act Article 32").

[1378] What happens: A user fills out a search form and clicks the search button.

[1379] Output: A search request is sent to the server.

[1380] Step 6:

[1381] The server receives queries from users and retrieves relevant legal data.

[1382] Input: The user's search query.

[1383] How it works: Runs a search query against the database to retrieve relevant legal data.

[1384] Output: Retrieved legal data.

[1385] Step 7:

[1386] The server simplifies and summarizes the search results using natural language processing.

[1387] Input: Retrieved legal data.

[1388] How it works: Uses a natural language processing engine to concisely summarize legal data.

[1389] Output: Simplified and summarized statutory data.

[1390] Step 8:

[1391] The server recognizes emotions from the user's input queries and operations.

[1392] Input: User search queries and operation logs.

[1393] How it works: Analyzes the user's emotions using an emotion engine (e.g., IBM Watson Tone Analyzer).

[1394] Output: User's emotion evaluation result.

[1395] Step 9:

[1396] The server provides relevant information based on the user's emotions.

[1397] Input: User's emotion evaluation result.

[1398] What it does: Add support information, FAQs, and helpline information that adapts to the user's emotions.

[1399] Output: Response data including auxiliary information.

[1400] Step 10:

[1401] The server provides simplified and summarized search results to the user.

[1402] Input: Simplified and summarized statutory data, and response data including supporting information.

[1403] Operation: Generates response data and sends it to the device.

[1404] Output: Simplified and summarized statutory data and supporting information displayed to the user.

[1405] Step 11:

[1406] The terminal displays the search results received from the server to the user.

[1407] Input: Response data from the server.

[1408] What it does: Visually displays the data received by the device.

[1409] Output: Search results and supporting information displayed to the user.

[1410] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1411] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1412] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1413] [Fourth embodiment]

[1414] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1415] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1416] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1417] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1418] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1419] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1420] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1421] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1422] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1423] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1424] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1425] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1426] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1427] ---

[1428] This invention is a system that automatically acquires data from official websites and includes a series of processes to analyze, store, search, and simplify the data. The system's program processing and specific examples are explained below.

[1429] This system mainly includes a server, a terminal, and a user. The server is mainly responsible for acquiring, analyzing, storing, searching, and simplifying data, while the terminal provides an interface with the user.

[1430] Data acquisition and analysis

[1431] The server has a list of URLs for official government ministry websites. Based on this list, it periodically retrieves the HTML content of each website. The server sends an HTTP request and stores the returned HTML data in local storage.

[1432] The server then parses the saved HTML content and extracts the required text data. This step uses a scraping library (e.g. BeautifulSoup, Scrapy).

[1433] The server inputs the extracted text data into a natural language processing engine, which analyzes and identifies each item (title, article, clause number, link to related laws, etc.), thereby constructing legal data.

[1434] Data storage

[1435] The server stores the analyzed legal data in a database with a schema that comprehensively stores legal titles, articles, clause numbers, and links to related legal documents.

[1436] Processing user requests

[1437] The user enters information about a specific law into a search form in the browser on their device. For example, they enter the query "Article 32 of the Labor Standards Act."

[1438] The terminal sends a request containing the user's input to the server. The terminal makes this request using the HTTP protocol.

[1439] The server extracts the query from the received request and searches the database.

[1440] Simplifying and providing search results

[1441] The server retrieves legal data that matches the query from the database. For example, it retrieves the content of "Article 32 of the Labor Standards Act" from the database.

[1442] The server uses a natural language processing engine to simplify and summarize the retrieved legal data, which is summarized as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[1443] The server generates a response including the summarized legal data and sends it to the terminal.

[1444] The terminal displays the search results received from the server to the user, who can then check them through an interface that displays the summarized content in an intuitive and easy-to-understand manner.

[1445] Specific examples

[1446] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following specific processing will occur.

[1447] 1. The user searches for "Labor Standards Act Article 32" on their device and sends a request.

[1448] 2. The server receives the request and retrieves data related to "Article 32 of the Labor Standards Act" from the database.

[1449] 3. The server simplifies the data it has obtained and creates a summary version.

[1450] 4. The server sends a summary of the information back to the terminal, which displays it to the user.

[1451] This allows users to quickly and easily understand the content of the legislation.

[1452] The processing flow will be explained below.

[1453] ---

[1454] Step 1:

[1455] The server has a list of URLs for official government ministry websites, and periodically sends HTTP requests to each site on this list to retrieve its HTML content.

[1456] Step 2:

[1457] The server stores the acquired HTML content in local storage.

[1458] Step 3:

[1459] The server parses the saved HTML content and extracts the required text data using a scraping library (e.g. BeautifulSoup, Scrapy).

[1460] Step 4:

[1461] The server inputs the extracted text data into a natural language processing engine to analyze and identify legal data, such as titles, articles, clause numbers, and links to related laws and regulations.

[1462] Step 5:

[1463] The server stores the analyzed legal data in a database whose schema is designed to comprehensively store information such as titles, articles, clause numbers, and links to related laws and regulations.

[1464] Step 6:

[1465] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[1466] Step 7:

[1467] The terminal sends a request including the user's input to the server using the HTTP protocol.

[1468] Step 8:

[1469] The server extracts the query from the received request and searches the database.

[1470] Step 9:

[1471] The server retrieves legal data that matches the query from the database, for example, the content of "Article 32 of the Labor Standards Act."

[1472] Step 10:

[1473] The server uses a natural language processing engine to simplify and summarize the search results, for example, summarizing them as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[1474] Step 11:

[1475] The server generates a response including the summarized legal data and sends it to the terminal.

[1476] Step 12:

[1477] The terminal displays the search results received from the server to the user, displaying the summarized content in an intuitive and easy-to-understand format.

[1478] ---

[1479] In this way, a system has been created that allows users to quickly understand the content of laws and regulations through many processing steps.

