System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting data from a corporate or organization's official website, news sources, and industry databases of interest; means for analyzing the collected text data using natural language processing techniques; means for summarizing the analyzed data into a pre-defined format; means for storing the summarized data in a database and making it accessible to users; means for users to search for information on a particular company or trade through a user interface; and means for displaying search results to the user's terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In sales, it is important to efficiently collect and organize information such as company profiles and transaction overviews when creating account plans and conducting reviews for top sales negotiations, but this can be problematic as it can require a lot of time and effort. Traditional methods require time-consuming research and manual data organization, which is inefficient and consumes a significant amount of human resources. This problem reduces the efficiency of sales activities and reduces competitiveness. [Means for solving the problem]

[0005] This invention provides a means to automatically collect data from official websites, news sources, and industry databases of target companies and organizations, and analyze that data using natural language processing (NLP) technology. It also provides a means to summarize the analyzed data in a predefined format and store it in a database. It also provides a means for users to search and display information about specific companies and transactions through a user interface. This significantly reduces the amount of work required for traditional manual data organization, improving the efficiency of sales activities.

[0006] "Data collection methods" are methods used to automatically collect information about the target company or organization from external resources such as official websites, news sources, and industry databases.

[0007] "Natural language processing (NLP) technology" is a technology that analyzes collected text data and performs a series of processes such as tokenizing sentences and words, tagging parts of speech, and recognizing entities.

[0008] The "summary generation means" is a means for automatically generating a summary based on the analyzed data in accordance with a predefined format.

[0009] "Database storage means" means a means for storing summarized data in a database in a structured format (e.g., JSON or XML format).

[0010] "User Interface" means the interface provided by a web or mobile application that enables a user to search for and request information about a particular company or transaction.

[0011] A "search means" is a means for searching for relevant information from the stored summary data based on a search query entered through a user interface.

[0012] The "display means" is a means for visually displaying the search results on the user's terminal. [Brief explanation of the drawings]

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

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

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

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

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

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

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0034] The present invention provides an automated information summarization system that collects data from official websites of target companies and organizations, news sources, and industry databases, analyzes and summarizes the data, and makes it available to users efficiently. The following means and methods are included to implement this system:

[0035] Explanation of program processing

[0036] 1. Data Collection

[0037] The server sets up URLs to access the target company's official website, news articles, and industry databases.

[0038] The server crawls each URL and retrieves the data in HTML or PDF format.

[0039] The server uses scraping technology to extract necessary information from web pages and documents, such as company profiles, financial information, and press releases.

[0040] 2. Data Analysis

[0041] The server analyzes the collected data using natural language processing (NLP) technology.

[0042] The server tokenizes the text, tags it with parts of speech, and uses Named Entity Recognition (NER) to identify important entities such as company names, people's names, and dates.

[0043] The server uses relational modeling to recognize the relationships between the identified entities.

[0044] 3. Summary Generation

[0045] Based on the analysis results, the server summarizes the company profile and transaction overview in a predefined format.

[0046] The server stores the generated summary as structured data (JSON or XML format).

[0047] 4. User Interface

[0048] The terminal provides a web or mobile application interface that allows users to input and search information about specific companies or transactions.

[0049] A user enters a search query through the interface to request information.

[0050] The server searches the database based on the input query and retrieves the relevant summary information.

[0051] The terminal displays the acquired summary information to the user in a visually viewable format.

[0052] Specific examples

[0053] 1. Data Collection

[0054] The server collects data from the official Temple Co., Ltd. website, industry news sites, and industry-specific databases.

[0055] The server extracts "Temple's latest press release" through HTML analysis and extracts "2023 Medium-Term Management Plan" using PDF analysis.

[0056] 2. Data Analysis

[0057] The server tokenizes the extracted text data and tags each word with a part of speech tag.

[0058] The server uses NER to identify entities such as "Temple," "AI technology," and "investment amount" and analyze the relationships between them.

[0059] 3. Summary Generation

[0060] The server generates the following summary based on the analysis results:

[0061] Company Overview: Temple is primarily engaged in software development and has approximately 1,500 employees.

[0062] Transaction Overview: In its medium-term management plan for fiscal 2023, the company announced that it will significantly expand investment in AI technology.

[0063] 4. User Interface

[0064] A user enters a search query: "Show me Temple's trading summary."

[0065] The server searches the abstract database for the latest information about "Temple Company" and transmits the retrieved data to the terminal.

[0066] The terminal displays the acquired summary information in a card format on the user interface, allowing the user to easily view it.

[0067] This system allows users to quickly obtain high-quality corporate information, enabling them to efficiently create account plans and prepare for business negotiations.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] The server prepares a list of URLs for the official websites of the companies from which data is collected, as well as URLs for news sources and industry databases.

[0071] Step 2:

[0072] The server accesses websites and news sites according to the URL list, using a crawler to retrieve HTML and PDF data.

[0073] Step 3:

[0074] The server scrapes the acquired HTML and PDF data, extracting necessary information such as company profiles, press releases, and financial information.

[0075] Step 4:

[0076] The server uses natural language processing (NLP) technology to analyze the extracted text data, specifically tokenizing and tagging parts of speech.

[0077] Step 5:

[0078] The server uses Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates.

[0079] Step 6:

[0080] The server uses relational modeling to analyze the relationships between the identified entities, for example, recognizing the relationship "Company A invests in AI technology."

[0081] Step 7:

[0082] The server generates summaries based on the analysis results in predefined formats, such as company overviews, transaction summaries, etc.

[0083] Step 8:

[0084] The server stores the generated summaries in a database in JSON or XML format, with a timestamp attached to the data, allowing users to distinguish between new and old information.

[0085] Step 9:

[0086] The device provides an interface for web and mobile applications that allow users to enter and search for information.

[0087] Step 10:

[0088] A user searches for information about a particular company or transaction through a terminal interface.

[0089] Step 11:

[0090] The device sends the search query entered by the user to the server, using an API request to send the query.

[0091] Step 12:

[0092] The server retrieves relevant abstract information from a database based on the received search query, and retrieves the most relevant and up-to-date abstract information.

[0093] Step 13:

[0094] The server sends a summary of the search results to the device, often in the form of a JSON response.

[0095] Step 14:

[0096] The terminal displays the received summary information on a user interface in a visually easy-to-read format, such as a card format or a table format.

[0097] Example 1

[0098] 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."

[0099] With the advancement of the information society, a huge amount of data is being generated from official websites of companies and organizations, news sources, and industry databases. However, there is a lack of an automated system to efficiently collect, analyze, summarize, and provide this data. Because accessing the information many users need is cumbersome, there is a need to obtain high-quality information quickly.

[0100] 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.

[0101] In this invention, the server includes means for collecting data from official websites, news platforms, and industry databases of target organizations and groups, means for using natural language processing (NLP) techniques to analyze the collected data, means for summarizing the analyzed data in a predefined format, means for storing the summarized data in a database and making it accessible to users, means for users to search for information on specific organizations and transactions through a user interface, and means for displaying search results on the user's terminal, thereby enabling efficient collection of large amounts of data and rapid provision of high-quality summarized information.

[0102] "Covered organizations and entities" refers to corporations, non-profit organizations, and other official entities that collect and analyze data.

[0103] "Official Website" refers to the online informational webpage officially operated by the organization or entity in question.

[0104] "News Platform" refers to an online platform operated by a news provider or media company to distribute the latest news and articles.

[0105] "Industry Database" refers to an online or offline information management system that collects, stores, and provides detailed data and information about a particular industry.

[0106] "Data collection methods" refers to the programs or technologies used to obtain information from the target website or database.

[0107] "Natural language processing (NLP) technology" refers to artificial intelligence technology that enables computers to understand, interpret, and generate human language.

[0108] "Means for analyzing data" refers to the programs and algorithms used to convert collected data into information and extract meaning.

[0109] "Summarization tools" refer to programs or technologies that extract important information from the analyzed data and summarize it concisely.

[0110] A "database" refers to a collection of information that is structured so that data can be efficiently managed and searched.

[0111] "User interface" refers to the screens and applications through which a user interacts with a system and inputs or obtains information.

[0112] "Information about a specific entity or transaction" refers to detailed data, news, and other relevant information about a specific entity or transaction.

[0113] "Terminal" refers to a device such as a computer, smartphone, or tablet that a user uses to access the system.

[0114] This invention is an automatic information summarization system that automatically collects data from official websites of target organizations and groups, news platforms, and industry databases, analyzes the data, generates summaries, and makes them available to users efficiently. To implement this system, the following hardware and software are used.

[0115] Hardware used

[0116] 1. Server: A central computer with high computing power and large storage capacity for collecting, analyzing, summarizing, and storing data.

[0117] 2. Device: The device a user uses to enter information and receive search results. This can include a computer, smartphone, tablet, etc.

[0118] Software used

[0119] 1. Crawl program: Software used to automatically collect data from target websites and databases.

[0120] 2. Natural Language Processing (NLP) libraries: For example, use Python's "spaCy" or "NLTK" to analyze the collected text data.

[0121] 3. Database management system: A system for storing and managing collected and analyzed data. Examples include MySQL (registered trademark) and MongoDB.

[0122] 4. User interface platform: A framework for building web and mobile applications. Examples include "React" and "Flutter (registered trademark)."

[0123] System processing procedure

[0124] Data collection

[0125] The server sets the URLs to access the official websites, news platforms, and industry databases of the target organizations and groups. It then crawls these URLs and retrieves data in HTML or PDF format. For example, the server sets the URL "https: / / example.com / company" to collect data on the relevant companies.

[0126] Data Preprocessing

[0127] The server parses the collected HTML and PDF documents and imports the necessary information into the database. For example, it extracts the "About Us" section through HTML parsing, converts it to JSON format, and stores it. It also performs data cleaning to remove invalid data and noise.

[0128] Data analysis

[0129] The server uses natural language processing (NLP) techniques to analyze the collected text data. For example, it uses the Python library "spaCy" to tokenize the text and tag parts of speech. It also uses Named Entity Recognition (NER) to identify important entities such as company names, people's names, and dates, and uses relational modeling to recognize relationships between the identified entities.

[0130] Summary Generation

[0131] Based on the analysis results, the server summarizes the company profile and transaction overview in a predefined format. For example, it generates a summary such as "Company profile: The target company is primarily engaged in software development, with approximately 1,500 employees." It then saves the summary in JSON or XML format.

[0132] Providing information

[0133] The terminal provides an interface that allows users to input and search for information about specific organizations or transactions. Users input search queries through the interface to request information, such as "Please show me an overview of the transactions of the target company." The server retrieves the relevant information from the summary database and sends it to the terminal. The terminal then displays the retrieved information to the user in a visually viewable format.

[0134] Usage example

[0135] Examples of prompts to input into the generative AI model include:

[0136] 1. "Please provide the latest financial information for the target company."

[0137] 2. "I would like to know about the medium-term management plan for fiscal 2023."

[0138] 3. "What are your investment plans for AI technology?"

[0139] This allows users to quickly obtain high-quality information and improve work efficiency.

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Step 1: Data collection

[0142] The server sets up access URLs to the target organization's official website, news platform, and industry database.

[0143] Input: URL list, e.g. "https: / / example.com / company"

[0144] Output: URLs of websites and databases to be collected

[0145] Specific operation: The server accesses the specified URL and downloads the web page or PDF document.

[0146] Step 2: Data extraction

[0147] The server parses the collected HTML and PDF documents and extracts the necessary information.

[0148] Input: Collected HTML and PDF data

[0149] Output: Extracted text data

[0150] What it does: The server uses HTML and PDF parsing tools to extract specific information in text format, such as the "About Us" and "Press Releases" sections.

[0151] Step 3: Data cleaning

[0152] The server cleans the extracted data to remove invalid data and noise.

[0153] Input: Extracted text data

[0154] Output: Cleaned data

[0155] What it does: The server uses regular expressions and machine learning models to filter out ads and irrelevant text.

[0156] Step 4: Data analysis

[0157] The server analyzes the cleaned data using natural language processing (NLP) techniques.

[0158] Input: Cleaned data

[0159] Output: Parsed text data

[0160] What it does: The server uses an NLP library (e.g., spaCy) to tokenize the text, tag it as a part of speech, and use Named Entity Recognition (NER) to identify significant entities.

[0161] Step 5: Relational Modeling

[0162] The server performs relational modeling based on the analyzed data and recognizes the relationships between entities.

[0163] Input: Parsed text data and entities

[0164] Output: Relationships between entities

[0165] What happens: The server models transitive relationships and identifies relationships such as "Organization A started project X in 2023."

[0166] Step 6: Summary generation

[0167] The server generates a summary in a predefined format based on the results of the relational modeling.

[0168] Input: Entities and their relationships

[0169] Output: Summary

[0170] Specific operation: The server uses a summary generation algorithm to generate a summary sentence such as, "Organization A announced investment in AI technology in its 2032 mid-term plan."

[0171] Step 7: Save Data

[0172] The server stores the generated summaries in a database in JSON or XML format.

[0173] Input: Generated summary

[0174] Output: Summary data stored in a database

[0175] Specific operation: The server uses a database management system (e.g., MySQL or MongoDB) to store the summary data in an appropriate format.

[0176] Step 8: Information Search

[0177] The terminal provides an interface for the user to search for information.

[0178] Input: User's search query

[0179] Output: Display of the user interface

[0180] What it does: The device provides a search box and filters through the web or mobile application.

[0181] Step 9: Run a search

[0182] The server searches the database based on the user's search query and retrieves the relevant summary information.

[0183] Input: User's search query

[0184] Output: Summary information obtained

[0185] Specific operation: The server extracts the relevant summary information from the database using an SQL query or the like.

[0186] Step 10: View the results

[0187] The terminal displays the retrieved summary information to the user in a visually viewable format.

[0188] Input: Retrieved summary information

[0189] Output: User-visible search results

[0190] What it does: The device displays search results in card or list format, allowing users to easily browse information.

[0191] As an example, when a user enters a prompt such as "Please provide the latest financial information of the target company," the system automatically performs a series of processes from data collection to displaying the results, and provides the user with the relevant information.

[0192] (Application example 1)

[0193] 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."

[0194] In recent years, with the rapid release of large amounts of information about security risks and vulnerabilities, it has become increasingly important for organizational and individual security personnel to efficiently collect, analyze, and implement appropriate countermeasures. However, manually collecting and analyzing this information requires a huge amount of time and effort, making it difficult to respond in a timely manner. Therefore, there is a need for a system that can collect and analyze security information in real time and provide summaries.

[0195] 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.

[0196] In this invention, the server includes means for collecting data from official websites of target organizations and groups, news sources, and industry databases, means for analyzing the collected text data using natural language processing (NLP) technology, means for summarizing the analyzed data in a predefined format, means for storing the summarized data in a database and making it accessible to users, means for users to search for information about specific organizations and transactions through a user interface, means for displaying search results on the user's terminal, and means for collecting and analyzing security information and providing summaries in real time, thereby enabling security personnel to quickly obtain high-quality security information and take appropriate measures.

[0197] "Organizations and groups" refers to companies, public institutions, non-profit organizations, etc. of various sizes and types.

[0198] "Official website" refers to an information dissemination page on the Internet that is officially operated and provided by an organization or group.

[0199] "News source" refers to the source of news articles and news programs provided by newspapers, news sites, television stations, etc.

[0200] "Industry Database" means a system or service that collects and stores data and information related to a particular industry.

[0201] "Means of collecting data" refers to technologies and devices used to obtain information, such as web scraping and API access.

[0202] "Natural Language Processing (NLP)" refers to the science and technology for analyzing, understanding, and generating human language.

[0203] "Means of analysis" refers to the software or algorithms used to analyze the collected text data.

[0204] "Summarization tools" refers to techniques or devices used to summarize key points from collected and analyzed data.

[0205] "Database" refers to a system for efficiently storing, retrieving, updating, and managing data.

[0206] "User interface" refers to the screens and applications through which users enter information and receive search results.

[0207] "User terminal" refers to a device such as a desktop computer, laptop, tablet, or smartphone that a user uses to connect to the Internet and obtain information.

[0208] "Security information" refers to information regarding vulnerabilities, threats, and risks of systems and networks.

[0209] "Real-time" refers to processing and providing information with extremely short delays.

[0210] This invention is a system for quickly collecting, analyzing, summarizing, and providing security-related information to users. This system is centered around a server and uses the following hardware and software:

[0211] Program Overview

[0212] 1. Data Collection

[0213] The server accesses the official websites of the organizations and organisations in question, news sources, and industry databases to retrieve information, using the Requests library to retrieve information from the network and BeautifulSoup to parse the HTML.