[1480] Example 1

[1481] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1482] In today's information society, the amount of legal information available from official websites is enormous, making it difficult to quickly and accurately retrieve and analyze the information needed. This has led to a growing need for systems that efficiently retrieve and simplify the specific legal information users require. There is also a need for a method to effectively manage the retrieved and analyzed legal data and present it to users in an easy-to-understand manner.

[1483] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1484] In this invention, the server includes means for acquiring data from official websites, means for analyzing the acquired data and extracting text data, means for analyzing the extracted text data using natural language processing to construct legal data, means for storing the analyzed legal data in a storage device, means for receiving queries from users and searching for relevant legal data, means for simplifying and summarizing search results using a generative AI model, means for generating prompt sentences and inputting them into the generative AI model to generate summarized search results, and means for providing the simplified and summarized search results to users, thereby enabling users to quickly and accurately acquire necessary legal information and easily understand its content.

[1485] An "official website" is an official information site on the Internet operated by a government, public institution, company, etc.

[1486] "Data" means information that is collected, stored, and analyzed electronically.

[1487] "Means" are the specific methods or processes used to achieve a goal.

[1488] "Acquisition" refers to the act of gathering necessary data or information.

[1489] "Analysis" is the process of breaking down data and information into an understandable form and clarifying its meaning and structure.

[1490] "Text data" refers to information data expressed as characters.

[1491] "Natural language processing" is a technology that allows computers to understand, analyze, and generate human language.

[1492] "Legal data" means data that contains information about laws and regulations.

[1493] A "storage device" is an electronic device or system for long-term retention of data.

[1494] A "query" is a request or question used to retrieve information from a database or the like.

[1495] "Search" is the process of finding specific information.

[1496] A "generative AI model" is an artificial intelligence model that automatically generates new information or summaries from input data.

[1497] "Simplification" refers to putting complex information into a simple form to make it easier to understand.

[1498] A summary is a short summary of the main points or content.

[1499] A "prompt" is an instruction or question that is input into a generative AI model.

[1500] "User" means an individual or organization that uses this system.

[1501] This invention is a system that includes a series of processes to automatically acquire data from official websites and analyze, store, search, and simplify it. The system mainly includes a server, a terminal, and a user, and the specific role of each component is described in detail below.

[1502] Data acquisition and analysis

[1503] The server loads a configuration file called "urls.txt" that contains a list of URLs for official government ministry websites. This list contains the URLs for each site to be harvested. The server uses the Python requests library to send HTTP requests to these URLs and receives the returned HTML data. The received HTML data is saved in the "raw_data" folder in local storage.

[1504] The server sequentially reads the saved HTML files and uses the Python scraping library BeautifulSoup to extract the necessary text data, such as the title and article text of the law.

[1505] The server inputs the extracted text data into a natural language processing engine (e.g., Spacy or NLTK), analyzes each item (title, article, clause number, link to related legislation, etc.), and converts it into structured legal data.

[1506] Data storage

[1507] The server stores the analyzed legal data in an SQL database (e.g., MySQL, PostgreSQL). The database schema is designed to efficiently store the title, article, clause number, and link to related laws for each legal entity.

[1508] Processing user requests

[1509] The user enters a query about a specific law into a search form on the device's browser, for example, "Article 32 of the Labor Standards Act," and clicks the search button.

[1510] The terminal sends an HTTP request containing the query entered by the user to the server, which sends data containing the query in JSON format to the server.

[1511] The server extracts the query from the received request, constructs an SQL query, and searches the database. For example, it executes the SQL statement "SELECT FROM Laws and Regulations WHERE title = 'Labor Standards Act Article 32'".

[1512] Simplifying and providing search results

[1513] The server retrieves legal data from the database that matches the query, including detailed information such as the full text of the legal document.

[1514] The server uses a generative AI model (e.g., GPT-4) to simplify and summarize the acquired legal data. For example, by inputting a prompt such as "Please summarize Article 32 of the Labor Standards Act" into the generative AI model, summarized information is generated.

[1515] The server generates an HTTP response including the summarized legal data and transmits it to the terminal. For example, the server transmits the generated summary to the terminal and displays it to the user.

[1516] The terminal analyzes the response received from the server and converts it into HTML for display. A summary of the legal content is displayed through an interface that is intuitive and easy for the user to understand.

[1517] Specific examples

[1518] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following prompt sentence is input to the generative AI model:

[1519] Please summarize Article 32 of the Labor Standards Act.

[1520] This prompt allows the generative AI model to summarize the law, and the generated information is quickly provided to the user.

[1521] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1522] Step 1:

[1523] The server reads the configuration file "urls.txt," which contains a list of URLs for official government ministry websites. This allows it to obtain the URLs of the sites to be retrieved as input. The server then uses the Python requests library to send an HTTP request to each URL. This action returns HTML data for each website as output. This HTML data is saved in the "raw_data" folder in local storage.

[1524] Step 2:

[1525] The server sequentially reads the HTML files stored in the "raw_data" folder in local storage. It uses the BeautifulSoup library to extract the necessary text data (e.g., law titles, article text, etc.) from the HTML files. The input is the HTML files, and the output is the extracted text data.

[1526] Step 3:

[1527] The server inputs the extracted text data into a natural language processing engine (e.g., Spacy or NLTK). The natural language processing engine analyzes the text data and identifies each item (title, article, clause number, link to related laws, etc.). The output of this process is structured legal data. For example, it is categorized into "legal title," "article," "clause number," etc.

[1528] Step 4:

[1529] The server stores the parsed legal data in an SQL database (e.g., MySQL, PostgreSQL). The input is structured legal data, which is stored in the corresponding tables in the database. The output is the data held in the database.

[1530] Step 5:

[1531] The user enters a query about a specific law into a search form on the device's browser. For example, they enter "Labor Standards Act Article 32." The user's input query is the input for this step, and when the user presses the search button, the query is sent to the server. This query is used in the next step.

[1532] Step 6:

[1533] The terminal sends an HTTP request containing the query entered by the user to the server. The input is the user's query, and the output is the HTTP request sent to the server.

[1534] Step 7:

[1535] The server extracts the query from the received HTTP request, constructs an SQL query, and searches the database. For example, it executes the SQL statement "SELECT FROM Laws and Regulations WHERE title = 'Labor Standards Act Article 32'". The input is the user's query and the database, and the output is the relevant law data.

[1536] Step 8:

[1537] The server retrieves legal data from the database and generates a prompt sentence for the generative AI model (e.g., GPT-4) and inputs it. The prompt sentence is "Please summarize Article 32 of the Labor Standards Act." The input is the legal data and the prompt sentence, and the generative AI model outputs the summarized information.

[1538] Step 9:

[1539] The server generates an HTTP response containing summary information obtained from the generative AI model and sends it to the terminal. The input is the generated summary information, and the output is the HTTP response sent to the terminal.

[1540] Step 10:

[1541] The terminal analyzes the response received from the server and converts it into HTML for display. This allows the user to see a summary of the legal content in an intuitively easy-to-understand format. The input is the response from the server, and the output is the summary information displayed on the browser.

[1542] Through the above process, users can quickly and accurately obtain specific legal information and easily understand its contents.

[1543] (Application example 1)

[1544] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1545] In modern society, it is important to effectively acquire and analyze large amounts of information from official information disclosure sites and provide it in a form that users can easily understand. In particular, information such as laws, news, and articles is often difficult for users to understand, so accurate information collection and effective information provision are required. The objective of this invention is to provide a system that efficiently acquires, analyzes, stores, searches, and summarizes information from official information disclosure sites, and provides information to users quickly and intuitively.

[1546] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1547] In this invention, the server includes means for acquiring data from official information disclosure sites, means for analyzing the acquired data and extracting text data, means for analyzing the extracted text data using natural language processing and constructing information data, means for storing the analyzed information data in a database, means for receiving queries from users and searching for related information data, means for simplifying and summarizing search results using natural language processing, means for providing the simplified and summarized search results to the user, and means for using a generative AI model to generate summaries of the information data. This makes it possible to efficiently analyze the vast amount of information acquired from official information disclosure sites and quickly provide it to users in a form that is intuitively understandable.

[1548] An "official information disclosure site" is a website operated by a government agency or other official organization to disclose information.

[1549] "Means of retrieving data" refers to the methods and technologies used to retrieve HTML content from a specified URL.

[1550] "Means for extracting text data" refers to methods and technologies for analyzing and extracting the necessary text information from HTML content.

[1551] "Means of analysis using natural language processing" refers to methods and technologies that use natural language processing technology to analyze text data and identify and extract specific information items.

[1552] "Means for constructing information data" refers to methods and technologies for generating structured data for storage in a database based on analyzed text data.

[1553] "Means of storing in a database" refers to the methods and technologies for storing and managing analyzed information data using a database management system.

[1554] "Means for receiving and searching queries" refers to methods and techniques for searching a database for relevant information based on user input.

[1555] "Means for simplifying and summarizing search results" refers to methods and technologies for concisely summarizing searched information using natural language processing technology.

[1556] "Means of providing to users" refers to the methods and technologies used to display and provide search results through a user-friendly interface.

[1557] "Means of using a generative AI model" refers to methods or technologies that use a generative AI model to summarize or generate information.

[1558] This invention is a system that automatically acquires data from official information disclosure sites, analyzes and stores it, and simplifies and summarizes relevant information in response to user queries. Specifically, it is composed of a server, a terminal, and a user.

[1559] The server first retrieves data from the official information disclosure website using an HTTP request and saves the retrieved HTML content in local storage.The server then uses a scraping library such as BeautifulSoup or Scrapy to parse the HTML content and extract the required text data.

[1560] The extracted text data is then input into a natural language processing engine, which uses, for example, a transformers library to analyze and identify each piece of text data and construct information data, including the title, text, related links, etc.

[1561] The server also stores the analyzed information data in a database, which uses, for example, SQLite and has a schema for efficiently storing and managing the information data.

[1562] When a user inputs a query via a device (smartphone, smart glasses, head-mounted display, etc.), the device sends the query to the server. The server searches the database based on the received query to retrieve relevant information data. At this time, it uses a generative AI model to simplify and summarize the search results.

[1563] The generated summary is then sent to the terminal using the HTTP protocol and provided to the user, allowing the user to quickly and intuitively understand the official information.

[1564] Specific examples

[1565] For example, when a user inputs a request such as "I want to know about the latest labor law revisions" and clicks the search button, the following specific processing is carried out.

[1566] 1. The user searches for "latest labor law changes" on their device and submits a request.

[1567] 2. The server receives the request and retrieves the relevant data from the database.

[1568] 3. The server simplifies the data it receives and creates a summary version, using a generative AI model.

[1569] 4. The server sends a summary of the information back to the terminal, which displays it to the user.

[1570] Prompt Sentence Examples

[1571] Write a Python program to automatically simplify complex text in news articles and news articles. This program will include functions to retrieve HTML from a specified URL, extract the text from it, and generate and save a simple summary.

[1572] In this way, it is possible to efficiently analyze the vast amount of information obtained from official information disclosure sites and quickly provide it to users in a format that they can intuitively understand.

[1573] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1574] Step 1:

[1575] Data Acquisition: The server acquires data from the official information disclosure site. Specifically, the server sends an HTTP request and receives the returned HTML content. This acquires HTML data from the official information disclosure site. The input is a list of site URLs, and the output is HTML data.