[0214] 2. Data Analysis

[0215] The server analyzes the collected text data using natural language processing (NLP) techniques, tokenizing the text data and extracting entities using the SpaCy library, and summarizing the text using Transformers models.

[0216] 3. Data Summary

[0217] Based on the analysis results, the server summarizes the information in a predefined format, and the summarized data is stored in a database in JSON or XML format.

[0218] 4. Information provision

[0219] The user's device can search for information about a specific organization or transaction through a user interface. When the user enters a search query, the server retrieves relevant summary data from the database and sends it to the device.

[0220] Specific example explanation

[0221] For example, if a user inputs a prompt such as "Show me information about the latest security risks," the server will generate a summary of the latest vulnerabilities and risks from the collected security information and display it on the terminal.

[0222] Hardware and Software Used

[0223] Server: A central device with high-performance processing power for data collection and analysis.

[0224] Requests library: Used to retrieve data from the web.

[0225] BeautifulSoup: Used for HTML parsing.

[0226] SpaCy library: Used for natural language processing.

[0227] Transformers library: Used for generating summaries.

[0228] Database: A system that stores data in JSON or XML format.

[0229] User devices: desktop computers, laptops, tablets, smartphones.

[0230] Prompt Sentence Examples

[0231] "Show me information about the latest security risks"

[0232] "Give me a summary of the latest vulnerability news."

[0233] By using this system, security personnel can quickly obtain high-quality information and take countermeasures efficiently.

[0234] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0235] Step 1:

[0236] The server collects data from official websites of organizations and groups of interest, news sources, and industry databases. Specifically, it uses the Requests library to access these sources and retrieve data in HTML or text format. The input to this step is a list of URLs for each source, and the output is the raw data retrieved.

[0237] Step 2:

[0238] The server uses BeautifulSoup to parse the raw data and extract the necessary information. For example, it extracts the article title, publication date, and body of the article through HTML parsing. The input to this step is the raw data obtained in step 1, and the output is the extracted structured data.

[0239] Step 3:

[0240] The server uses natural language processing (NLP) techniques to parse the extracted structured data. Specifically, it uses the SpaCy library to tokenize the text and extract entities (e.g., vulnerabilities, risks, organization names, etc.). The input of this step is the structured data obtained in step 2, and the output is the parsed data containing entity information.

[0241] Step 4:

[0242] The server uses Transformers to summarize the parsed data. For example, it extracts important parts from the original text and creates a short summary. The input to this step is the parsed data obtained in step 3, and the output is a summary sentence.

[0243] Step 5:

[0244] The server stores the abstract in a database in JSON or XML format. The input of this step is the abstract generated in step 4, and the output is the stored structured data.

[0245] Step 6:

[0246] A user utilizes a terminal to search for information about a particular organization or transaction through a user interface, for example, by entering a prompt phrase such as "Show me information about the latest security risks." The input to this step is the user's search query, and the output is a search request.

[0247] Step 7:

[0248] The server receives the search request and retrieves the relevant summary data from the database. The input to this step is the search query received in step 6, and the output is the search results.

[0249] Step 8:

[0250] The server sends the search results to the user's terminal. The input of this step is the search results obtained in step 7, and the output is the data sent to the terminal.

[0251] Step 9:

[0252] The terminal displays the received data on a user interface, for example visually displaying the summarized data in the form of cards. The input of this step is the data received in step 8, and the output is the displayed information.

[0253] 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.

[0254] The present invention relates to a system for collecting data related to a company or organization, analyzing and summarizing the data, and providing customized information by recognizing user emotions. The system includes a data collection means, a natural language processing (NLP) means, a summary generation means, a database storage means, a user interface, a search means, a display means, and an emotion engine.

[0255] Explanation of program processing

[0256] 1. Data Collection

[0257] The server prepares a list of URLs to access official company and organization websites, news sources, and industry databases.

[0258] The server crawls the URL list to obtain HTML and PDF data, and then uses scraping technology to extract necessary information such as company profiles, press releases, and financial information.

[0259] 2. Data Analysis

[0260] The server analyzes the collected data using natural language processing (NLP) techniques, tokenizing the text and tagging it by part of speech.

[0261] The server utilizes Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates.

[0262] The server uses relational modeling to analyze the relationships between entities.

[0263] 3. Summary Generation

[0264] The server generates a summary in a predefined format based on the analysis results, including a company profile and transaction overview.

[0265] The server stores the generated summaries in a database in JSON or XML format.

[0266] 4. User Interface

[0267] The device provides an interface through which the user can enter a search query, either through a web application or a mobile application.

[0268] A user uses the device interface to search for information about a particular company or transaction.

[0269] 5. Search and Display

[0270] The device sends the search query entered by the user to the server via an API request.

[0271] The server searches and retrieves relevant summary information from a database.

[0272] The terminal displays the search results on a user interface in a visually viewable format.

[0273] 6. Use of Emotion Engine

[0274] The server uses an emotion engine to parse emotions from the user's text input. For example, if the user inputs "I'm disappointed with my recent transaction," the emotion engine recognizes the emotion "disappointed."

[0275] The server provides search results and customized feedback that match the user's mood based on the recognized emotions.

[0276] Based on the results of the emotion engine, the device displays appropriate feedback, such as "We suggest improvements for this transaction."

[0277] Specific examples

[0278] 1. Data Collection

[0279] The server collects data from the official website of "Reisha Co., Ltd.", industry news sites, and industry-specific databases. The data includes official announcements, industry trend reports, etc.

[0280] 2. Data Analysis

[0281] The server tokenizes the collected text data, tags it with parts of speech, and uses NER to identify entities such as "example company," "AI technology," and "investment amount."

[0282] 3. Summary Generation

[0283] The server generates the following summary based on the analysis results:

[0284] Company profile: Reisha Co., Ltd. is primarily engaged in software development and has approximately 1,500 employees.

[0285] Transaction Overview: The latest mid-term plan calls for a significant increase in investment in AI technology.

[0286] 4. User Interface

[0287] A user enters a search query such as "I want to see an overview of transactions for example companies."

[0288] The server retrieves the latest information about the sample company from the summary database and transmits the retrieved data to the terminal.

[0289] The terminal displays the acquired summary information in a card format on the user interface.

[0290] 5. Use of Emotion Engine

[0291] The user enters a comment saying, "I am very satisfied with the company's recent performance."

[0292] The server uses an emotion engine to recognize the emotion "satisfaction" and provides the user with positive feedback such as "We'll also introduce you to some new investment opportunities that may interest you."

[0293] The device will then display appropriate feedback or additional information based on the recognized emotion.

[0294] This system not only enables users to quickly obtain high-quality corporate information, but also allows them to more efficiently gather information and prepare for business negotiations by receiving customized feedback tailored to their emotional state.

[0295] The processing flow will be explained below.

[0296] Step 1:

[0297] The server prepares a pre-configured list of URLs for official company websites, news sites, and industry databases to be collected.

[0298] Step 2:

[0299] The server sends an HTTP request to each website based on the URL list to retrieve HTML content or PDF files.

[0300] Step 3:

[0301] The server parses the HTML content and PDF files and uses scraping technology to extract important data such as company profiles, press releases, and financial information.

[0302] Step 4:

[0303] The server then uses natural language processing (NLP) technology to tokenize the extracted text data, breaking it down into sentences and words.

[0304] Step 5:

[0305] The server performs part-of-speech tagging on the tokenized data, which identifies the part of speech (noun, verb, adjective, etc.) of each word.

[0306] Step 6:

[0307] The server uses Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates.

[0308] Step 7:

[0309] The server uses relational modeling techniques to analyze the relationships between the identified entities, e.g., to clarify the relationship between Company A's investment in AI technology.

[0310] Step 8:

[0311] The server generates a summary based on the analysis results in a predefined format, which includes information such as company overview and transaction summary.

[0312] Step 9:

[0313] The server stores the generated summaries in a database in JSON or XML format, with a timestamp attached to distinguish between new and old data.

[0314] Step 10:

[0315] The terminal provides an interface through which the user enters a search query via a web application or a mobile application.

[0316] Step 11:

[0317] The user enters information about a particular company or transaction into the terminal interface and presses the search button.

[0318] Step 12:

[0319] The terminal sends the search query entered by the user to the server in the form of an API request.

[0320] Step 13:

[0321] The server retrieves relevant summary information from the database based on the received search query, retrieving the most recent and relevant data.

[0322] Step 14:

[0323] The server sends summary information of the search results in JSON format to the terminal.

[0324] Step 15:

[0325] The terminal visually displays the received summary information on the user interface in an easy-to-read format such as cards or tables.

[0326] Step 16:

[0327] The server analyzes the user's text input using an emotion engine that recognizes emotions (e.g., joy, sadness, disappointment, etc.) from the user's input text.

[0328] Step 17:

[0329] The server customizes search results and feedback based on the user's emotions, as recognized by the emotion engine. For example, if a user expresses "disappointment," the server suggests related topics and solutions.

[0330] Step 18:

[0331] The device presents appropriate feedback to the user based on the results of the emotion engine, allowing the user to receive information according to their emotions.

[0332] Example 2

[0333] 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."

[0334] Conventional corporate information gathering systems have made it difficult for users to quickly and accurately obtain the information they need. Furthermore, they lack the ability to analyze and summarize the collected information, and provide customized information based on the user's feelings, resulting in issues with the usefulness of the information and ease of access. As a result, efficiency in preparing for business negotiations and making decisions has decreased, and gathering information has taken a lot of time.

[0335] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0336] In this invention, the server includes: means for collecting data from official websites, information sources, and industry databases of target organizations; means for analyzing the collected text data using natural language processing technology; means for summarizing the analyzed data in a predefined format; means for storing the summarized data in a database and making it accessible to users; means for users to search for information on specific organizations or transactions through a user interface; means for displaying search results on the user's terminal; and means including an emotion engine for analyzing user emotions from text input and providing customized information. This allows users to not only quickly obtain high-quality corporate information but also receive customized feedback tailored to their emotional state.

[0337] "Covered Entity" means the company, association, or other legal entity identified as the subject of data collection, analysis, or abstraction.

[0338] "Sources" refers to websites and databases that contain information related to an organization, such as news sites, industry reports, and social media.

[0339] "Industry database" refers to an online database that centrally manages and provides data related to a specific industry.

[0340] "Data collection methods" refers to the techniques and methods used to obtain the desired data from websites and databases, such as using web crawlers or API requests.

[0341] "Natural language processing technology" refers to machine learning algorithms and text analysis technologies for analyzing and understanding human language.

[0342] "Summarization means" refers to algorithms or methods for succinctly presenting collected and analyzed data as a summary statement.

[0343] "Means of database storage" refers to the techniques and methods used to store data in a structured format in a database to facilitate search and retrieval.

[0344] "User Interface" refers to the interactive screen or application through which a user interacts with a system, inputs information, and obtains results.

[0345] "Terminal" refers to a device such as a computer or smartphone that allows a user to input data and display results.

[0346] An "emotion engine" refers to technology or algorithms that analyze a user's emotional state from text and provide appropriate feedback based on the results.

[0347] "Relational modeling technology" refers to a data analysis technique for analyzing the relationships between entities and visualizing them as a network.

[0348] "JSON format" is an abbreviation for JavaScript (registered trademark) Object Notation, and refers to a lightweight data exchange format for structuring and storing data.

[0349] "XML format" is an abbreviation for eXtensible Markup Language, and refers to a general-purpose markup language for hierarchically structuring and storing data.

[0350] The present invention is a system that collects information about organizations and groups, analyzes and summarizes it, and further recognizes the user's emotions to provide customized information. This system consists of a server, a terminal, and a user. The roles and operations of each component are described in detail below.

[0351] server

[0352] 1. Data Collection

[0353] The server collects data from official websites, information sources, and industry databases. Specifically, it uses a web crawler such as Selenium to access a specified list of URLs and retrieves data in HTML or PDF format. From the retrieved data, it extracts necessary information such as company profiles, press releases, and financial information using tools such as BeautifulSoup and PyPDF2.

[0354] 2. Data Analysis

[0355] The server analyzes the collected text data using natural language processing techniques. For example, it uses libraries such as spaCy and NLTK to tokenize the text data and tag parts of speech. It also uses Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates. It then analyzes the relationships between entities using relational modeling tools such as NetworkX.

[0356] 3. Summary Generation

[0357] The server generates a summary in a predefined format based on the analysis results. This summary includes a company profile and a business overview. For example, it generates a summary statement in the following format: "Reisha Co., Ltd. is primarily engaged in software development and has approximately 1,500 employees. Its latest medium-term plan calls for a significant increase in investment in AI technology." The generated summary is stored in a database in JSON or XML format.

[0358] 4. Use of Emotion Engine

[0359] The server uses an emotion engine to analyze emotions from the user's text input. For example, if a user enters a comment such as "I'm very satisfied with the recent performance of the company," the emotion engine recognizes the emotion as "satisfied." Based on the results, the server generates customized feedback and provides it in the form of, for example, "We'll also introduce you to some new investment opportunities that may be of interest to you."

[0360] Terminal

[0361] 1. Interface provision

[0362] The terminal provides an interface, including a web application or a mobile application, through which a user can enter a search query. For example, the terminal may provide an interface through which a user can enter a query such as "Show me a trading summary for company X."

[0363] 2. Search and Display

[0364] The device sends the search query entered by the user to the server and visually displays the search results received from the server. The search results are displayed in a card format, providing a concise and visually easy-to-understand format.

[0365] User

[0366] 1. Information Search

[0367] Users use the device interface to search for information about a particular organization or transaction, for example, by entering a search query such as "Show me an overview of transactions for this company."

[0368] Specific examples

[0369] The server collects data from the official website of "Example Company," industry news sites, and industry-specific databases. For example, it obtains and analyzes data such as official announcements and industry trend reports to generate a summary such as "Example Company announced plans to invest 50 billion yen in AI technology." If a user inputs "I would like to see an overview of Example Company's transactions," this summary is displayed on the device. Furthermore, if a user inputs a comment such as "I am very satisfied with Example Company's recent performance," the emotion engine recognizes that emotion and provides feedback such as "I would also like to introduce some new investment opportunities that may be of interest to you."

[0370] Example prompt for a generative AI model:

[0371] Could you please provide the latest trading information for Example Co., Ltd.? Also, could you please provide a system that analyzes the sentiment of user reviews about recent business performance and provides positive feedback?

[0372] In this way, the present invention enables the rapid acquisition of high-quality corporate information and improves the efficiency of information gathering and decision-making by providing customized feedback that is tailored to the user's emotions.

[0373] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0374] Step 1:

[0375] Data collection

[0376] 1. The server prepares a list of URLs to collect data from official websites of organizations, information sources, and industry databases. The input is the URL list, and the server sets up a web crawler based on it.

[0377] 2. The server launches a web crawler using Selenium to retrieve data in HTML or PDF format from the specified URL list, and the output is the retrieved raw web data.

[0378] 3. The server uses BeautifulSoup and PyPDF2 to parse the HTML or PDF data and extract the necessary information, such as company profiles, press releases, financial information, etc. The input is raw web data, and the output is extracted text data.

[0379] Step 2:

[0380] Data analysis

[0381] 1. The server analyzes the collected text data using natural language processing technology. The input is the extracted text data, and the output is the analyzed text data.

[0382] 2. The server uses spaCy and NLTK to tokenize the text data and tag it with parts of speech. Specifically, it splits sentences such as "Reisha Co., Ltd. invests in AI technology" into words and tags each word with a part of speech.

[0383] 3. The server uses Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates. The input is tokenized text data, and the output is text data with identified entities.

[0384] 4. The server uses NetworkX to analyze the relationships between entities using relational modeling techniques. The input is text data with identified entities, and the output is data with analyzed relationships.

[0385] Step 3:

[0386] Summary Generation

[0387] 1. The server generates a summary in a predefined format based on the analysis results. The input is the analyzed text data, and the output is the generated summary sentence.

[0388] 2. The server saves the generated summary in a database in JSON or XML format. The input is the generated summary sentence, and the output is the summary data saved in the database.

[0389] Step 4:

[0390] User Search Interface

[0391] 1. The device provides an interface where the user can enter a search query. Specifically, it provides a web page or mobile application that includes a search bar and a search button. The input is the user's search query, and the output is an API request from the device to the server.

[0392] 2. The user enters a query about a specific organization or transaction, such as "Show me an overview of transactions for this company."

[0393] Step 5:

[0394] Search and Display

[0395] 1. The device sends the search query entered by the user to the server. The input is the user's search query, and the output is an API request to the server.