[1576] Step 2:

[1577] Extracting text data: The server analyzes the acquired HTML data and extracts the necessary text data. Specifically, it extracts text from HTML using a scraping library such as BeautifulSoup or Scrapy. The input of this step is HTML data, and the output is the extracted text data.

[1578] Step 3:

[1579] Analysis by natural language processing: The server inputs the extracted text data into a natural language processing engine to construct information data. For example, it uses the transformers library to identify and extract titles and body text from the text. The input of this step is the extracted text data, and the output is the constructed information data.

[1580] Step 4:

[1581] Data storage: The server stores the analyzed information data in a database. Specifically, SQLite is used to efficiently store and manage the information data. The input of this step is the information data, and the output is the stored database entry.

[1582] Step 5:

[1583] Receiving a query: The user inputs and sends a query to search for specific information through the terminal. For example, they input a query such as "Information on the latest labor law revisions." The input of this step is the query entered by the user, and the output is an HTTP request to the server.

[1584] Step 6:

[1585] Database search: The server searches the database based on the received query to obtain relevant information data. The input is the user's query, and the output is the search result information data.

[1586] Step 7:

[1587] Summarization using a generative AI model: The server uses a generative AI model to simplify and summarize the information data in the search results. Specifically, it uses a generative AI model to shorten long text. The input for this step is the information data in the search results, and the output is the summarized information.

[1588] Step 8:

[1589] Providing summary results: The server sends the summarized information to the user's terminal, which then displays it to the user. The input is the summarized information data, and the output is the display on the user's terminal.

[1590] This allows users to quickly and intuitively understand official information.

[1591] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1592] ---

[1593] This invention is a system that automatically acquires data from official websites, analyzes, saves, searches, and simplifies it, and combines it with an emotion engine that recognizes the user's emotions. The system's program processing and specific examples are explained below.

[1594] This system mainly includes a server, a terminal, and a user. The server is mainly responsible for data acquisition, analysis, storage, retrieval, simplification, and emotion recognition, while the terminal provides an interface with the user.

[1595] Data acquisition and analysis

[1596] The server has a list of URLs for official government ministry websites, and periodically sends HTTP requests to each site on this list to retrieve its HTML content.

[1597] The server stores the acquired HTML content in local storage.

[1598] The server parses the saved HTML content and extracts the required text data. This step uses a scraping library (e.g. BeautifulSoup, Scrapy).

[1599] The server inputs the extracted text data into a natural language processing engine to analyze and identify legal data, such as titles, articles, clause numbers, and links to related laws and regulations.

[1600] Data storage

[1601] The server stores the analyzed legal data in a database whose schema is designed to comprehensively store information such as titles, articles, clause numbers, and links to related laws and regulations.

[1602] Processing user requests

[1603] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[1604] The terminal sends a request including the user's input to the server using the HTTP protocol.

[1605] The server extracts the query from the received request and searches the database.

[1606] Simplifying and providing search results

[1607] The server retrieves legal data that matches the query from the database, for example, the content of "Article 32 of the Labor Standards Act."

[1608] The server uses a natural language processing engine to simplify and summarize the search results, for example, summarizing them as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[1609] The server generates a response including the summarized legal data and sends it to the terminal.

[1610] The terminal displays the search results received from the server to the user, displaying the summarized content in an intuitive and easy-to-understand format.

[1611] emotion recognition

[1612] The server uses an emotion engine to recognize emotions from the user's input queries and operations.

[1613] Based on the user's emotions recognized by the emotion engine, the server takes measures such as making the summary of the legal text more concise and displaying additional diagrams and tables if the user is feeling stressed.

[1614] If the server recognizes that the user's sentiment is negative, it will provide additional support information, FAQs, and even relevant consultation service information to help the user understand relevant laws and regulations.

[1615] Specific examples

[1616] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following specific processing will occur.

[1617] 1. The user searches for "Labor Standards Act Article 32" on their device and sends a request.

[1618] 2. The server receives the request and retrieves data related to "Article 32 of the Labor Standards Act" from the database.

[1619] 3. The server simplifies the data it has obtained and creates a summary version.

[1620] 4. The server recognizes the user's emotions and adds supporting information for interpretation if it determines that the user is feeling anxious, for example.

[1621] 5. The server sends the summary and supporting information back to the terminal, which displays it to the user.

[1622] This allows users to quickly and easily understand the content of the legislation and receive additional support if needed.

[1623] ---

[1624] The processing flow will be explained below.

[1625] ---

[1626] Step 1:

[1627] The server has a list of URLs for official government ministry websites, and periodically sends HTTP requests to each site on this list to retrieve its HTML content.

[1628] Step 2:

[1629] The server stores the acquired HTML content in local storage.

[1630] Step 3:

[1631] The server parses the saved HTML content and extracts the required text data using a scraping library (e.g. BeautifulSoup, Scrapy).

[1632] Step 4:

[1633] The server passes the extracted text data to a natural language processing engine, which analyzes and identifies the legal data, specifically identifying the legal title, article, clause number, and links to related laws.

[1634] Step 5:

[1635] The server stores the analyzed legal data in a database that comprehensively stores legal titles, articles, clause numbers, links to related laws, and other information in an easily accessible format.

[1636] Step 6:

[1637] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[1638] Step 7:

[1639] The terminal sends an HTTP request including the user's input to the server.

[1640] Step 8:

[1641] The server extracts the query from the received request and searches the database, for example, for data related to "Article 32 of the Labor Standards Act."

[1642] Step 9:

[1643] The server retrieves legal data matching the query from the database, including specific legal provisions and related information.

[1644] Step 10:

[1645] The server uses a natural language processing engine to simplify and summarize the search results, for example, summarizing them as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[1646] Step 11:

[1647] The server uses an emotion engine to recognize emotions from the user's input queries and operations. The emotion engine analyzes the user's query content and operation history.

[1648] Step 12:

[1649] The server customizes the search results based on the user's emotions as recognized by the emotion engine, for example, making the search results more concise and adding diagrams and tables if the user is feeling stressed.

[1650] Step 13:

[1651] Based on emotion recognition, if a negative emotion is detected, the server will provide support information, FAQs, consultation service information, etc. to help users understand relevant laws and regulations.

[1652] Step 14:

[1653] The server generates a response including the simplified and summarized legal data and additional supporting information and transmits it to the terminal.

[1654] Step 15:

[1655] The terminal displays the response received from the server to the user, displaying summarized legal data, related information, and additional supporting information in an intuitive and easy-to-understand format.

[1656] ---

[1657] In this way, through many concrete steps, users can quickly and easily understand the content of the law and receive additional support if necessary.

[1658] Example 2

[1659] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1660] Conventional legal information search systems make it difficult for users to quickly and easily understand the content of laws and regulations and related information. In addition, they provide uniform information without considering the user's emotional state, which means that users who are particularly stressed cannot receive sufficient support.

[1661] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for acquiring data from an official website, a means for analyzing the acquired data and extracting text data, and a means for analyzing the extracted text data using natural language processing to construct legal data. This allows legal information desired by the user to be quickly and easily searched for and provided, and further enables flexible provision of information according to the user's emotional state.

[1662] ---

[1663] An "official website" is an information dissemination medium on the Internet operated by a government agency or official organization.

[1664] "Means of acquiring data" refers to the process or method of collecting data from external sources via the Internet.

[1665] "Means of analyzing and extracting text data" refers to a method of using a program to decipher and analyze the content of the acquired data and extract the necessary information.

[1666] "Natural language processing" refers to techniques and means by which computers understand, interpret, and generate human language.

[1667] "Legal data" refers to official documents such as laws, rules, regulations, and safety guidelines, as well as information about their contents.

[1668] A "database" is a system for efficiently storing, searching, and managing large amounts of data.

[1669] A "query" refers to a command or question that commands a database to search for or manipulate information.

[1670] Simplification and summarization techniques are techniques and methods for reducing information-rich data into shorter, more understandable forms.

[1671] An "emotion recognition engine" is a system that analyzes and determines a user's emotional state from their input and operation data.

[1672] "Means for adding or modifying information to be provided according to emotions" refers to a method for appropriately adjusting the information to be provided based on the user's emotional state and adding necessary support.

[1673] This invention is a system that automatically acquires data from official websites, analyzes, saves, searches, and simplifies it, and combines it with an emotion engine that recognizes user emotions. The system's program processing and specific examples are described below. This system mainly includes a server, a terminal, and a user. The server is primarily responsible for data acquisition, analysis, storage, search, simplification, and emotion recognition, while the terminal provides the interface with the user.

[1674] Data acquisition and analysis

[1675] The server maintains a list of official website URLs, and periodically sends HTTP requests to each site on this list to retrieve their HTML content.

[1676] The server stores the acquired HTML content in local storage.

[1677] The server parses the saved HTML content and extracts the necessary text data. This step uses a scraping library (e.g. BeautifulSoup, Scrapy).

[1678] The server inputs the extracted text data into a natural language processing engine (e.g., SpaCy or the BERT model) to analyze and identify the legal data. As a result of the analysis, the title, article, clause number, and links to related laws and regulations are identified.

[1679] Data storage

[1680] The server stores the analyzed legal data in a database (e.g., PostgreSQL). The database schema is designed to structure and store data with attributes such as title, article, clause number, and links to related laws and regulations.

[1681] Processing user requests

[1682] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[1683] The terminal sends a request including the user's input to the server using the HTTP protocol.

[1684] The server extracts the query from the received request and searches the database.

[1685] Simplifying and providing search results

[1686] The server retrieves legal data that matches the query from the database, for example, the content of "Article 32 of the Labor Standards Act."

[1687] The server uses a natural language processing engine to simplify and summarize the search results, for example, summarizing them as "Article 32 of the Labor Standards Act includes a provision that working hours shall not exceed eight hours per day."

[1688] The server generates a response including the summarized legal data and sends it to the terminal.

[1689] The terminal displays the search results received from the server to the user, displaying the summarized content in an intuitive and easy-to-understand format.

[1690] emotion recognition

[1691] The server uses an emotion recognition engine (e.g., Sentiment Analysis API) to recognize emotions from the user's input queries and actions.

[1692] Based on the user's emotions recognized by the emotion engine, the server takes measures such as making the summary of the legal text more concise and displaying additional diagrams and tables if the user is feeling stressed.

[1693] If the server recognizes the user's emotions as negative, it will provide additional support information and FAQs to help them understand the relevant laws and regulations, as well as information on relevant consultation centers.

[1694] Specific examples

[1695] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following specific processing will occur.

[1696] 1. The user searches for "Labor Standards Act Article 32" on their device and sends a request.

[1697] 2. The server receives the request and retrieves data related to "Article 32 of the Labor Standards Act" from the database.

[1698] 3. The server simplifies the data it has obtained and creates a summary version.

[1699] 4. The server recognizes the user's emotions and adds supporting information for interpretation if it determines that the user is feeling anxious, for example.

[1700] 5. The server sends the summary and supporting information back to the terminal, which displays it to the user.

[1701] Prompt Sentence Examples

[1702] Below is a concrete example of a prompt sentence to be entered into this system.

[1703] 1. "Please tell me about Article 32 of the Labor Standards Act."

[1704] 2. "Please provide information about the maximum working hours."

[1705] This allows users to quickly and easily understand the content of the legislation and provides additional support if needed.

[1706] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1707] ---

[1708] Step 1:

[1709] Data Acquisition

[1710] The server maintains a list of official website URLs, and periodically sends HTTP requests to each site on this list to retrieve their HTML content.