[0396] 2. The server searches the database for summary information related to the user's query and retrieves it in JSON format. The input is the API request to the server, and the output is the retrieved summary information.

[0397] 3. The terminal visually displays the acquired summary information. Specifically, it presents the information in a card format. The input is summary information in JSON format, and the output is summary information displayed on the user interface.

[0398] Step 6:

[0399] Using the Emotion Engine

[0400] 1. The server sends the user's text input to the emotion engine for emotion analysis. The input is the user's text comment, and the output is the analyzed emotion data.

[0401] 2. The server generates customized feedback based on the analysis results of the emotion engine. For example, if the analyzed emotion is "satisfied," it generates feedback such as "We'll also introduce you to some new investment opportunities that may interest you." The input is the analyzed emotion data, and the output is the generated feedback.

[0402] 3. The terminal displays the feedback received from the server on the user interface. The input is the feedback from the server, and the output is the feedback displayed on the user interface.

[0403] (Application example 2)

[0404] 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."

[0405] In modern society, the rapid increase in the amount of information is making it difficult for users to quickly and efficiently obtain the information they need. There is also a growing need for personalized information provision, and there is also a demand for information that is customized based on the user's emotions. However, current systems do not adequately provide information that meets these needs. Therefore, it is necessary to provide a system that allows users to efficiently search for specific information and obtain information that matches their emotional state.

[0406] 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.

[0407] In this invention, the server includes means for collecting data from official websites, information sources, and industry databases of target organizations and groups, means for analyzing the collected text data using natural language processing technology, means for summarizing the analyzed data in a predefined format, means for storing the summarized data in a database and making it accessible to users, means for users to search for information on specific organizations and transactions through a user interface, means for displaying search results on the user's terminal, and means for analyzing emotions from text entered by the user and customizing search results based on the emotions, thereby enabling users to not only quickly obtain high-quality information but also receive information customized based on their emotions.

[0408] An "organization" is a group formed to achieve a specific purpose, and includes companies, groups, institutions, etc.

[0409] A "group" is a collection of people or things with a common purpose or interest.

[0410] A "source" is a medium or platform that provides information such as news, data, and statistics.

[0411] An "industry database" is a database that collects and manages information related to a specific industry.

[0412] "Data collection" is the process of gathering data needed for a specific purpose.

[0413] "Natural language processing technology" is a technology that enables computers to understand and process human language, and includes language analysis, sentiment analysis, summary generation, and more.

[0414] "Analysis" is the process of understanding the content of data and extracting meaning from it.

[0415] A "summary" is a shortened version of text or information that extracts only the main points.

[0416] A "database" is a system that centrally manages and stores data and allows quick access when needed.

[0417] "User interface" refers to the screens and operating methods that users use to interact with the system.

[0418] "Searching" is the process of searching a database or the Internet to find specific information.

[0419] A "terminal" is a hardware device that allows a user to enter information and display results.

[0420] "Emotion analysis" is a technology that extracts and understands user emotions from text, voice, etc.

[0421] "Customization" means adjusting functionality and display content to suit a user's specific needs and preferences.

[0422] This invention is a system that uses a server and a terminal to allow a user to efficiently collect information about an organization or group and provide customized information based on that information. This system includes the following processes.

[0423] First, the server collects data from the target organization's or group's official website, information sources, and industry databases using web scraping techniques such as the requests library and BeautifulSoup, and saves the collected data in text format.

[0424] The collected data is then analyzed on the server using natural language processing techniques, specifically using NLP libraries such as Spacy to tokenize the text data, tag parts of speech, and perform entity recognition, and sentiment analysis using a pipeline of Transformers libraries.

[0425] The parsed data is summarized in a predefined format, including key points and entity information, and stored in a database in JSON or XML format.

[0426] When a user searches for information about a particular organization or transaction, they enter a search query through a user interface, which can be a web or mobile application.

[0427] The server retrieves relevant summary information from a database based on the input search query and sends it to the terminal, which displays the information on a user interface.

[0428] Furthermore, when a user inputs their thoughts or opinions through their device, the server-side sentiment analysis engine analyzes them and customizes search results based on the user's emotions. For example, if a user inputs a sentiment such as "I'm satisfied with my recent transaction," the server will recognize the emotion "satisfied" and provide the user with additional, more positive information.

[0429] As a specific example, if a user responds to the prompt, "Please tell us how you felt after reading recent news about AI technology," by typing, "I'm very satisfied with the recent progress in AI technology. I'd like to hear other positive news," the server will analyze this sentiment and provide additional positive news.

[0430] This system allows users to efficiently obtain information of interest and also receive customized information based on their own emotions.

[0431] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0432] Step 1:

[0433] The server collects data from the official websites of target organizations and groups, information sources, and industry databases. It receives a list of URLs and a search query as input, which determines the content of the web pages to collect. It issues HTTP requests using the requests library to retrieve HTML or PDF data from the specified websites. It uses BeautifulSoup to extract the necessary text information from the retrieved data. The extracted text information is output and passed to the next analysis step.

[0434] Step 2:

[0435] The server analyzes the collected text data using natural language processing technology. It receives the text information extracted in step 1 as input. It uses an NLP library such as Spacy to tokenize the text data and perform part-of-speech tagging and entity recognition. This identifies entities such as nouns, verbs, company names, and dates in the text, and outputs the relationships between these entities and the text.

[0436] Step 3:

[0437] The server summarizes the analyzed data into a predefined format. It receives as input the analysis results (entities and relationships) obtained in step 2. It uses a summary generation algorithm to generate a shortened text that includes key points and entity information. The generated summary text is output in a format (JSON or XML) that can be stored in the database.

[0438] Step 4:

[0439] The server stores the summarized data in a database and makes it accessible to users. As input, it receives the summary text generated in step 3. The server stores this in the database in the specified format (JSON or XML). The stored data can be later used for searching and display.

[0440] Step 5:

[0441] A user inputs a search query for information about a specific organization or transaction into the user interface via a terminal. The user inputs a specific query into the interface using a keyboard or mouse. The query is sent from the terminal to the server. The server analyzes the query, searches the database for relevant summary information, and outputs the search results.

[0442] Step 6:

[0443] The server sends the search results to the terminal, which displays them on its user interface. As input, it receives the search results obtained in step 5. The terminal visually displays the search results using HTML and CSS, allowing the user to view the results on the screen.

[0444] Step 7:

[0445] The user inputs their thoughts and opinions through the terminal. The user inputs their thoughts and opinions in the text box and presses the send button. This input data is sent from the terminal to the server.

[0446] Step 8:

[0447] The server uses a sentiment analysis engine to analyze the sentiment from the text entered by the user. As input, it receives the user's text data obtained in step 7. Using a pipeline of the Transformers library, it classifies the sentiment in the text into categories such as positive or negative. The analyzed sentiment is obtained as output.

[0448] Step 9:

[0449] The server customizes search results based on the analyzed emotions and provides them to the user. As input, it receives the emotion analysis results obtained in step 8. If the emotion is positive, it searches for and selects additional information containing positive content and adds it to the search results. The customized search results are output and sent back to the terminal. The user can receive information that suits their emotions.

[0450] As a specific example of how it works, if the server displays a prompt on the device saying, "Please tell us how you felt after reading recent news about AI technology," and the user enters, "I'm very satisfied with the recent progress in AI technology. I'd like to hear other positive news," the server will recognize this emotion as "POSITIVE" and provide additional positive news.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] [Second embodiment]

[0455] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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).

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] 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."

[0467] The present invention provides an automated information summarization system that collects data from official websites of target companies and organizations, news sources, and industry databases, analyzes and summarizes the data, and makes it available to users efficiently. The following means and methods are included to implement this system:

[0468] Explanation of program processing

[0469] 1. Data Collection

[0470] The server sets up URLs to access the target company's official website, news articles, and industry databases.

[0471] The server crawls each URL and retrieves the data in HTML or PDF format.

[0472] The server uses scraping technology to extract necessary information from web pages and documents, such as company profiles, financial information, and press releases.

[0473] 2. Data Analysis

[0474] The server analyzes the collected data using natural language processing (NLP) technology.

[0475] The server tokenizes the text, tags it with parts of speech, and uses Named Entity Recognition (NER) to identify important entities such as company names, people's names, and dates.

[0476] The server uses relational modeling to recognize the relationships between the identified entities.

[0477] 3. Summary Generation

[0478] Based on the analysis results, the server summarizes the company profile and transaction overview in a predefined format.

[0479] The server stores the generated summary as structured data (JSON or XML format).

[0480] 4. User Interface

[0481] The terminal provides a web or mobile application interface that allows users to input and search information about specific companies or transactions.

[0482] A user enters a search query through the interface to request information.

[0483] The server searches the database based on the input query and retrieves the relevant summary information.

[0484] The terminal displays the acquired summary information to the user in a visually viewable format.

[0485] Specific examples

[0486] 1. Data Collection

[0487] The server collects data from the official Temple Co., Ltd. website, industry news sites, and industry-specific databases.

[0488] The server extracts "Temple's latest press release" through HTML analysis and extracts "2023 Medium-Term Management Plan" using PDF analysis.

[0489] 2. Data Analysis

[0490] The server tokenizes the extracted text data and tags each word with a part of speech tag.

[0491] The server uses NER to identify entities such as "Temple," "AI technology," and "investment amount" and analyze the relationships between them.

[0492] 3. Summary Generation

[0493] The server generates the following summary based on the analysis results:

[0494] Company Overview: Temple is primarily engaged in software development and has approximately 1,500 employees.

[0495] Transaction Overview: In its medium-term management plan for fiscal 2023, the company announced that it will significantly expand investment in AI technology.

[0496] 4. User Interface

[0497] A user enters a search query: "Show me Temple's trading summary."

[0498] The server searches the abstract database for the latest information about "Temple Company" and transmits the retrieved data to the terminal.

[0499] The terminal displays the acquired summary information in a card format on the user interface, allowing the user to easily view it.

[0500] This system allows users to quickly obtain high-quality corporate information, enabling them to efficiently create account plans and prepare for business negotiations.

[0501] The processing flow will be explained below.

[0502] Step 1:

[0503] The server prepares a list of URLs for the official websites of the companies from which data is collected, as well as URLs for news sources and industry databases.

[0504] Step 2:

[0505] The server accesses websites and news sites according to the URL list, using a crawler to retrieve HTML and PDF data.

[0506] Step 3:

[0507] The server scrapes the acquired HTML and PDF data, extracting necessary information such as company profiles, press releases, and financial information.

[0508] Step 4:

[0509] The server uses natural language processing (NLP) technology to analyze the extracted text data, specifically tokenizing and tagging parts of speech.

[0510] Step 5:

[0511] The server uses Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates.

[0512] Step 6:

[0513] The server uses relational modeling to analyze the relationships between the identified entities, for example, recognizing the relationship "Company A invests in AI technology."

[0514] Step 7:

[0515] The server generates summaries based on the analysis results in predefined formats, such as company overviews, transaction summaries, etc.

[0516] Step 8:

[0517] The server stores the generated summaries in a database in JSON or XML format, with a timestamp attached to the data, allowing users to distinguish between new and old information.

[0518] Step 9:

[0519] The device provides an interface for web and mobile applications that allow users to enter and search for information.

[0520] Step 10:

[0521] A user searches for information about a particular company or transaction through a terminal interface.

[0522] Step 11:

[0523] The device sends the search query entered by the user to the server, using an API request to send the query.

[0524] Step 12:

[0525] The server retrieves relevant abstract information from a database based on the received search query, and retrieves the most relevant and up-to-date abstract information.

[0526] Step 13:

[0527] The server sends a summary of the search results to the device, often in the form of a JSON response.

[0528] Step 14:

[0529] The terminal displays the received summary information on a user interface in a visually easy-to-read format, such as a card format or a table format.

[0530] Example 1

[0531] 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."

[0532] With the advancement of the information society, a huge amount of data is being generated from official websites of companies and organizations, news sources, and industry databases. However, there is a lack of an automated system to efficiently collect, analyze, summarize, and provide this data. Because accessing the information many users need is cumbersome, there is a need to obtain high-quality information quickly.

[0533] 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.

[0534] In this invention, the server includes means for collecting data from official websites, news platforms, and industry databases of target organizations and groups, means for using natural language processing (NLP) techniques to analyze the collected data, means for summarizing the analyzed data in a predefined format, means for storing the summarized data in a database and making it accessible to users, means for users to search for information on specific organizations and transactions through a user interface, and means for displaying search results on the user's terminal, thereby enabling efficient collection of large amounts of data and rapid provision of high-quality summarized information.

[0535] "Covered organizations and entities" refers to corporations, non-profit organizations, and other official entities that collect and analyze data.

[0536] "Official Website" refers to the online informational webpage officially operated by the organization or entity in question.

[0537] "News Platform" refers to an online platform operated by a news provider or media company to distribute the latest news and articles.

[0538] "Industry Database" refers to an online or offline information management system that collects, stores, and provides detailed data and information about a particular industry.

[0539] "Data collection methods" refers to the programs or technologies used to obtain information from the target website or database.

[0540] "Natural language processing (NLP) technology" refers to artificial intelligence technology that enables computers to understand, interpret, and generate human language.

[0541] "Means for analyzing data" refers to the programs and algorithms used to convert collected data into information and extract meaning.

[0542] "Summarization tools" refer to programs or technologies that extract important information from the analyzed data and summarize it concisely.

[0543] A "database" refers to a collection of information that is structured so that data can be efficiently managed and searched.

[0544] "User interface" refers to the screens and applications through which a user interacts with a system and inputs or obtains information.

[0545] "Information about a specific entity or transaction" refers to detailed data, news, and other relevant information about a specific entity or transaction.

[0546] "Terminal" refers to a device such as a computer, smartphone, or tablet that a user uses to access the system.

[0547] This invention is an automatic information summarization system that automatically collects data from official websites of target organizations and groups, news platforms, and industry databases, analyzes the data, generates summaries, and makes them available to users efficiently. To implement this system, the following hardware and software are used.

[0548] Hardware used

[0549] 1. Server: A central computer with high computing power and large storage capacity for collecting, analyzing, summarizing, and storing data.

[0550] 2. Device: The device a user uses to enter information and receive search results. This can include a computer, smartphone, tablet, etc.

[0551] Software used

[0552] 1. Crawl program: Software used to automatically collect data from target websites and databases.

[0553] 2. Natural Language Processing (NLP) libraries: For example, use Python's "spaCy" or "NLTK" to analyze the collected text data.

[0554] 3. Database management system: A system for storing and managing collected and analyzed data. Examples include "MySQL" and "MongoDB."

[0555] 4. User interface platform: A framework for building web and mobile applications. Examples include "React" and "Flutter."

[0556] System processing procedure

[0557] Data collection

[0558] The server sets the URLs to access the official websites, news platforms, and industry databases of the target organizations and groups. It then crawls these URLs and retrieves data in HTML or PDF format. For example, the server sets the URL "https: / / example.com / company" to collect data on the relevant companies.

[0559] Data Preprocessing

[0560] The server parses the collected HTML and PDF documents and imports the necessary information into the database. For example, it extracts the "About Us" section through HTML parsing, converts it to JSON format, and stores it. It also performs data cleaning to remove invalid data and noise.

[0561] Data analysis

[0562] The server uses natural language processing (NLP) techniques to analyze the collected text data. For example, it uses the Python library "spaCy" to tokenize the text and tag parts of speech. It also uses Named Entity Recognition (NER) to identify important entities such as company names, people's names, and dates, and uses relational modeling to recognize relationships between the identified entities.

[0563] Summary Generation

[0564] Based on the analysis results, the server summarizes the company profile and transaction overview in a predefined format. For example, it generates a summary such as "Company profile: The target company is primarily engaged in software development, with approximately 1,500 employees." It then saves the summary in JSON or XML format.

[0565] Providing information

[0566] The terminal provides an interface that allows users to input and search for information about specific organizations or transactions. Users input search queries through the interface to request information, such as "Please show me an overview of the transactions of the target company." The server retrieves the relevant information from the summary database and sends it to the terminal. The terminal then displays the retrieved information to the user in a visually viewable format.

[0567] Usage example

[0568] Examples of prompts to input into the generative AI model include:

[0569] 1. "Please provide the latest financial information for the target company."

[0570] 2. "I would like to know about the medium-term management plan for fiscal 2023."

[0571] 3. "What are your investment plans for AI technology?"

[0572] This allows users to quickly obtain high-quality information and improve work efficiency.

[0573] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0574] Step 1: Data collection

[0575] The server sets up access URLs to the target organization's official website, news platform, and industry database.