[1711] Input: URL list

[1712] Output: HTML content retrieved from each site

[1713] What happens: The server cycles through the list of URLs, sends an HTTP request to each URL, and collects the returned HTML content.

[1714] Step 2:

[1715] Saving HTML Content

[1716] The server saves the retrieved HTML content in local storage, where the destination is specified as a specific directory.

[1717] Input: HTML content

[1718] Output: HTML file saved in local storage

[1719] Specific operation: The server writes and saves the retrieved HTML content as a file on the local disk.

[1720] Step 3:

[1721] Parsing HTML content

[1722] The server uses the BeautifulSoup library to parse the stored HTML content and extract the required text data.

[1723] Input: Saved HTML file

[1724] Output: Extracted text data (title of law, article, clause number, link to related law, etc.)

[1725] What it does: The server uses BeautifulSoup to parse HTML content and extract legal information from specific tags and class names.

[1726] Step 4:

[1727] Input to a natural language processing engine

[1728] The server inputs the extracted text data into a natural language processing engine (e.g., SpaCy or the BERT model) to analyze and identify legal data.

[1729] Input: Extracted text data

[1730] Output: Analysis results (structure of laws and regulations, links to related laws, etc.)

[1731] Specific operation: The server supplies the text data to a natural language processing engine, which then uses the engine to identify the structure and attributes of the legislation.

[1732] Step 5:

[1733] Saving to a database

[1734] The server stores the parsed legal data in a database (e.g., PostgreSQL).

[1735] Input: Parsed legal data

[1736] Output: Legal data stored in a database

[1737] Specific operation: The server inserts and stores the analysis results into the appropriate tables and columns in the database.

[1738] Step 6:

[1739] User search input

[1740] The user uses the device's browser to enter information about a specific law into a search form, for example, "Article 32 of the Labor Standards Act."

[1741] Input: User query (e.g., "Labor Standards Act Article 32")

[1742] Output: The search request submitted

[1743] Specific operation: The user enters a query into the input form on the browser and clicks the search button to submit the request.

[1744] Step 7:

[1745] Submitting a Request

[1746] The terminal sends a request including the user's input to the server using the HTTP protocol.

[1747] Input: User query

[1748] Output: Request sent to the server

[1749] Specific operation: The device sends the entered query to the server as an HTTP request.

[1750] Step 8:

[1751] Query extraction and database search

[1752] The server extracts the query from the received request and searches the database.

[1753] Input: The request sent

[1754] Output: Legal data retrieved from the database

[1755] What happens: The server parses the request, gets the query, and then executes the query against the database.

[1756] Step 9:

[1757] Data Acquisition and Summarization

[1758] The server retrieves legal data that matches the query from the database.

[1759] Input: The database query that was executed

[1760] Output: Retrieved legal data

[1761] Specific operation: The server retrieves the relevant legal data from the database and summarizes it using a natural language processing engine.

[1762] Step 10:

[1763] Generating and Sending a Response

[1764] The server generates the generated summary data as a response and transmits it to the terminal.

[1765] Input: Obtained legal data

[1766] Output: A response containing summarized statute data

[1767] Specific operation: The server generates summarized legal data as an HTTP response and sends it to the terminal.

[1768] Step 11:

[1769] Displaying search results

[1770] The terminal displays the summary data received from the server to the user.

[1771] Input: The response sent by the server

[1772] Output: Summary data displayed to the user

[1773] Specific operation: The device displays the received summary data in the browser in an appropriate format.

[1774] Step 12:

[1775] User Emotion Recognition

[1776] The server uses an emotion recognition engine to recognize emotions from the user's input query and operation data.

[1777] Input: User query and operation data

[1778] Output: User's emotional state

[1779] Specific operation: The server inputs the query and operation log into the emotion recognition engine and analyzes the user's emotional state.

[1780] Step 13:

[1781] Emotion-based responses

[1782] The server tailors the information it provides based on the user's emotional state, adding support information and relevant helpline information as needed.

[1783] Input: User's emotional state

[1784] Output: Adjusted information

[1785] Specific operation: The server takes into account the emotion recognition results and generates a response including additional support information and FAQs, which it then sends to the device.

[1786] The above is the specific processing flow of this system.

[1787] (Application example 2)

[1788] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1789] Conventional security information systems manually retrieve and analyze information from official websites, making it difficult to quickly obtain and provide the latest information to users. Furthermore, they do not provide appropriate information based on the user's emotions or psychological state, which can lead to anxiety and stress. To address this issue, there is a demand for a system that allows users to quickly obtain appropriate security information with peace of mind.

[1790] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1791] In this invention, the server includes means for acquiring data from official websites, means for analyzing the acquired data to extract text data, means for analyzing the extracted text data using natural language processing to construct legal data, and means for recognizing user emotions and providing related information, thereby enabling users to quickly acquire the latest and most relevant security information with peace of mind.

[1792] An "official website" is a website officially operated by a government agency, public organization, or other entity with the purpose of providing reliable information.

[1793] A "means for obtaining data" is a means that has the function of sending an HTTP request and receiving data such as HTML content from a specified URL.

[1794] "Means for analyzing data and extracting text data" refers to the use of scraping or text analysis techniques to filter unnecessary information from the acquired data and extract the necessary text data.

[1795] "Means of analyzing using natural language processing and constructing legal data" refers to a means of using a natural language processing engine to analyze the titles, articles, clause numbers, related links, etc. of legal data from text data to generate legal data.

[1796] "Means for recognizing a user's emotions and providing related information" refers to means that uses an emotion recognition engine to analyze emotions from the user's input and operations and provide content and support information that is adapted to those emotions.

[1797] "Means of storing in a database" refers to the use of technologies and tools to structure the analyzed legal data and store it in a database.

[1798] The "means for receiving a query and searching for relevant legal data" refers to a means for receiving a search request from a user, searching the database based on that request, and obtaining relevant legal data.

[1799] "Means for simplifying and summarizing using natural language processing" refers to a means of concisely summarizing acquired legal data using a natural language processing engine and providing it in summary form.

[1800] "Means for providing to the user" refers to means including interfaces and UI components for conveying search results and summary information to the user.

[1801] This invention is a system that automatically acquires data from official websites, analyzes, stores, and searches the data, and combines it with an emotion engine that recognizes user emotions. The system mainly includes a server, a terminal, and a user. The server is primarily responsible for data acquisition, analysis, storage, search, simplification, and emotion recognition, while the terminal provides the interface with the user.

[1802] Data acquisition and analysis

[1803] The server has a list of URLs for reliable official websites. Based on this list, it periodically sends HTTP requests to each site to obtain their HTML content. The obtained HTML content is saved in local storage, and the saved HTML content is parsed to extract the required text data. This step uses a scraping library (e.g., BeautifulSoup, Scrapy).

[1804] The text data is input into a natural language processing engine (e.g., TextBlob, spaCy) and constructed as legal data, which identifies titles, articles, clause numbers, links to related laws, etc.

[1805] Data storage

[1806] The server stores the analyzed legal data in a database whose schema is designed to comprehensively store information such as titles, articles, clause numbers, and links to related laws and regulations.

[1807] Processing user requests

[1808] A user uses a browser on their device to enter information about a specific law into a search form. For example, they might enter "Article 32 of the Labor Standards Act." The device then sends a request containing the user's input to the server using the HTTP protocol. The server then extracts the query from the received request and searches its database.

[1809] Simplifying and providing search results

[1810] The server retrieves legal data that matches the query from the database. For example, it retrieves the content of "Article 32 of the Labor Standards Act." The server then uses a natural language processing engine to simplify and summarize the search results. For example, it may summarize the results as "Article 32 of the Labor Standards Act includes a provision that working hours not exceed eight hours per day." The server then generates a response containing the summarized legal data and sends it to the terminal. The terminal then displays the search results received from the server to the user. The summarized content is displayed in an intuitive and easy-to-understand format.

[1811] emotion recognition

[1812] The server uses an emotion engine to recognize emotions from the user's input queries and operations. Based on the user's emotions recognized by the emotion engine (e.g., IBM Watson Tone Analyzer, Microsoft Azure Emotion API), measures such as making the summary of the legal text more concise and displaying additional figures and tables may be taken if the user is feeling stressed. Furthermore, if the user's emotions are recognized as negative, additional support information, FAQs, and even information on relevant consultation centers may be provided to help the user understand the relevant laws and regulations.

[1813] Specific examples

[1814] For example, if a user inputs a request such as "I want to know about Article 32 of the Labor Standards Act" and clicks the search button, the following specific process takes place: The user searches for "Article 32 of the Labor Standards Act" on their device and sends a request. The server receives the request and retrieves data related to "Article 32 of the Labor Standards Act" from a database. The server simplifies the retrieved data and creates a summary. The server recognizes the user's emotions, and if it determines that the user is feeling anxious, for example, it adds supporting information for interpretation. The server returns the summary and supporting information to the device, which displays it to the user.

[1815] This allows users to quickly and easily understand the content of the legislation and receive additional support if needed.

[1816] Prompt Sentence Examples

[1817] "Please briefly explain the latest security alerts. Use emotion recognition to provide a clear, concise explanation that will not alarm users and include any necessary countermeasures."

[1818] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1819] Step 1:

[1820] The server retrieves the data from the official website.

[1821] Input: Official website URL list.

[1822] How it works: The server periodically sends HTTP requests to retrieve HTML content.

[1823] Output: The retrieved HTML content.

[1824] Step 2:

[1825] The server analyzes the acquired data and extracts text data.

[1826] Input: The retrieved HTML content.

[1827] What it does: Use a scraping library such as BeautifulSoup to extract the required text data from the HTML content.

[1828] Output: The extracted text data.

[1829] Step 3:

[1830] The server analyzes the extracted text data using natural language processing and constructs legal data.

[1831] Input: Extracted text data.

[1832] How it works: It uses a natural language processing engine such as TextBlob or spaCy to parse the text data and construct the statute title, article, clause number, related links, etc.

[1833] Output: Constructed legal data.

[1834] Step 4:

[1835] The server stores the analyzed legal data in a database.

[1836] Input: Constructed legal data.

[1837] What it does: Executes SQL queries to a database and stores legal data.

[1838] Output: Legislation data stored in a database.

[1839] Step 5:

[1840] The user uses the device's browser to search for information about a particular law.

[1841] Input: User's search query (e.g., "Labor Standards Act Article 32").

[1842] What happens: A user fills out a search form and clicks the search button.

[1843] Output: A search request is sent to the server.

[1844] Step 6:

[1845] The server receives queries from users and retrieves relevant legal data.

[1846] Input: The user's search query.

[1847] How it works: Runs a search query against the database to retrieve relevant legal data.

[1848] Output: Retrieved legal data.

[1849] Step 7:

[1850] The server simplifies and summarizes the search results using natural language processing.

[1851] Input: Retrieved legal data.

[1852] How it works: Uses a natural language processing engine to concisely summarize legal data.

[1853] Output: Simplified and summarized statutory data.

[1854] Step 8:

[1855] The server recognizes emotions from the user's input queries and operations.

[1856] Input: User search queries and operation logs.

[1857] How it works: Analyzes the user's emotions using an emotion engine (e.g., IBM Watson Tone Analyzer).

[1858] Output: User's emotion evaluation result.

[1859] Step 9:

[1860] The server provides relevant information based on the user's emotions.

[1861] Input: User's emotion evaluation result.