[0576] Input: URL list, e.g. "https: / / example.com / company"

[0577] Output: URLs of websites and databases to be collected

[0578] Specific operation: The server accesses the specified URL and downloads the web page or PDF document.

[0579] Step 2: Data extraction

[0580] The server parses the collected HTML and PDF documents and extracts the necessary information.

[0581] Input: Collected HTML and PDF data

[0582] Output: Extracted text data

[0583] What it does: The server uses HTML and PDF parsing tools to extract specific information in text format, such as the "About Us" and "Press Releases" sections.

[0584] Step 3: Data cleaning

[0585] The server cleans the extracted data to remove invalid data and noise.

[0586] Input: Extracted text data

[0587] Output: Cleaned data

[0588] What it does: The server uses regular expressions and machine learning models to filter out ads and irrelevant text.

[0589] Step 4: Data analysis

[0590] The server analyzes the cleaned data using natural language processing (NLP) techniques.

[0591] Input: Cleaned data

[0592] Output: Parsed text data

[0593] What it does: The server uses an NLP library (e.g., spaCy) to tokenize the text, tag it as a part of speech, and use Named Entity Recognition (NER) to identify significant entities.

[0594] Step 5: Relational Modeling

[0595] The server performs relational modeling based on the analyzed data and recognizes the relationships between entities.

[0596] Input: Parsed text data and entities

[0597] Output: Relationships between entities

[0598] What happens: The server models transitive relationships and identifies relationships such as "Organization A started project X in 2023."

[0599] Step 6: Summary generation

[0600] The server generates a summary in a predefined format based on the results of the relational modeling.

[0601] Input: Entities and their relationships

[0602] Output: Summary

[0603] Specific operation: The server uses a summary generation algorithm to generate a summary sentence such as, "Organization A announced investment in AI technology in its 2032 mid-term plan."

[0604] Step 7: Save Data

[0605] The server stores the generated summaries in a database in JSON or XML format.

[0606] Input: Generated summary

[0607] Output: Summary data stored in a database

[0608] Specific operation: The server uses a database management system (e.g., MySQL or MongoDB) to store the summary data in an appropriate format.

[0609] Step 8: Information Search

[0610] The terminal provides an interface for the user to search for information.

[0611] Input: User's search query

[0612] Output: Display of the user interface

[0613] What it does: The device provides a search box and filters through the web or mobile application.

[0614] Step 9: Run a search

[0615] The server searches the database based on the user's search query and retrieves the relevant summary information.

[0616] Input: User's search query

[0617] Output: Summary information obtained

[0618] Specific operation: The server extracts the relevant summary information from the database using an SQL query or the like.

[0619] Step 10: View the results

[0620] The terminal displays the retrieved summary information to the user in a visually viewable format.

[0621] Input: Retrieved summary information

[0622] Output: User-visible search results

[0623] What it does: The device displays search results in card or list format, allowing users to easily browse information.

[0624] As an example, when a user enters a prompt such as "Please provide the latest financial information of the target company," the system automatically performs a series of processes from data collection to displaying the results, and provides the user with the relevant information.

[0625] (Application example 1)

[0626] 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."

[0627] In recent years, with the rapid release of large amounts of information about security risks and vulnerabilities, it has become increasingly important for organizational and individual security personnel to efficiently collect, analyze, and implement appropriate countermeasures. However, manually collecting and analyzing this information requires a huge amount of time and effort, making it difficult to respond in a timely manner. Therefore, there is a need for a system that can collect and analyze security information in real time and provide summaries.

[0628] 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.

[0629] In this invention, the server includes means for collecting data from official websites of target organizations and groups, news sources, and industry databases, means for analyzing the collected text data using natural language processing (NLP) technology, means for summarizing the analyzed data in a predefined format, means for storing the summarized data in a database and making it accessible to users, means for users to search for information about specific organizations and transactions through a user interface, means for displaying search results on the user's terminal, and means for collecting and analyzing security information and providing summaries in real time, thereby enabling security personnel to quickly obtain high-quality security information and take appropriate measures.

[0630] "Organizations and groups" refers to companies, public institutions, non-profit organizations, etc. of various sizes and types.

[0631] "Official website" refers to an information dissemination page on the Internet that is officially operated and provided by an organization or group.

[0632] "News source" refers to the source of news articles and news programs provided by newspapers, news sites, television stations, etc.

[0633] "Industry Database" means a system or service that collects and stores data and information related to a particular industry.

[0634] "Means of collecting data" refers to technologies and devices used to obtain information, such as web scraping and API access.

[0635] "Natural Language Processing (NLP)" refers to the science and technology for analyzing, understanding, and generating human language.

[0636] "Means of analysis" refers to the software or algorithms used to analyze the collected text data.

[0637] "Summarization tools" refers to techniques or devices used to summarize key points from collected and analyzed data.

[0638] "Database" refers to a system for efficiently storing, retrieving, updating, and managing data.

[0639] "User interface" refers to the screens and applications through which users enter information and receive search results.

[0640] "User terminal" refers to a device such as a desktop computer, laptop, tablet, or smartphone that a user uses to connect to the Internet and obtain information.

[0641] "Security information" refers to information regarding vulnerabilities, threats, and risks of systems and networks.

[0642] "Real-time" refers to processing and providing information with extremely short delays.

[0643] This invention is a system for quickly collecting, analyzing, summarizing, and providing security-related information to users. This system is centered around a server and uses the following hardware and software:

[0644] Program Overview

[0645] 1. Data Collection

[0646] The server accesses the official websites of the organizations and organisations in question, news sources, and industry databases to retrieve information, using the Requests library to retrieve information from the network and BeautifulSoup to parse the HTML.

[0647] 2. Data Analysis

[0648] The server analyzes the collected text data using natural language processing (NLP) techniques, tokenizing the text data and extracting entities using the SpaCy library, and summarizing the text using Transformers models.

[0649] 3. Data Summary

[0650] Based on the analysis results, the server summarizes the information in a predefined format, and the summarized data is stored in a database in JSON or XML format.

[0651] 4. Information provision

[0652] The user's device can search for information about a specific organization or transaction through a user interface. When the user enters a search query, the server retrieves relevant summary data from the database and sends it to the device.

[0653] Specific example explanation

[0654] For example, if a user inputs a prompt such as "Show me information about the latest security risks," the server will generate a summary of the latest vulnerabilities and risks from the collected security information and display it on the terminal.

[0655] Hardware and Software Used

[0656] Server: A central device with high-performance processing power for data collection and analysis.

[0657] Requests library: Used to retrieve data from the web.

[0658] BeautifulSoup: Used for HTML parsing.

[0659] SpaCy library: Used for natural language processing.

[0660] Transformers library: Used for generating summaries.

[0661] Database: A system that stores data in JSON or XML format.

[0662] User devices: desktop computers, laptops, tablets, smartphones.

[0663] Prompt Sentence Examples

[0664] "Show me information about the latest security risks"

[0665] "Give me a summary of the latest vulnerability news."

[0666] By using this system, security personnel can quickly obtain high-quality information and take countermeasures efficiently.

[0667] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0668] Step 1:

[0669] The server collects data from official websites of organizations and groups of interest, news sources, and industry databases. Specifically, it uses the Requests library to access these sources and retrieve data in HTML or text format. The input to this step is a list of URLs for each source, and the output is the raw data retrieved.

[0670] Step 2:

[0671] The server uses BeautifulSoup to parse the raw data and extract the necessary information. For example, it extracts the article title, publication date, and body of the article through HTML parsing. The input to this step is the raw data obtained in step 1, and the output is the extracted structured data.

[0672] Step 3:

[0673] The server uses natural language processing (NLP) techniques to parse the extracted structured data. Specifically, it uses the SpaCy library to tokenize the text and extract entities (e.g., vulnerabilities, risks, organization names, etc.). The input of this step is the structured data obtained in step 2, and the output is the parsed data containing entity information.

[0674] Step 4:

[0675] The server uses Transformers to summarize the parsed data. For example, it extracts important parts from the original text and creates a short summary. The input to this step is the parsed data obtained in step 3, and the output is a summary sentence.

[0676] Step 5:

[0677] The server stores the abstract in a database in JSON or XML format. The input of this step is the abstract generated in step 4, and the output is the stored structured data.

[0678] Step 6:

[0679] A user utilizes a terminal to search for information about a particular organization or transaction through a user interface, for example, by entering a prompt phrase such as "Show me information about the latest security risks." The input to this step is the user's search query, and the output is a search request.

[0680] Step 7:

[0681] The server receives the search request and retrieves the relevant summary data from the database. The input to this step is the search query received in step 6, and the output is the search results.

[0682] Step 8:

[0683] The server sends the search results to the user's terminal. The input of this step is the search results obtained in step 7, and the output is the data sent to the terminal.

[0684] Step 9:

[0685] The terminal displays the received data on a user interface, for example visually displaying the summarized data in the form of cards. The input of this step is the data received in step 8, and the output is the displayed information.

[0686] 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.

[0687] The present invention relates to a system for collecting data related to a company or organization, analyzing and summarizing the data, and providing customized information by recognizing user emotions. The system includes a data collection means, a natural language processing (NLP) means, a summary generation means, a database storage means, a user interface, a search means, a display means, and an emotion engine.

[0688] Explanation of program processing

[0689] 1. Data Collection

[0690] The server prepares a list of URLs to access official company and organization websites, news sources, and industry databases.

[0691] The server crawls the URL list to obtain HTML and PDF data, and then uses scraping technology to extract necessary information such as company profiles, press releases, and financial information.

[0692] 2. Data Analysis

[0693] The server analyzes the collected data using natural language processing (NLP) techniques, tokenizing the text and tagging it by part of speech.

[0694] The server utilizes Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates.

[0695] The server uses relational modeling to analyze the relationships between entities.

[0696] 3. Summary Generation

[0697] The server generates a summary in a predefined format based on the analysis results, including a company profile and transaction overview.

[0698] The server stores the generated summaries in a database in JSON or XML format.

[0699] 4. User Interface

[0700] The device provides an interface through which the user can enter a search query, either through a web application or a mobile application.

[0701] A user uses the device interface to search for information about a particular company or transaction.

[0702] 5. Search and Display

[0703] The device sends the search query entered by the user to the server via an API request.

[0704] The server searches and retrieves relevant summary information from a database.

[0705] The terminal displays the search results on a user interface in a visually viewable format.

[0706] 6. Use of Emotion Engine

[0707] The server uses an emotion engine to parse emotions from the user's text input. For example, if the user inputs "I'm disappointed with my recent transaction," the emotion engine recognizes the emotion "disappointed."

[0708] The server provides search results and customized feedback that match the user's mood based on the recognized emotions.

[0709] Based on the results of the emotion engine, the device displays appropriate feedback, such as "We suggest improvements for this transaction."

[0710] Specific examples

[0711] 1. Data Collection

[0712] The server collects data from the official website of "Reisha Co., Ltd.", industry news sites, and industry-specific databases. The data includes official announcements, industry trend reports, etc.

[0713] 2. Data Analysis

[0714] The server tokenizes the collected text data, tags it with parts of speech, and uses NER to identify entities such as "example company," "AI technology," and "investment amount."

[0715] 3. Summary Generation

[0716] The server generates the following summary based on the analysis results:

[0717] Company profile: Reisha Co., Ltd. is primarily engaged in software development and has approximately 1,500 employees.

[0718] Transaction Overview: The latest mid-term plan calls for a significant increase in investment in AI technology.

[0719] 4. User Interface

[0720] A user enters a search query such as "I want to see an overview of transactions for example companies."

[0721] The server retrieves the latest information about the sample company from the summary database and transmits the retrieved data to the terminal.

[0722] The terminal displays the acquired summary information in a card format on the user interface.

[0723] 5. Use of Emotion Engine

[0724] The user enters a comment saying, "I am very satisfied with the company's recent performance."

[0725] The server uses an emotion engine to recognize the emotion "satisfaction" and provides the user with positive feedback such as "We'll also introduce you to some new investment opportunities that may interest you."

[0726] The device will then display appropriate feedback or additional information based on the recognized emotion.

[0727] This system not only enables users to quickly obtain high-quality corporate information, but also allows them to more efficiently gather information and prepare for business negotiations by receiving customized feedback tailored to their emotional state.

[0728] The processing flow will be explained below.

[0729] Step 1:

[0730] The server prepares a pre-configured list of URLs for official company websites, news sites, and industry databases to be collected.

[0731] Step 2:

[0732] The server sends an HTTP request to each website based on the URL list to retrieve HTML content or PDF files.

[0733] Step 3:

[0734] The server parses the HTML content and PDF files and uses scraping technology to extract important data such as company profiles, press releases, and financial information.

[0735] Step 4:

[0736] The server then uses natural language processing (NLP) technology to tokenize the extracted text data, breaking it down into sentences and words.

[0737] Step 5:

[0738] The server performs part-of-speech tagging on the tokenized data, which identifies the part of speech (noun, verb, adjective, etc.) of each word.

[0739] Step 6:

[0740] The server uses Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates.

[0741] Step 7:

[0742] The server uses relational modeling techniques to analyze the relationships between the identified entities, e.g., to clarify the relationship between Company A's investment in AI technology.

[0743] Step 8:

[0744] The server generates a summary based on the analysis results in a predefined format, which includes information such as company overview and transaction summary.

[0745] Step 9:

[0746] The server stores the generated summaries in a database in JSON or XML format, with a timestamp attached to distinguish between new and old data.

[0747] Step 10:

[0748] The terminal provides an interface through which the user enters a search query via a web application or a mobile application.

[0749] Step 11:

[0750] The user enters information about a particular company or transaction into the terminal interface and presses the search button.

[0751] Step 12:

[0752] The terminal sends the search query entered by the user to the server in the form of an API request.

[0753] Step 13:

[0754] The server retrieves relevant summary information from the database based on the received search query, retrieving the most recent and relevant data.

[0755] Step 14:

[0756] The server sends summary information of the search results in JSON format to the terminal.

[0757] Step 15:

[0758] The terminal visually displays the received summary information on the user interface in an easy-to-read format such as cards or tables.

[0759] Step 16:

[0760] The server analyzes the user's text input using an emotion engine that recognizes emotions (e.g., joy, sadness, disappointment, etc.) from the user's input text.

[0761] Step 17:

[0762] The server customizes search results and feedback based on the user's emotions, as recognized by the emotion engine. For example, if a user expresses "disappointment," the server suggests related topics and solutions.

[0763] Step 18:

[0764] The device presents appropriate feedback to the user based on the results of the emotion engine, allowing the user to receive information according to their emotions.

[0765] Example 2

[0766] 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."

[0767] Conventional corporate information gathering systems have made it difficult for users to quickly and accurately obtain the information they need. Furthermore, they lack the ability to analyze and summarize the collected information, and provide customized information based on the user's feelings, resulting in issues with the usefulness of the information and ease of access. As a result, efficiency in preparing for business negotiations and making decisions has decreased, and gathering information has taken a lot of time.

[0768] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0769] In this invention, the server includes: means for collecting data from official websites, information sources, and industry databases of target organizations; means for analyzing the collected text data using natural language processing technology; means for summarizing the analyzed data in a predefined format; means for storing the summarized data in a database and making it accessible to users; means for users to search for information on specific organizations or transactions through a user interface; means for displaying search results on the user's terminal; and means including an emotion engine for analyzing user emotions from text input and providing customized information. This allows users to not only quickly obtain high-quality corporate information but also receive customized feedback tailored to their emotional state.

[0770] "Covered Entity" means the company, association, or other legal entity identified as the subject of data collection, analysis, or abstraction.

[0771] "Sources" refers to websites and databases that contain information related to an organization, such as news sites, industry reports, and social media.

[0772] "Industry database" refers to an online database that centrally manages and provides data related to a specific industry.

[0773] "Data collection methods" refers to the techniques and methods used to obtain the desired data from websites and databases, such as using web crawlers or API requests.

[0774] "Natural language processing technology" refers to machine learning algorithms and text analysis technologies for analyzing and understanding human language.

[0775] "Summarization means" refers to algorithms or methods for succinctly presenting collected and analyzed data as a summary statement.

[0776] "Means of database storage" refers to the techniques and methods used to store data in a structured format in a database to facilitate search and retrieval.

[0777] "User Interface" refers to the interactive screen or application through which a user interacts with a system, inputs information, and obtains results.

[0778] "Terminal" refers to a device such as a computer or smartphone that allows a user to input data and display results.

[0779] An "emotion engine" refers to technology or algorithms that analyze a user's emotional state from text and provide appropriate feedback based on the results.

[0780] "Relational modeling technology" refers to a data analysis technique for analyzing the relationships between entities and visualizing them as a network.

[0781] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a lightweight data exchange format for structuring and storing data.