[1862] What it does: Add support information, FAQs, and helpline information that adapts to the user's emotions.

[1863] Output: Response data including auxiliary information.

[1864] Step 10:

[1865] The server provides simplified and summarized search results to the user.

[1866] Input: Simplified and summarized statutory data, and response data including supporting information.

[1867] Operation: Generates response data and sends it to the device.

[1868] Output: Simplified and summarized statutory data and supporting information displayed to the user.

[1869] Step 11:

[1870] The terminal displays the search results received from the server to the user.

[1871] Input: Response data from the server.

[1872] What it does: Visually displays the data received by the device.

[1873] Output: Search results and supporting information displayed to the user.

[1874] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1875] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1876] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1877] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1878] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1879] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1880] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1881] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1882] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1883] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1884] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1885] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1886] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1887] 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.

[1888] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1889] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1890] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1891] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1892] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1893] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1894] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1895] The following is further disclosed regarding the above embodiment.

[1896] (Claim 1)

[1897] A means of obtaining data from the official website;

[1898] A means for analyzing the acquired data and extracting text data;

[1899] A means for analyzing the extracted text data using natural language processing and constructing legal data;

[1900] a means for storing the analyzed legal data in a database;

[1901] means for receiving a query from a user and retrieving relevant legal data;

[1902] A means of simplifying and summarizing search results using natural language processing;

[1903] A means for providing simplified and summarized search results to users;

[1904] A system including:

[1905] (Claim 2)

[1906] 10. The system of claim 1, further comprising: means for periodically crawling the official website to obtain updated data.

[1907] (Claim 3)

[1908] 2. The system according to claim 1, further comprising means for automatically constructing link relationships between the analyzed legal data.

[1909] "Example 1"

[1910] (Claim 1)

[1911] A means of obtaining data from the official website;

[1912] A means for analyzing the acquired data and extracting text data;

[1913] A means for analyzing the extracted text data using natural language processing and constructing legal data;

[1914] means for storing the analyzed legal data in a storage device;

[1915] means for receiving a query from a user and retrieving relevant legal data;

[1916] A means of simplifying and summarizing search results using a generative AI model;

[1917] a means for generating prompt sentences and inputting the prompt sentences to a generative AI model to generate summarized search results;

[1918] A means for providing simplified and summarized search results to users;

[1919] A system including:

[1920] (Claim 2)

[1921] 10. The system of claim 1, further comprising: means for periodically crawling the official website to obtain updated data.

[1922] (Claim 3)

[1923] 2. The system according to claim 1, further comprising means for automatically constructing link relationships between the analyzed legal data.

[1924] "Application Example 1"

[1925] (Claim 1)

[1926] A means of obtaining data from official information disclosure sites,

[1927] A means for analyzing the acquired data and extracting text data;

[1928] A means for analyzing the extracted text data using natural language processing and constructing information data;

[1929] A means for storing the analyzed information data in a database;

[1930] means for receiving a query from a user and retrieving relevant information data;

[1931] A means of simplifying and summarizing search results using natural language processing;

[1932] A means for providing simplified and summarized search results to users;

[1933] a means for using a generative AI model to generate a summary of the information data;

[1934] A system including:

[1935] (Claim 2)

[1936] 10. The system of claim 1, further comprising: means for periodically crawling official information publication sites to obtain updated data.

[1937] (Claim 3)

[1938] 10. The system of claim 1, further comprising means for automatically building relationships between the analyzed information data.

[1939] "Example 2: Combining Emotion Engines"

[1940] ---

[1941] (Claim 1)

[1942] A means of obtaining data from the official website;

[1943] A means for analyzing the acquired data and extracting text data;

[1944] A means for analyzing the extracted text data using natural language processing and constructing legal data;

[1945] a means for storing the analyzed legal data in a database;

[1946] means for receiving a query from a user and retrieving relevant legal data;

[1947] A means of simplifying and summarizing search results using natural language processing;

[1948] a means for providing search results and simplified / summarized results to the user;

[1949] Equipped with an emotion recognition engine that recognizes the user's emotions from input queries and operations,

[1950] A means for adding or modifying information to be provided according to the user's emotions;

[1951] A system including:

[1952] (Claim 2)

[1953] 10. The system of claim 1, further comprising: means for periodically crawling the official website to obtain updated data.

[1954] (Claim 3)

[1955] 2. The system according to claim 1, further comprising means for automatically constructing link relationships between the analyzed legal data.

[1956] "Application example 2 when combining emotion engines"

[1957] (Claim 1)

[1958] A means of obtaining data from the official website;

[1959] A means for analyzing the acquired data and extracting text data;

[1960] A means for analyzing the extracted text data using natural language processing to construct legal data, a means for recognizing the user's emotions and providing related information,

[1961] a means for storing the analyzed legal data in a database;

[1962] means for receiving a query from a user and retrieving relevant legal data;

[1963] A means of simplifying and summarizing search results using natural language processing;

[1964] A means for providing simplified and summarized search results to users;

[1965] A system including:

[1966] (Claim 2)

[1967] 10. The system of claim 1, further comprising: means for periodically crawling the official website to obtain updated data.

[1968] (Claim 3)

[1969] 2. The system according to claim 1, further comprising means for automatically constructing link relationships between the analyzed legal data. [Explanation of symbols]

[1970] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining data from the official website; A means for analyzing the acquired data and extracting text data; A means for analyzing the extracted text data using natural language processing and constructing legal data; a means for storing the analyzed legal data in a database; means for receiving a query from a user and retrieving relevant legal data; A means of simplifying and summarizing search results using natural language processing; a means for providing simplified and summarized search results to users; A system including:

2. The system of claim 1 , further comprising: means for periodically crawling the official website to obtain updated data.

3. 2. The system according to claim 1, further comprising means for automatically constructing link relationships between the analyzed legal data.

Images