[0782] "XML format" is an abbreviation for eXtensible Markup Language, and refers to a general-purpose markup language for hierarchically structuring and storing data.

[0783] The present invention is a system that collects information about organizations and groups, analyzes and summarizes it, and further recognizes the user's emotions to provide customized information. This system consists of a server, a terminal, and a user. The roles and operations of each component are described in detail below.

[0784] server

[0785] 1. Data Collection

[0786] The server collects data from official websites, information sources, and industry databases. Specifically, it uses a web crawler such as Selenium to access a specified list of URLs and retrieves data in HTML or PDF format. From the retrieved data, it extracts necessary information such as company profiles, press releases, and financial information using tools such as BeautifulSoup and PyPDF2.

[0787] 2. Data Analysis

[0788] The server analyzes the collected text data using natural language processing techniques. For example, it uses libraries such as spaCy and NLTK to tokenize the text data and tag parts of speech. It also uses Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates. It then analyzes the relationships between entities using relational modeling tools such as NetworkX.

[0789] 3. Summary Generation

[0790] The server generates a summary in a predefined format based on the analysis results. This summary includes a company profile and a business overview. For example, it generates a summary statement in the following format: "Reisha Co., Ltd. is primarily engaged in software development and has approximately 1,500 employees. Its latest medium-term plan calls for a significant increase in investment in AI technology." The generated summary is stored in a database in JSON or XML format.

[0791] 4. Use of Emotion Engine

[0792] The server uses an emotion engine to analyze emotions from the user's text input. For example, if a user enters a comment such as "I'm very satisfied with the recent performance of the company," the emotion engine recognizes the emotion as "satisfied." Based on the results, the server generates customized feedback and provides it in the form of, for example, "We'll also introduce you to some new investment opportunities that may be of interest to you."

[0793] Terminal

[0794] 1. Interface provision

[0795] The terminal provides an interface, including a web application or a mobile application, through which a user can enter a search query. For example, the terminal may provide an interface through which a user can enter a query such as "Show me a trading summary for company X."

[0796] 2. Search and Display

[0797] The device sends the search query entered by the user to the server and visually displays the search results received from the server. The search results are displayed in a card format, providing a concise and visually easy-to-understand format.

[0798] User

[0799] 1. Information Search

[0800] Users use the device interface to search for information about a particular organization or transaction, for example, by entering a search query such as "Show me an overview of transactions for this company."

[0801] Specific examples

[0802] The server collects data from the official website of "Example Company," industry news sites, and industry-specific databases. For example, it obtains and analyzes data such as official announcements and industry trend reports to generate a summary such as "Example Company announced plans to invest 50 billion yen in AI technology." If a user inputs "I would like to see an overview of Example Company's transactions," this summary is displayed on the device. Furthermore, if a user inputs a comment such as "I am very satisfied with Example Company's recent performance," the emotion engine recognizes that emotion and provides feedback such as "I would also like to introduce some new investment opportunities that may be of interest to you."

[0803] Example prompt for a generative AI model:

[0804] Could you please provide the latest trading information for Example Co., Ltd.? Also, could you please provide a system that analyzes the sentiment of user reviews about recent business performance and provides positive feedback?

[0805] In this way, the present invention enables the rapid acquisition of high-quality corporate information and improves the efficiency of information gathering and decision-making by providing customized feedback that is tailored to the user's emotions.

[0806] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0807] Step 1:

[0808] Data collection

[0809] 1. The server prepares a list of URLs to collect data from official websites of organizations, information sources, and industry databases. The input is the URL list, and the server sets up a web crawler based on it.

[0810] 2. The server launches a web crawler using Selenium to retrieve data in HTML or PDF format from the specified URL list, and the output is the retrieved raw web data.

[0811] 3. The server uses BeautifulSoup and PyPDF2 to parse the HTML or PDF data and extract the necessary information, such as company profiles, press releases, financial information, etc. The input is raw web data, and the output is extracted text data.

[0812] Step 2:

[0813] Data analysis

[0814] 1. The server analyzes the collected text data using natural language processing technology. The input is the extracted text data, and the output is the analyzed text data.

[0815] 2. The server uses spaCy and NLTK to tokenize the text data and tag it with parts of speech. Specifically, it splits sentences such as "Reisha Co., Ltd. invests in AI technology" into words and tags each word with a part of speech.

[0816] 3. The server uses Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates. The input is tokenized text data, and the output is text data with identified entities.

[0817] 4. The server uses NetworkX to analyze the relationships between entities using relational modeling techniques. The input is text data with identified entities, and the output is data with analyzed relationships.

[0818] Step 3:

[0819] Summary Generation

[0820] 1. The server generates a summary in a predefined format based on the analysis results. The input is the analyzed text data, and the output is the generated summary sentence.

[0821] 2. The server saves the generated summary in a database in JSON or XML format. The input is the generated summary sentence, and the output is the summary data saved in the database.

[0822] Step 4:

[0823] User Search Interface

[0824] 1. The device provides an interface where the user can enter a search query. Specifically, it provides a web page or mobile application that includes a search bar and a search button. The input is the user's search query, and the output is an API request from the device to the server.

[0825] 2. The user enters a query about a specific organization or transaction, such as "Show me an overview of transactions for this company."

[0826] Step 5:

[0827] Search and Display

[0828] 1. The device sends the search query entered by the user to the server. The input is the user's search query, and the output is an API request to the server.

[0829] 2. The server searches the database for summary information related to the user's query and retrieves it in JSON format. The input is the API request to the server, and the output is the retrieved summary information.

[0830] 3. The terminal visually displays the acquired summary information. Specifically, it presents the information in a card format. The input is summary information in JSON format, and the output is summary information displayed on the user interface.

[0831] Step 6:

[0832] Using the Emotion Engine

[0833] 1. The server sends the user's text input to the emotion engine for emotion analysis. The input is the user's text comment, and the output is the analyzed emotion data.

[0834] 2. The server generates customized feedback based on the analysis results of the emotion engine. For example, if the analyzed emotion is "satisfied," it generates feedback such as "We'll also introduce you to some new investment opportunities that may interest you." The input is the analyzed emotion data, and the output is the generated feedback.

[0835] 3. The terminal displays the feedback received from the server on the user interface. The input is the feedback from the server, and the output is the feedback displayed on the user interface.

[0836] (Application example 2)

[0837] 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."

[0838] In modern society, the rapid increase in the amount of information is making it difficult for users to quickly and efficiently obtain the information they need. There is also a growing need for personalized information provision, and there is also a demand for information that is customized based on the user's emotions. However, current systems do not adequately provide information that meets these needs. Therefore, it is necessary to provide a system that allows users to efficiently search for specific information and obtain information that matches their emotional state.

[0839] 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.

[0840] In this invention, the server includes means for collecting data from official websites, information sources, and industry databases of target organizations and groups, means for analyzing the collected text data using natural language processing technology, means for summarizing the analyzed data in a predefined format, means for storing the summarized data in a database and making it accessible to users, means for users to search for information on specific organizations and transactions through a user interface, means for displaying search results on the user's terminal, and means for analyzing emotions from text entered by the user and customizing search results based on the emotions, thereby enabling users to not only quickly obtain high-quality information but also receive information customized based on their emotions.

[0841] An "organization" is a group formed to achieve a specific purpose, and includes companies, groups, institutions, etc.

[0842] A "group" is a collection of people or things with a common purpose or interest.

[0843] A "source" is a medium or platform that provides information such as news, data, and statistics.

[0844] An "industry database" is a database that collects and manages information related to a specific industry.

[0845] "Data collection" is the process of gathering data needed for a specific purpose.

[0846] "Natural language processing technology" is a technology that enables computers to understand and process human language, and includes language analysis, sentiment analysis, summary generation, and more.

[0847] "Analysis" is the process of understanding the content of data and extracting meaning from it.

[0848] A "summary" is a shortened version of text or information that extracts only the main points.

[0849] A "database" is a system that centrally manages and stores data and allows quick access when needed.

[0850] "User interface" refers to the screens and operating methods that users use to interact with the system.

[0851] "Searching" is the process of searching a database or the Internet to find specific information.

[0852] A "terminal" is a hardware device that allows a user to enter information and display results.

[0853] "Emotion analysis" is a technology that extracts and understands user emotions from text, voice, etc.

[0854] "Customization" means adjusting functionality and display content to suit a user's specific needs and preferences.

[0855] This invention is a system that uses a server and a terminal to allow a user to efficiently collect information about an organization or group and provide customized information based on that information. This system includes the following processes.

[0856] First, the server collects data from the target organization's or group's official website, information sources, and industry databases using web scraping techniques such as the requests library and BeautifulSoup, and saves the collected data in text format.

[0857] The collected data is then analyzed on the server using natural language processing techniques, specifically using NLP libraries such as Spacy to tokenize the text data, tag parts of speech, and perform entity recognition, and sentiment analysis using a pipeline of Transformers libraries.

[0858] The parsed data is summarized in a predefined format, including key points and entity information, and stored in a database in JSON or XML format.

[0859] When a user searches for information about a particular organization or transaction, they enter a search query through a user interface, which can be a web or mobile application.

[0860] The server retrieves relevant summary information from a database based on the input search query and sends it to the terminal, which displays the information on a user interface.

[0861] Furthermore, when a user inputs their thoughts or opinions through their device, the server-side sentiment analysis engine analyzes them and customizes search results based on the user's emotions. For example, if a user inputs a sentiment such as "I'm satisfied with my recent transaction," the server will recognize the emotion "satisfied" and provide the user with additional, more positive information.

[0862] As a specific example, if a user responds to the prompt, "Please tell us how you felt after reading recent news about AI technology," by typing, "I'm very satisfied with the recent progress in AI technology. I'd like to hear other positive news," the server will analyze this sentiment and provide additional positive news.

[0863] This system allows users to efficiently obtain information of interest and also receive customized information based on their own emotions.

[0864] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0865] Step 1:

[0866] The server collects data from the official websites of target organizations and groups, information sources, and industry databases. It receives a list of URLs and a search query as input, which determines the content of the web pages to collect. It issues HTTP requests using the requests library to retrieve HTML or PDF data from the specified websites. It uses BeautifulSoup to extract the necessary text information from the retrieved data. The extracted text information is output and passed to the next analysis step.

[0867] Step 2:

[0868] The server analyzes the collected text data using natural language processing technology. It receives the text information extracted in step 1 as input. It uses an NLP library such as Spacy to tokenize the text data and perform part-of-speech tagging and entity recognition. This identifies entities such as nouns, verbs, company names, and dates in the text, and outputs the relationships between these entities and the text.

[0869] Step 3:

[0870] The server summarizes the analyzed data into a predefined format. It receives as input the analysis results (entities and relationships) obtained in step 2. It uses a summary generation algorithm to generate a shortened text that includes key points and entity information. The generated summary text is output in a format (JSON or XML) that can be stored in the database.

[0871] Step 4:

[0872] The server stores the summarized data in a database and makes it accessible to users. As input, it receives the summary text generated in step 3. The server stores this in the database in the specified format (JSON or XML). The stored data can be later used for searching and display.

[0873] Step 5:

[0874] A user inputs a search query for information about a specific organization or transaction into the user interface via a terminal. The user inputs a specific query into the interface using a keyboard or mouse. The query is sent from the terminal to the server. The server analyzes the query, searches the database for relevant summary information, and outputs the search results.

[0875] Step 6:

[0876] The server sends the search results to the terminal, which displays them on its user interface. As input, it receives the search results obtained in step 5. The terminal visually displays the search results using HTML and CSS, allowing the user to view the results on the screen.

[0877] Step 7:

[0878] The user inputs their thoughts and opinions through the terminal. The user inputs their thoughts and opinions in the text box and presses the send button. This input data is sent from the terminal to the server.

[0879] Step 8:

[0880] The server uses a sentiment analysis engine to analyze the sentiment from the text entered by the user. As input, it receives the user's text data obtained in step 7. Using a pipeline of the Transformers library, it classifies the sentiment in the text into categories such as positive or negative. The analyzed sentiment is obtained as output.

[0881] Step 9:

[0882] The server customizes search results based on the analyzed emotions and provides them to the user. As input, it receives the emotion analysis results obtained in step 8. If the emotion is positive, it searches for and selects additional information containing positive content and adds it to the search results. The customized search results are output and sent back to the terminal. The user can receive information that suits their emotions.

[0883] As a specific example of how it works, if the server displays a prompt on the device saying, "Please tell us how you felt after reading recent news about AI technology," and the user enters, "I'm very satisfied with the recent progress in AI technology. I'd like to hear other positive news," the server will recognize this emotion as "POSITIVE" and provide additional positive news.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] [Third embodiment]

[0888] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0889] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0890] 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).

[0891] 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.

[0892] 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.

[0893] 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).

[0894] 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.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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."

[0900] The present invention provides an automated information summarization system that collects data from official websites of target companies and organizations, news sources, and industry databases, analyzes and summarizes the data, and makes it available to users efficiently. The following means and methods are included to implement this system:

[0901] Explanation of program processing

[0902] 1. Data Collection

[0903] The server sets up URLs to access the target company's official website, news articles, and industry databases.

[0904] The server crawls each URL and retrieves the data in HTML or PDF format.

[0905] The server uses scraping technology to extract necessary information from web pages and documents, such as company profiles, financial information, and press releases.

[0906] 2. Data Analysis

[0907] The server analyzes the collected data using natural language processing (NLP) technology.

[0908] The server tokenizes the text, tags it with parts of speech, and uses Named Entity Recognition (NER) to identify important entities such as company names, people's names, and dates.

[0909] The server uses relational modeling to recognize the relationships between the identified entities.

[0910] 3. Summary Generation

[0911] Based on the analysis results, the server summarizes the company profile and transaction overview in a predefined format.

[0912] The server stores the generated summary as structured data (JSON or XML format).

[0913] 4. User Interface

[0914] The terminal provides a web or mobile application interface that allows users to input and search information about specific companies or transactions.

[0915] A user enters a search query through the interface to request information.

[0916] The server searches the database based on the input query and retrieves the relevant summary information.

[0917] The terminal displays the acquired summary information to the user in a visually viewable format.

[0918] Specific examples

[0919] 1. Data Collection

[0920] The server collects data from the official Temple Co., Ltd. website, industry news sites, and industry-specific databases.

[0921] The server extracts "Temple's latest press release" through HTML analysis and extracts "2023 Medium-Term Management Plan" using PDF analysis.

[0922] 2. Data Analysis

[0923] The server tokenizes the extracted text data and tags each word with a part of speech tag.

[0924] The server uses NER to identify entities such as "Temple," "AI technology," and "investment amount" and analyze the relationships between them.

[0925] 3. Summary Generation

[0926] The server generates the following summary based on the analysis results:

[0927] Company Overview: Temple is primarily engaged in software development and has approximately 1,500 employees.

[0928] Transaction Overview: In its medium-term management plan for fiscal 2023, the company announced that it will significantly expand investment in AI technology.

[0929] 4. User Interface

[0930] A user enters a search query: "Show me Temple's trading summary."

[0931] The server searches the abstract database for the latest information about "Temple Company" and transmits the retrieved data to the terminal.

[0932] The terminal displays the acquired summary information in a card format on the user interface, allowing the user to easily view it.

[0933] This system allows users to quickly obtain high-quality corporate information, enabling them to efficiently create account plans and prepare for business negotiations.

[0934] The processing flow will be explained below.

[0935] Step 1:

[0936] The server prepares a list of URLs for the official websites of the companies from which data is collected, as well as URLs for news sources and industry databases.

[0937] Step 2:

[0938] The server accesses websites and news sites according to the URL list, using a crawler to retrieve HTML and PDF data.

[0939] Step 3:

[0940] The server scrapes the acquired HTML and PDF data, extracting necessary information such as company profiles, press releases, and financial information.

[0941] Step 4:

[0942] The server uses natural language processing (NLP) technology to analyze the extracted text data, specifically tokenizing and tagging parts of speech.

[0943] Step 5:

[0944] The server uses Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates.

[0945] Step 6:

[0946] The server uses relational modeling to analyze the relationships between the identified entities, for example, recognizing the relationship "Company A invests in AI technology."

[0947] Step 7:

[0948] The server generates summaries based on the analysis results in predefined formats, such as company overviews, transaction summaries, etc.

[0949] Step 8:

[0950] The server stores the generated summaries in a database in JSON or XML format, with a timestamp attached to the data, allowing users to distinguish between new and old information.

[0951] Step 9:

[0952] The device provides an interface for web and mobile applications that allow users to enter and search for information.

[0953] Step 10:

[0954] A user searches for information about a particular company or transaction through a terminal interface.

[0955] Step 11:

[0956] The device sends the search query entered by the user to the server, using an API request to send the query.

[0957] Step 12:

[0958] The server retrieves relevant abstract information from a database based on the received search query, and retrieves the most relevant and up-to-date abstract information.

[0959] Step 13:

[0960] The server sends a summary of the search results to the device, often in the form of a JSON response.

[0961] Step 14:

[0962] The terminal displays the received summary information on a user interface in a visually easy-to-read format, such as a card format or a table format.

[0963] Example 1

[0964] 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."

[0965] With the advancement of the information society, a huge amount of data is being generated from official websites of companies and organizations, news sources, and industry databases. However, there is a lack of an automated system to efficiently collect, analyze, summarize, and provide this data. Because accessing the information many users need is cumbersome, there is a need to obtain high-quality information quickly.

[0966] 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.

[0967] In this invention, the server includes means for collecting data from official websites, news platforms, and industry databases of target organizations and groups, means for using natural language processing (NLP) techniques to analyze the collected data, means for summarizing the analyzed data in a predefined format, means for storing the summarized data in a database and making it accessible to users, means for users to search for information on specific organizations and transactions through a user interface, and means for displaying search results on the user's terminal, thereby enabling efficient collection of large amounts of data and rapid provision of high-quality summarized information.

[0968] "Covered organizations and entities" refers to corporations, non-profit organizations, and other official entities that collect and analyze data.

[0969] "Official Website" refers to the online informational webpage officially operated by the organization or entity in question.

[0970] "News Platform" refers to an online platform operated by a news provider or media company to distribute the latest news and articles.

[0971] "Industry Database" refers to an online or offline information management system that collects, stores, and provides detailed data and information about a particular industry.

[0972] "Data collection methods" refers to the programs or technologies used to obtain information from the target website or database.

[0973] "Natural language processing (NLP) technology" refers to artificial intelligence technology that enables computers to understand, interpret, and generate human language.

[0974] "Means for analyzing data" refers to the programs and algorithms used to convert collected data into information and extract meaning.

[0975] "Summarization tools" refer to programs or technologies that extract important information from the analyzed data and summarize it concisely.

[0976] A "database" refers to a collection of information that is structured so that data can be efficiently managed and searched.

[0977] "User interface" refers to the screens and applications through which a user interacts with a system and inputs or obtains information.

[0978] "Information about a specific entity or transaction" refers to detailed data, news, and other relevant information about a specific entity or transaction.

[0979] "Terminal" refers to a device such as a computer, smartphone, or tablet that a user uses to access the system.

[0980] This invention is an automatic information summarization system that automatically collects data from official websites of target organizations and groups, news platforms, and industry databases, analyzes the data, generates summaries, and makes them available to users efficiently. To implement this system, the following hardware and software are used.

[0981] Hardware used

[0982] 1. Server: A central computer with high computing power and large storage capacity for collecting, analyzing, summarizing, and storing data.

[0983] 2. Device: The device a user uses to enter information and receive search results. This can include a computer, smartphone, tablet, etc.

[0984] Software used

[0985] 1. Crawl program: Software used to automatically collect data from target websites and databases.

[0986] 2. Natural Language Processing (NLP) libraries: For example, use Python's "spaCy" or "NLTK" to analyze the collected text data.

[0987] 3. Database management system: A system for storing and managing collected and analyzed data. Examples include "MySQL" and "MongoDB."

[0988] 4. User interface platform: A framework for building web and mobile applications. Examples include "React" and "Flutter."

[0989] System processing procedure

[0990] Data collection

[0991] The server sets the URLs to access the official websites, news platforms, and industry databases of the target organizations and groups. It then crawls these URLs and retrieves data in HTML or PDF format. For example, the server sets the URL "https: / / example.com / company" to collect data on the relevant companies.

[0992] Data Preprocessing

[0993] The server parses the collected HTML and PDF documents and imports the necessary information into the database. For example, it extracts the "About Us" section through HTML parsing, converts it to JSON format, and stores it. It also performs data cleaning to remove invalid data and noise.

[0994] Data analysis

[0995] The server uses natural language processing (NLP) techniques to analyze the collected text data. For example, it uses the Python library "spaCy" to tokenize the text and tag parts of speech. It also uses Named Entity Recognition (NER) to identify important entities such as company names, people's names, and dates, and uses relational modeling to recognize relationships between the identified entities.

[0996] Summary Generation

[0997] Based on the analysis results, the server summarizes the company profile and transaction overview in a predefined format. For example, it generates a summary such as "Company profile: The target company is primarily engaged in software development, with approximately 1,500 employees." It then saves the summary in JSON or XML format.

[0998] Providing information

[0999] The terminal provides an interface that allows users to input and search for information about specific organizations or transactions. Users input search queries through the interface to request information, such as "Please show me an overview of the transactions of the target company." The server retrieves the relevant information from the summary database and sends it to the terminal. The terminal then displays the retrieved information to the user in a visually viewable format.

[1000] Usage example

[1001] Examples of prompts to input into the generative AI model include:

[1002] 1. "Please provide the latest financial information for the target company."

[1003] 2. "I would like to know about the medium-term management plan for fiscal 2023."

[1004] 3. "What are your investment plans for AI technology?"

[1005] This allows users to quickly obtain high-quality information and improve work efficiency.

[1006] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1007] Step 1: Data collection

[1008] The server sets up access URLs to the target organization's official website, news platform, and industry database.

[1009] Input: URL list, e.g. "https: / / example.com / company"

[1010] Output: URLs of websites and databases to be collected

[1011] Specific operation: The server accesses the specified URL and downloads the web page or PDF document.

[1012] Step 2: Data extraction

[1013] The server parses the collected HTML and PDF documents and extracts the necessary information.

[1014] Input: Collected HTML and PDF data

[1015] Output: Extracted text data

[1016] What it does: The server uses HTML and PDF parsing tools to extract specific information in text format, such as the "About Us" and "Press Releases" sections.

[1017] Step 3: Data cleaning

[1018] The server cleans the extracted data to remove invalid data and noise.

[1019] Input: Extracted text data

[1020] Output: Cleaned data

[1021] What it does: The server uses regular expressions and machine learning models to filter out ads and irrelevant text.

[1022] Step 4: Data analysis

[1023] The server analyzes the cleaned data using natural language processing (NLP) techniques.

[1024] Input: Cleaned data

[1025] Output: Parsed text data

[1026] What it does: The server uses an NLP library (e.g., spaCy) to tokenize the text, tag it as a part of speech, and use Named Entity Recognition (NER) to identify significant entities.

[1027] Step 5: Relational Modeling

[1028] The server performs relational modeling based on the analyzed data and recognizes the relationships between entities.

[1029] Input: Parsed text data and entities

[1030] Output: Relationships between entities

[1031] What happens: The server models transitive relationships and identifies relationships such as "Organization A started project X in 2023."

[1032] Step 6: Summary generation

[1033] The server generates a summary in a predefined format based on the results of the relational modeling.

[1034] Input: Entities and their relationships

[1035] Output: Summary

[1036] Specific operation: The server uses a summary generation algorithm to generate a summary sentence such as, "Organization A announced investment in AI technology in its 2032 mid-term plan."

[1037] Step 7: Save Data

[1038] The server stores the generated summaries in a database in JSON or XML format.

[1039] Input: Generated summary

[1040] Output: Summary data stored in a database

[1041] Specific operation: The server uses a database management system (e.g., MySQL or MongoDB) to store the summary data in an appropriate format.

[1042] Step 8: Information Search

[1043] The terminal provides an interface for the user to search for information.

[1044] Input: User's search query

[1045] Output: Display of the user interface

[1046] What it does: The device provides a search box and filters through the web or mobile application.

[1047] Step 9: Run a search

[1048] The server searches the database based on the user's search query and retrieves the relevant summary information.

[1049] Input: User's search query

[1050] Output: Summary information obtained

[1051] Specific operation: The server extracts the relevant summary information from the database using an SQL query or the like.

[1052] Step 10: View the results

[1053] The terminal displays the retrieved summary information to the user in a visually viewable format.

[1054] Input: Retrieved summary information

[1055] Output: User-visible search results

[1056] What it does: The device displays search results in card or list format, allowing users to easily browse information.

[1057] As an example, when a user enters a prompt such as "Please provide the latest financial information of the target company," the system automatically performs a series of processes from data collection to displaying the results, and provides the user with the relevant information.

[1058] (Application example 1)

[1059] 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."

[1060] In recent years, with the rapid release of large amounts of information about security risks and vulnerabilities, it has become increasingly important for organizational and individual security personnel to efficiently collect, analyze, and implement appropriate countermeasures. However, manually collecting and analyzing this information requires a huge amount of time and effort, making it difficult to respond in a timely manner. Therefore, there is a need for a system that can collect and analyze security information in real time and provide summaries.

[1061] 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.

[1062] In this invention, the server includes means for collecting data from official websites of target organizations and groups, news sources, and industry databases, means for analyzing the collected text data using natural language processing (NLP) technology, means for summarizing the analyzed data in a predefined format, means for storing the summarized data in a database and making it accessible to users, means for users to search for information about specific organizations and transactions through a user interface, means for displaying search results on the user's terminal, and means for collecting and analyzing security information and providing summaries in real time, thereby enabling security personnel to quickly obtain high-quality security information and take appropriate measures.

[1063] "Organizations and groups" refers to companies, public institutions, non-profit organizations, etc. of various sizes and types.

[1064] "Official website" refers to an information dissemination page on the Internet that is officially operated and provided by an organization or group.

[1065] "News source" refers to the source of news articles and news programs provided by newspapers, news sites, television stations, etc.

[1066] "Industry Database" means a system or service that collects and stores data and information related to a particular industry.

[1067] "Means of collecting data" refers to technologies and devices used to obtain information, such as web scraping and API access.

[1068] "Natural Language Processing (NLP)" refers to the science and technology for analyzing, understanding, and generating human language.

[1069] "Means of analysis" refers to the software or algorithms used to analyze the collected text data.

[1070] "Summarization tools" refers to techniques or devices used to summarize key points from collected and analyzed data.

[1071] "Database" refers to a system for efficiently storing, retrieving, updating, and managing data.

[1072] "User interface" refers to the screens and applications through which users enter information and receive search results.

[1073] "User terminal" refers to a device such as a desktop computer, laptop, tablet, or smartphone that a user uses to connect to the Internet and obtain information.

[1074] "Security information" refers to information regarding vulnerabilities, threats, and risks of systems and networks.

[1075] "Real-time" refers to processing and providing information with extremely short delays.

[1076] This invention is a system for quickly collecting, analyzing, summarizing, and providing security-related information to users. This system is centered around a server and uses the following hardware and software:

[1077] Program Overview

[1078] 1. Data Collection

[1079] The server accesses the official websites of the organizations and organisations in question, news sources, and industry databases to retrieve information, using the Requests library to retrieve information from the network and BeautifulSoup to parse the HTML.

[1080] 2. Data Analysis

[1081] The server analyzes the collected text data using natural language processing (NLP) techniques, tokenizing the text data and extracting entities using the SpaCy library, and summarizing the text using Transformers models.

[1082] 3. Data Summary

[1083] Based on the analysis results, the server summarizes the information in a predefined format, and the summarized data is stored in a database in JSON or XML format.

[1084] 4. Information provision

[1085] The user's device can search for information about a specific organization or transaction through a user interface. When the user enters a search query, the server retrieves relevant summary data from the database and sends it to the device.

[1086] Specific example explanation

[1087] For example, if a user inputs a prompt such as "Show me information about the latest security risks," the server will generate a summary of the latest vulnerabilities and risks from the collected security information and display it on the terminal.

[1088] Hardware and Software Used

[1089] Server: A central device with high-performance processing power for data collection and analysis.

[1090] Requests library: Used to retrieve data from the web.

[1091] BeautifulSoup: Used for HTML parsing.

[1092] SpaCy library: Used for natural language processing.

[1093] Transformers library: Used for generating summaries.

[1094] Database: A system that stores data in JSON or XML format.

[1095] User devices: desktop computers, laptops, tablets, smartphones.

[1096] Prompt Sentence Examples

[1097] "Show me information about the latest security risks"

[1098] "Give me a summary of the latest vulnerability news."

[1099] By using this system, security personnel can quickly obtain high-quality information and take countermeasures efficiently.

[1100] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1101] Step 1:

[1102] The server collects data from official websites of organizations and groups of interest, news sources, and industry databases. Specifically, it uses the Requests library to access these sources and retrieve data in HTML or text format. The input to this step is a list of URLs for each source, and the output is the raw data retrieved.

[1103] Step 2:

[1104] The server uses BeautifulSoup to parse the raw data and extract the necessary information. For example, it extracts the article title, publication date, and body of the article through HTML parsing. The input to this step is the raw data obtained in step 1, and the output is the extracted structured data.

[1105] Step 3:

[1106] The server uses natural language processing (NLP) techniques to parse the extracted structured data. Specifically, it uses the SpaCy library to tokenize the text and extract entities (e.g., vulnerabilities, risks, organization names, etc.). The input of this step is the structured data obtained in step 2, and the output is the parsed data containing entity information.

[1107] Step 4:

[1108] The server uses Transformers to summarize the parsed data. For example, it extracts important parts from the original text and creates a short summary. The input to this step is the parsed data obtained in step 3, and the output is a summary sentence.

[1109] Step 5:

[1110] The server stores the abstract in a database in JSON or XML format. The input of this step is the abstract generated in step 4, and the output is the stored structured data.

[1111] Step 6:

[1112] A user utilizes a terminal to search for information about a particular organization or transaction through a user interface, for example, by entering a prompt phrase such as "Show me information about the latest security risks." The input to this step is the user's search query, and the output is a search request.

[1113] Step 7:

[1114] The server receives the search request and retrieves the relevant summary data from the database. The input to this step is the search query received in step 6, and the output is the search results.

[1115] Step 8:

[1116] The server sends the search results to the user's terminal. The input of this step is the search results obtained in step 7, and the output is the data sent to the terminal.

[1117] Step 9:

[1118] The terminal displays the received data on a user interface, for example visually displaying the summarized data in the form of cards. The input of this step is the data received in step 8, and the output is the displayed information.

[1119] 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.

[1120] The present invention relates to a system for collecting data related to a company or organization, analyzing and summarizing the data, and providing customized information by recognizing user emotions. The system includes a data collection means, a natural language processing (NLP) means, a summary generation means, a database storage means, a user interface, a search means, a display means, and an emotion engine.

[1121] Explanation of program processing

[1122] 1. Data Collection

[1123] The server prepares a list of URLs to access official company and organization websites, news sources, and industry databases.

[1124] The server crawls the URL list to obtain HTML and PDF data, and then uses scraping technology to extract necessary information such as company profiles, press releases, and financial information.

[1125] 2. Data Analysis

[1126] The server analyzes the collected data using natural language processing (NLP) techniques, tokenizing the text and tagging it by part of speech.

[1127] The server utilizes Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates.

[1128] The server uses relational modeling to analyze the relationships between entities.

[1129] 3. Summary Generation

[1130] The server generates a summary in a predefined format based on the analysis results, including a company profile and transaction overview.

[1131] The server stores the generated summaries in a database in JSON or XML format.

[1132] 4. User Interface

[1133] The device provides an interface through which the user can enter a search query, either through a web application or a mobile application.

[1134] A user uses the device interface to search for information about a particular company or transaction.

[1135] 5. Search and Display

[1136] The device sends the search query entered by the user to the server via an API request.

[1137] The server searches and retrieves relevant summary information from a database.

[1138] The terminal displays the search results on a user interface in a visually viewable format.

[1139] 6. Use of Emotion Engine

[1140] The server uses an emotion engine to parse emotions from the user's text input. For example, if the user inputs "I'm disappointed with my recent transaction," the emotion engine recognizes the emotion "disappointed."

[1141] The server provides search results and customized feedback that match the user's mood based on the recognized emotions.

[1142] Based on the results of the emotion engine, the device displays appropriate feedback, such as "We suggest improvements for this transaction."

[1143] Specific examples

[1144] 1. Data Collection

[1145] The server collects data from the official website of "Reisha Co., Ltd.", industry news sites, and industry-specific databases. The data includes official announcements, industry trend reports, etc.

[1146] 2. Data Analysis

[1147] The server tokenizes the collected text data, tags it with parts of speech, and uses NER to identify entities such as "example company," "AI technology," and "investment amount."

[1148] 3. Summary Generation

[1149] The server generates the following summary based on the analysis results:

[1150] Company profile: Reisha Co., Ltd. is primarily engaged in software development and has approximately 1,500 employees.

[1151] Transaction Overview: The latest mid-term plan calls for a significant increase in investment in AI technology.

[1152] 4. User Interface

[1153] A user enters a search query such as "I want to see an overview of transactions for example companies."

[1154] The server retrieves the latest information about the sample company from the summary database and transmits the retrieved data to the terminal.

[1155] The terminal displays the acquired summary information in a card format on the user interface.

[1156] 5. Use of Emotion Engine

[1157] The user enters a comment saying, "I am very satisfied with the company's recent performance."

[1158] The server uses an emotion engine to recognize the emotion "satisfaction" and provides the user with positive feedback such as "We'll also introduce you to some new investment opportunities that may interest you."

[1159] The device will then display appropriate feedback or additional information based on the recognized emotion.

[1160] This system not only enables users to quickly obtain high-quality corporate information, but also allows them to more efficiently gather information and prepare for business negotiations by receiving customized feedback tailored to their emotional state.

[1161] The processing flow will be explained below.

[1162] Step 1:

[1163] The server prepares a pre-configured list of URLs for official company websites, news sites, and industry databases to be collected.

[1164] Step 2:

[1165] The server sends an HTTP request to each website based on the URL list to retrieve HTML content or PDF files.

[1166] Step 3:

[1167] The server parses the HTML content and PDF files and uses scraping technology to extract important data such as company profiles, press releases, and financial information.

[1168] Step 4:

[1169] The server then uses natural language processing (NLP) technology to tokenize the extracted text data, breaking it down into sentences and words.

[1170] Step 5:

[1171] The server performs part-of-speech tagging on the tokenized data, which identifies the part of speech (noun, verb, adjective, etc.) of each word.

[1172] Step 6:

[1173] The server uses Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates.

[1174] Step 7:

[1175] The server uses relational modeling techniques to analyze the relationships between the identified entities, e.g., to clarify the relationship between Company A's investment in AI technology.

[1176] Step 8:

[1177] The server generates a summary based on the analysis results in a predefined format, which includes information such as company overview and transaction summary.

[1178] Step 9:

[1179] The server stores the generated summaries in a database in JSON or XML format, with a timestamp attached to distinguish between new and old data.

[1180] Step 10:

[1181] The terminal provides an interface through which the user enters a search query via a web application or a mobile application.

[1182] Step 11:

[1183] The user enters information about a particular company or transaction into the terminal interface and presses the search button.

[1184] Step 12:

[1185] The terminal sends the search query entered by the user to the server in the form of an API request.

[1186] Step 13:

[1187] The server retrieves relevant summary information from the database based on the received search query, retrieving the most recent and relevant data.

[1188] Step 14:

[1189] The server sends summary information of the search results in JSON format to the terminal.

[1190] Step 15:

[1191] The terminal visually displays the received summary information on the user interface in an easy-to-read format such as cards or tables.

[1192] Step 16:

[1193] The server analyzes the user's text input using an emotion engine that recognizes emotions (e.g., joy, sadness, disappointment, etc.) from the user's input text.

[1194] Step 17:

[1195] The server customizes search results and feedback based on the user's emotions, as recognized by the emotion engine. For example, if a user expresses "disappointment," the server suggests related topics and solutions.

[1196] Step 18:

[1197] The device presents appropriate feedback to the user based on the results of the emotion engine, allowing the user to receive information according to their emotions.

[1198] Example 2

[1199] 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."

[1200] Conventional corporate information gathering systems have made it difficult for users to quickly and accurately obtain the information they need. Furthermore, they lack the ability to analyze and summarize the collected information, and provide customized information based on the user's feelings, resulting in issues with the usefulness of the information and ease of access. As a result, efficiency in preparing for business negotiations and making decisions has decreased, and gathering information has taken a lot of time.

[1201] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1202] In this invention, the server includes: means for collecting data from official websites, information sources, and industry databases of target organizations; means for analyzing the collected text data using natural language processing technology; means for summarizing the analyzed data in a predefined format; means for storing the summarized data in a database and making it accessible to users; means for users to search for information on specific organizations or transactions through a user interface; means for displaying search results on the user's terminal; and means including an emotion engine for analyzing user emotions from text input and providing customized information. This allows users to not only quickly obtain high-quality corporate information but also receive customized feedback tailored to their emotional state.

[1203] "Covered Entity" means the company, association, or other legal entity identified as the subject of data collection, analysis, or abstraction.

[1204] "Sources" refers to websites and databases that contain information related to an organization, such as news sites, industry reports, and social media.

[1205] "Industry database" refers to an online database that centrally manages and provides data related to a specific industry.

[1206] "Data collection methods" refers to the techniques and methods used to obtain the desired data from websites and databases, such as using web crawlers or API requests.

[1207] "Natural language processing technology" refers to machine learning algorithms and text analysis technologies for analyzing and understanding human language.

[1208] "Summarization means" refers to algorithms or methods for succinctly presenting collected and analyzed data as a summary statement.

[1209] "Means of database storage" refers to the techniques and methods used to store data in a structured format in a database to facilitate search and retrieval.

[1210] "User Interface" refers to the interactive screen or application through which a user interacts with a system, inputs information, and obtains results.

[1211] "Terminal" refers to a device such as a computer or smartphone that allows a user to input data and display results.

[1212] An "emotion engine" refers to technology or algorithms that analyze a user's emotional state from text and provide appropriate feedback based on the results.

[1213] "Relational modeling technology" refers to a data analysis technique for analyzing the relationships between entities and visualizing them as a network.

[1214] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a lightweight data exchange format for structuring and storing data.

[1215] "XML format" is an abbreviation for eXtensible Markup Language, and refers to a general-purpose markup language for hierarchically structuring and storing data.

[1216] The present invention is a system that collects information about organizations and groups, analyzes and summarizes it, and further recognizes the user's emotions to provide customized information. This system consists of a server, a terminal, and a user. The roles and operations of each component are described in detail below.

[1217] server

[1218] 1. Data Collection

[1219] The server collects data from official websites, information sources, and industry databases. Specifically, it uses a web crawler such as Selenium to access a specified list of URLs and retrieves data in HTML or PDF format. From the retrieved data, it extracts necessary information such as company profiles, press releases, and financial information using tools such as BeautifulSoup and PyPDF2.

[1220] 2. Data Analysis

[1221] The server analyzes the collected text data using natural language processing techniques. For example, it uses libraries such as spaCy and NLTK to tokenize the text data and tag parts of speech. It also uses Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates. It then analyzes the relationships between entities using relational modeling tools such as NetworkX.

[1222] 3. Summary Generation

[1223] The server generates a summary in a predefined format based on the analysis results. This summary includes a company profile and a business overview. For example, it generates a summary statement in the following format: "Reisha Co., Ltd. is primarily engaged in software development and has approximately 1,500 employees. Its latest medium-term plan calls for a significant increase in investment in AI technology." The generated summary is stored in a database in JSON or XML format.

[1224] 4. Use of Emotion Engine

[1225] The server uses an emotion engine to analyze emotions from the user's text input. For example, if a user enters a comment such as "I'm very satisfied with the recent performance of the company," the emotion engine recognizes the emotion as "satisfied." Based on the results, the server generates customized feedback and provides it in the form of, for example, "We'll also introduce you to some new investment opportunities that may be of interest to you."

[1226] Terminal

[1227] 1. Interface provision

[1228] The terminal provides an interface, including a web application or a mobile application, through which a user can enter a search query. For example, the terminal may provide an interface through which a user can enter a query such as "Show me a trading summary for company X."

[1229] 2. Search and Display

[1230] The device sends the search query entered by the user to the server and visually displays the search results received from the server. The search results are displayed in a card format, providing a concise and visually easy-to-understand format.

[1231] User

[1232] 1. Information Search

[1233] Users use the device interface to search for information about a particular organization or transaction, for example, by entering a search query such as "Show me an overview of transactions for this company."

[1234] Specific examples

[1235] The server collects data from the official website of "Example Company," industry news sites, and industry-specific databases. For example, it obtains and analyzes data such as official announcements and industry trend reports to generate a summary such as "Example Company announced plans to invest 50 billion yen in AI technology." If a user inputs "I would like to see an overview of Example Company's transactions," this summary is displayed on the device. Furthermore, if a user inputs a comment such as "I am very satisfied with Example Company's recent performance," the emotion engine recognizes that emotion and provides feedback such as "I would also like to introduce some new investment opportunities that may be of interest to you."

[1236] Example prompt for a generative AI model:

[1237] Could you please provide the latest trading information for Example Co., Ltd.? Also, could you please provide a system that analyzes the sentiment of user reviews about recent business performance and provides positive feedback?

[1238] In this way, the present invention enables the rapid acquisition of high-quality corporate information and improves the efficiency of information gathering and decision-making by providing customized feedback that is tailored to the user's emotions.

[1239] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1240] Step 1:

[1241] Data collection

[1242] 1. The server prepares a list of URLs to collect data from official websites of organizations, information sources, and industry databases. The input is the URL list, and the server sets up a web crawler based on it.

[1243] 2. The server launches a web crawler using Selenium to retrieve data in HTML or PDF format from the specified URL list, and the output is the retrieved raw web data.

[1244] 3. The server uses BeautifulSoup and PyPDF2 to parse the HTML or PDF data and extract the necessary information, such as company profiles, press releases, financial information, etc. The input is raw web data, and the output is extracted text data.

[1245] Step 2:

[1246] Data analysis

[1247] 1. The server analyzes the collected text data using natural language processing technology. The input is the extracted text data, and the output is the analyzed text data.

[1248] 2. The server uses spaCy and NLTK to tokenize the text data and tag it with parts of speech. Specifically, it splits sentences such as "Reisha Co., Ltd. invests in AI technology" into words and tags each word with a part of speech.

[1249] 3. The server uses Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates. The input is tokenized text data, and the output is text data with identified entities.

[1250] 4. The server uses NetworkX to analyze the relationships between entities using relational modeling techniques. The input is text data with identified entities, and the output is data with analyzed relationships.

[1251] Step 3:

[1252] Summary Generation

[1253] 1. The server generates a summary in a predefined format based on the analysis results. The input is the analyzed text data, and the output is the generated summary sentence.

[1254] 2. The server saves the generated summary in a database in JSON or XML format. The input is the generated summary sentence, and the output is the summary data saved in the database.

[1255] Step 4:

[1256] User Search Interface

[1257] 1. The device provides an interface where the user can enter a search query. Specifically, it provides a web page or mobile application that includes a search bar and a search button. The input is the user's search query, and the output is an API request from the device to the server.

[1258] 2. The user enters a query about a specific organization or transaction, such as "Show me an overview of transactions for this company."

[1259] Step 5:

[1260] Search and Display

[1261] 1. The device sends the search query entered by the user to the server. The input is the user's search query, and the output is an API request to the server.

[1262] 2. The server searches the database for summary information related to the user's query and retrieves it in JSON format. The input is the API request to the server, and the output is the retrieved summary information.

[1263] 3. The terminal visually displays the acquired summary information. Specifically, it presents the information in a card format. The input is summary information in JSON format, and the output is summary information displayed on the user interface.

[1264] Step 6:

[1265] Using the Emotion Engine

[1266] 1. The server sends the user's text input to the emotion engine for emotion analysis. The input is the user's text comment, and the output is the analyzed emotion data.

[1267] 2. The server generates customized feedback based on the analysis results of the emotion engine. For example, if the analyzed emotion is "satisfied," it generates feedback such as "We'll also introduce you to some new investment opportunities that may interest you." The input is the analyzed emotion data, and the output is the generated feedback.

[1268] 3. The terminal displays the feedback received from the server on the user interface. The input is the feedback from the server, and the output is the feedback displayed on the user interface.

[1269] (Application example 2)

[1270] 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."

[1271] In modern society, the rapid increase in the amount of information is making it difficult for users to quickly and efficiently obtain the information they need. There is also a growing need for personalized information provision, and there is also a demand for information that is customized based on the user's emotions. However, current systems do not adequately provide information that meets these needs. Therefore, it is necessary to provide a system that allows users to efficiently search for specific information and obtain information that matches their emotional state.

[1272] 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.

[1273] In this invention, the server includes means for collecting data from official websites, information sources, and industry databases of target organizations and groups, means for analyzing the collected text data using natural language processing technology, means for summarizing the analyzed data in a predefined format, means for storing the summarized data in a database and making it accessible to users, means for users to search for information on specific organizations and transactions through a user interface, means for displaying search results on the user's terminal, and means for analyzing emotions from text entered by the user and customizing search results based on the emotions, thereby enabling users to not only quickly obtain high-quality information but also receive information customized based on their emotions.

[1274] An "organization" is a group formed to achieve a specific purpose, and includes companies, groups, institutions, etc.

[1275] A "group" is a collection of people or things with a common purpose or interest.

[1276] A "source" is a medium or platform that provides information such as news, data, and statistics.

[1277] An "industry database" is a database that collects and manages information related to a specific industry.

[1278] "Data collection" is the process of gathering data needed for a specific purpose.

[1279] "Natural language processing technology" is a technology that enables computers to understand and process human language, and includes language analysis, sentiment analysis, summary generation, and more.

[1280] "Analysis" is the process of understanding the content of data and extracting meaning from it.

[1281] A "summary" is a shortened version of text or information that extracts only the main points.

[1282] A "database" is a system that centrally manages and stores data and allows quick access when needed.

[1283] "User interface" refers to the screens and operating methods that users use to interact with the system.

[1284] "Searching" is the process of searching a database or the Internet to find specific information.

[1285] A "terminal" is a hardware device that allows a user to enter information and display results.

[1286] "Emotion analysis" is a technology that extracts and understands user emotions from text, voice, etc.

[1287] "Customization" means adjusting functionality and display content to suit a user's specific needs and preferences.

[1288] This invention is a system that uses a server and a terminal to allow a user to efficiently collect information about an organization or group and provide customized information based on that information. This system includes the following processes.

[1289] First, the server collects data from the target organization's or group's official website, information sources, and industry databases using web scraping techniques such as the requests library and BeautifulSoup, and saves the collected data in text format.

[1290] The collected data is then analyzed on the server using natural language processing techniques, specifically using NLP libraries such as Spacy to tokenize the text data, tag parts of speech, and perform entity recognition, and sentiment analysis using a pipeline of Transformers libraries.

[1291] The parsed data is summarized in a predefined format, including key points and entity information, and stored in a database in JSON or XML format.

[1292] When a user searches for information about a particular organization or transaction, they enter a search query through a user interface, which can be a web or mobile application.

[1293] The server retrieves relevant summary information from a database based on the input search query and sends it to the terminal, which displays the information on a user interface.

[1294] Furthermore, when a user inputs their thoughts or opinions through their device, the server-side sentiment analysis engine analyzes them and customizes search results based on the user's emotions. For example, if a user inputs a sentiment such as "I'm satisfied with my recent transaction," the server will recognize the emotion "satisfied" and provide the user with additional, more positive information.

[1295] As a specific example, if a user responds to the prompt, "Please tell us how you felt after reading recent news about AI technology," by typing, "I'm very satisfied with the recent progress in AI technology. I'd like to hear other positive news," the server will analyze this sentiment and provide additional positive news.

[1296] This system allows users to efficiently obtain information of interest and also receive customized information based on their own emotions.

[1297] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1298] Step 1:

[1299] The server collects data from the official websites of target organizations and groups, information sources, and industry databases. It receives a list of URLs and a search query as input, which determines the content of the web pages to collect. It issues HTTP requests using the requests library to retrieve HTML or PDF data from the specified websites. It uses BeautifulSoup to extract the necessary text information from the retrieved data. The extracted text information is output and passed to the next analysis step.

[1300] Step 2:

[1301] The server analyzes the collected text data using natural language processing technology. It receives the text information extracted in step 1 as input. It uses an NLP library such as Spacy to tokenize the text data and perform part-of-speech tagging and entity recognition. This identifies entities such as nouns, verbs, company names, and dates in the text, and outputs the relationships between these entities and the text.

[1302] Step 3:

[1303] The server summarizes the analyzed data into a predefined format. It receives as input the analysis results (entities and relationships) obtained in step 2. It uses a summary generation algorithm to generate a shortened text that includes key points and entity information. The generated summary text is output in a format (JSON or XML) that can be stored in the database.

[1304] Step 4:

[1305] The server stores the summarized data in a database and makes it accessible to users. As input, it receives the summary text generated in step 3. The server stores this in the database in the specified format (JSON or XML). The stored data can be later used for searching and display.

[1306] Step 5:

[1307] A user inputs a search query for information about a specific organization or transaction into the user interface via a terminal. The user inputs a specific query into the interface using a keyboard or mouse. The query is sent from the terminal to the server. The server analyzes the query, searches the database for relevant summary information, and outputs the search results.

[1308] Step 6:

[1309] The server sends the search results to the terminal, which displays them on its user interface. As input, it receives the search results obtained in step 5. The terminal visually displays the search results using HTML and CSS, allowing the user to view the results on the screen.

[1310] Step 7:

[1311] The user inputs their thoughts and opinions through the terminal. The user inputs their thoughts and opinions in the text box and presses the send button. This input data is sent from the terminal to the server.

[1312] Step 8:

[1313] The server uses a sentiment analysis engine to analyze the sentiment from the text entered by the user. As input, it receives the user's text data obtained in step 7. Using a pipeline of the Transformers library, it classifies the sentiment in the text into categories such as positive or negative. The analyzed sentiment is obtained as output.

[1314] Step 9:

[1315] The server customizes search results based on the analyzed emotions and provides them to the user. As input, it receives the emotion analysis results obtained in step 8. If the emotion is positive, it searches for and selects additional information containing positive content and adds it to the search results. The customized search results are output and sent back to the terminal. The user can receive information that suits their emotions.

[1316] As a specific example of how it works, if the server displays a prompt on the device saying, "Please tell us how you felt after reading recent news about AI technology," and the user enters, "I'm very satisfied with the recent progress in AI technology. I'd like to hear other positive news," the server will recognize this emotion as "POSITIVE" and provide additional positive news.

[1317] 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.

[1318] 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.

[1319] 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.

[1320] [Fourth embodiment]

[1321] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1322] 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.

[1323] 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).

[1324] 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.

[1325] 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.

[1326] 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).

[1327] 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.

[1328] 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.

[1329] 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.

[1330] 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.

[1331] 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.

[1332] 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.

[1333] 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."

[1334] The present invention provides an automated information summarization system that collects data from official websites of target companies and organizations, news sources, and industry databases, analyzes and summarizes the data, and makes it available to users efficiently. The following means and methods are included to implement this system:

[1335] Explanation of program processing

[1336] 1. Data Collection

[1337] The server sets up URLs to access the target company's official website, news articles, and industry databases.

[1338] The server crawls each URL and retrieves the data in HTML or PDF format.

[1339] The server uses scraping technology to extract necessary information from web pages and documents, such as company profiles, financial information, and press releases.

[1340] 2. Data Analysis

[1341] The server analyzes the collected data using natural language processing (NLP) technology.

[1342] The server tokenizes the text, tags it with parts of speech, and uses Named Entity Recognition (NER) to identify important entities such as company names, people's names, and dates.

[1343] The server uses relational modeling to recognize the relationships between the identified entities.

[1344] 3. Summary Generation

[1345] Based on the analysis results, the server summarizes the company profile and transaction overview in a predefined format.

[1346] The server stores the generated summary as structured data (JSON or XML format).

[1347] 4. User Interface

[1348] The terminal provides a web or mobile application interface that allows users to input and search information about specific companies or transactions.

[1349] A user enters a search query through the interface to request information.

[1350] The server searches the database based on the input query and retrieves the relevant summary information.

[1351] The terminal displays the acquired summary information to the user in a visually viewable format.

[1352] Specific examples

[1353] 1. Data Collection

[1354] The server collects data from the official Temple Co., Ltd. website, industry news sites, and industry-specific databases.

[1355] The server extracts "Temple's latest press release" through HTML analysis and extracts "2023 Medium-Term Management Plan" using PDF analysis.

[1356] 2. Data Analysis

[1357] The server tokenizes the extracted text data and tags each word with a part of speech tag.

[1358] The server uses NER to identify entities such as "Temple," "AI technology," and "investment amount" and analyze the relationships between them.

[1359] 3. Summary Generation

[1360] The server generates the following summary based on the analysis results:

[1361] Company Overview: Temple is primarily engaged in software development and has approximately 1,500 employees.

[1362] Transaction Overview: In its medium-term management plan for fiscal 2023, the company announced that it will significantly expand investment in AI technology.

[1363] 4. User Interface

[1364] A user enters a search query: "Show me Temple's trading summary."

[1365] The server searches the abstract database for the latest information about "Temple Company" and transmits the retrieved data to the terminal.

[1366] The terminal displays the acquired summary information in a card format on the user interface, allowing the user to easily view it.

[1367] This system allows users to quickly obtain high-quality corporate information, enabling them to efficiently create account plans and prepare for business negotiations.

[1368] The processing flow will be explained below.

[1369] Step 1:

[1370] The server prepares a list of URLs for the official websites of the companies from which data is collected, as well as URLs for news sources and industry databases.

[1371] Step 2:

[1372] The server accesses websites and news sites according to the URL list, using a crawler to retrieve HTML and PDF data.

[1373] Step 3:

[1374] The server scrapes the acquired HTML and PDF data, extracting necessary information such as company profiles, press releases, and financial information.

[1375] Step 4:

[1376] The server uses natural language processing (NLP) technology to analyze the extracted text data, specifically tokenizing and tagging parts of speech.

[1377] Step 5:

[1378] The server uses Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates.

[1379] Step 6:

[1380] The server uses relational modeling to analyze the relationships between the identified entities, for example, recognizing the relationship "Company A invests in AI technology."

[1381] Step 7:

[1382] The server generates summaries based on the analysis results in predefined formats, such as company overviews, transaction summaries, etc.

[1383] Step 8:

[1384] The server stores the generated summaries in a database in JSON or XML format, with a timestamp attached to the data, allowing users to distinguish between new and old information.

[1385] Step 9:

[1386] The device provides an interface for web and mobile applications that allow users to enter and search for information.

[1387] Step 10:

[1388] A user searches for information about a particular company or transaction through a terminal interface.

[1389] Step 11:

[1390] The device sends the search query entered by the user to the server, using an API request to send the query.

[1391] Step 12:

[1392] The server retrieves relevant abstract information from a database based on the received search query, and retrieves the most relevant and up-to-date abstract information.

[1393] Step 13:

[1394] The server sends a summary of the search results to the device, often in the form of a JSON response.

[1395] Step 14:

[1396] The terminal displays the received summary information on a user interface in a visually easy-to-read format, such as a card format or a table format.

[1397] Example 1

[1398] 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."

[1399] With the advancement of the information society, a huge amount of data is being generated from official websites of companies and organizations, news sources, and industry databases. However, there is a lack of an automated system to efficiently collect, analyze, summarize, and provide this data. Because accessing the information many users need is cumbersome, there is a need to obtain high-quality information quickly.

[1400] 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.

[1401] In this invention, the server includes means for collecting data from official websites, news platforms, and industry databases of target organizations and groups, means for using natural language processing (NLP) techniques to analyze the collected data, means for summarizing the analyzed data in a predefined format, means for storing the summarized data in a database and making it accessible to users, means for users to search for information on specific organizations and transactions through a user interface, and means for displaying search results on the user's terminal, thereby enabling efficient collection of large amounts of data and rapid provision of high-quality summarized information.

[1402] "Covered organizations and entities" refers to corporations, non-profit organizations, and other official entities that collect and analyze data.

[1403] "Official Website" refers to the online informational webpage officially operated by the organization or entity in question.

[1404] "News Platform" refers to an online platform operated by a news provider or media company to distribute the latest news and articles.

[1405] "Industry Database" refers to an online or offline information management system that collects, stores, and provides detailed data and information about a particular industry.

[1406] "Data collection methods" refers to the programs or technologies used to obtain information from the target website or database.

[1407] "Natural language processing (NLP) technology" refers to artificial intelligence technology that enables computers to understand, interpret, and generate human language.

[1408] "Means for analyzing data" refers to the programs and algorithms used to convert collected data into information and extract meaning.

[1409] "Summarization tools" refer to programs or technologies that extract important information from the analyzed data and summarize it concisely.

[1410] A "database" refers to a collection of information that is structured so that data can be efficiently managed and searched.

[1411] "User interface" refers to the screens and applications through which a user interacts with a system and inputs or obtains information.

[1412] "Information about a specific entity or transaction" refers to detailed data, news, and other relevant information about a specific entity or transaction.

[1413] "Terminal" refers to a device such as a computer, smartphone, or tablet that a user uses to access the system.

[1414] This invention is an automatic information summarization system that automatically collects data from official websites of target organizations and groups, news platforms, and industry databases, analyzes the data, generates summaries, and makes them available to users efficiently. To implement this system, the following hardware and software are used.

[1415] Hardware used

[1416] 1. Server: A central computer with high computing power and large storage capacity for collecting, analyzing, summarizing, and storing data.

[1417] 2. Device: The device a user uses to enter information and receive search results. This can include a computer, smartphone, tablet, etc.

[1418] Software used

[1419] 1. Crawl program: Software used to automatically collect data from target websites and databases.

[1420] 2. Natural Language Processing (NLP) libraries: For example, use Python's "spaCy" or "NLTK" to analyze the collected text data.

[1421] 3. Database management system: A system for storing and managing collected and analyzed data. Examples include "MySQL" and "MongoDB."

[1422] 4. User interface platform: A framework for building web and mobile applications. Examples include "React" and "Flutter."

[1423] System processing procedure

[1424] Data collection

[1425] The server sets the URLs to access the official websites, news platforms, and industry databases of the target organizations and groups. It then crawls these URLs and retrieves data in HTML or PDF format. For example, the server sets the URL "https: / / example.com / company" to collect data on the relevant companies.

[1426] Data Preprocessing

[1427] The server parses the collected HTML and PDF documents and imports the necessary information into the database. For example, it extracts the "About Us" section through HTML parsing, converts it to JSON format, and stores it. It also performs data cleaning to remove invalid data and noise.

[1428] Data analysis

[1429] The server uses natural language processing (NLP) techniques to analyze the collected text data. For example, it uses the Python library "spaCy" to tokenize the text and tag parts of speech. It also uses Named Entity Recognition (NER) to identify important entities such as company names, people's names, and dates, and uses relational modeling to recognize relationships between the identified entities.

[1430] Summary Generation

[1431] Based on the analysis results, the server summarizes the company profile and transaction overview in a predefined format. For example, it generates a summary such as "Company profile: The target company is primarily engaged in software development, with approximately 1,500 employees." It then saves the summary in JSON or XML format.

[1432] Providing information

[1433] The terminal provides an interface that allows users to input and search for information about specific organizations or transactions. Users input search queries through the interface to request information, such as "Please show me an overview of the transactions of the target company." The server retrieves the relevant information from the summary database and sends it to the terminal. The terminal then displays the retrieved information to the user in a visually viewable format.

[1434] Usage example

[1435] Examples of prompts to input into the generative AI model include:

[1436] 1. "Please provide the latest financial information for the target company."

[1437] 2. "I would like to know about the medium-term management plan for fiscal 2023."

[1438] 3. "What are your investment plans for AI technology?"

[1439] This allows users to quickly obtain high-quality information and improve work efficiency.

[1440] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1441] Step 1: Data collection

[1442] The server sets up access URLs to the target organization's official website, news platform, and industry database.

[1443] Input: URL list, e.g. "https: / / example.com / company"

[1444] Output: URLs of websites and databases to be collected

[1445] Specific operation: The server accesses the specified URL and downloads the web page or PDF document.

[1446] Step 2: Data extraction

[1447] The server parses the collected HTML and PDF documents and extracts the necessary information.

[1448] Input: Collected HTML and PDF data

[1449] Output: Extracted text data

[1450] What it does: The server uses HTML and PDF parsing tools to extract specific information in text format, such as the "About Us" and "Press Releases" sections.

[1451] Step 3: Data cleaning

[1452] The server cleans the extracted data to remove invalid data and noise.

[1453] Input: Extracted text data

[1454] Output: Cleaned data

[1455] What it does: The server uses regular expressions and machine learning models to filter out ads and irrelevant text.

[1456] Step 4: Data analysis

[1457] The server analyzes the cleaned data using natural language processing (NLP) techniques.

[1458] Input: Cleaned data

[1459] Output: Parsed text data

[1460] What it does: The server uses an NLP library (e.g., spaCy) to tokenize the text, tag it as a part of speech, and use Named Entity Recognition (NER) to identify significant entities.

[1461] Step 5: Relational Modeling

[1462] The server performs relational modeling based on the analyzed data and recognizes the relationships between entities.

[1463] Input: Parsed text data and entities

[1464] Output: Relationships between entities

[1465] What happens: The server models transitive relationships and identifies relationships such as "Organization A started project X in 2023."

[1466] Step 6: Summary generation

[1467] The server generates a summary in a predefined format based on the results of the relational modeling.

[1468] Input: Entities and their relationships

[1469] Output: Summary

[1470] Specific operation: The server uses a summary generation algorithm to generate a summary sentence such as, "Organization A announced investment in AI technology in its 2032 mid-term plan."

[1471] Step 7: Save Data

[1472] The server stores the generated summaries in a database in JSON or XML format.

[1473] Input: Generated summary

[1474] Output: Summary data stored in a database

[1475] Specific operation: The server uses a database management system (e.g., MySQL or MongoDB) to store the summary data in an appropriate format.

[1476] Step 8: Information Search

[1477] The terminal provides an interface for the user to search for information.

[1478] Input: User's search query

[1479] Output: Display of the user interface

[1480] What it does: The device provides a search box and filters through the web or mobile application.

[1481] Step 9: Run a search

[1482] The server searches the database based on the user's search query and retrieves the relevant summary information.

[1483] Input: User's search query

[1484] Output: Summary information obtained

[1485] Specific operation: The server extracts the relevant summary information from the database using an SQL query or the like.

[1486] Step 10: View the results

[1487] The terminal displays the retrieved summary information to the user in a visually viewable format.

[1488] Input: Retrieved summary information

[1489] Output: User-visible search results

[1490] What it does: The device displays search results in card or list format, allowing users to easily browse information.

[1491] As an example, when a user enters a prompt such as "Please provide the latest financial information of the target company," the system automatically performs a series of processes from data collection to displaying the results, and provides the user with the relevant information.

[1492] (Application example 1)

[1493] 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."

[1494] In recent years, with the rapid release of large amounts of information about security risks and vulnerabilities, it has become increasingly important for organizational and individual security personnel to efficiently collect, analyze, and implement appropriate countermeasures. However, manually collecting and analyzing this information requires a huge amount of time and effort, making it difficult to respond in a timely manner. Therefore, there is a need for a system that can collect and analyze security information in real time and provide summaries.

[1495] 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.

[1496] In this invention, the server includes means for collecting data from official websites of target organizations and groups, news sources, and industry databases, means for analyzing the collected text data using natural language processing (NLP) technology, means for summarizing the analyzed data in a predefined format, means for storing the summarized data in a database and making it accessible to users, means for users to search for information about specific organizations and transactions through a user interface, means for displaying search results on the user's terminal, and means for collecting and analyzing security information and providing summaries in real time, thereby enabling security personnel to quickly obtain high-quality security information and take appropriate measures.

[1497] "Organizations and groups" refers to companies, public institutions, non-profit organizations, etc. of various sizes and types.

[1498] "Official website" refers to an information dissemination page on the Internet that is officially operated and provided by an organization or group.

[1499] "News source" refers to the source of news articles and news programs provided by newspapers, news sites, television stations, etc.

[1500] "Industry Database" means a system or service that collects and stores data and information related to a particular industry.

[1501] "Means of collecting data" refers to technologies and devices used to obtain information, such as web scraping and API access.

[1502] "Natural Language Processing (NLP)" refers to the science and technology for analyzing, understanding, and generating human language.

[1503] "Means of analysis" refers to the software or algorithms used to analyze the collected text data.

[1504] "Summarization tools" refers to techniques or devices used to summarize key points from collected and analyzed data.

[1505] "Database" refers to a system for efficiently storing, retrieving, updating, and managing data.

[1506] "User interface" refers to the screens and applications through which users enter information and receive search results.

[1507] "User terminal" refers to a device such as a desktop computer, laptop, tablet, or smartphone that a user uses to connect to the Internet and obtain information.

[1508] "Security information" refers to information regarding vulnerabilities, threats, and risks of systems and networks.

[1509] "Real-time" refers to processing and providing information with extremely short delays.

[1510] This invention is a system for quickly collecting, analyzing, summarizing, and providing security-related information to users. This system is centered around a server and uses the following hardware and software:

[1511] Program Overview

[1512] 1. Data Collection

[1513] The server accesses the official websites of the organizations and organisations in question, news sources, and industry databases to retrieve information, using the Requests library to retrieve information from the network and BeautifulSoup to parse the HTML.

[1514] 2. Data Analysis

[1515] The server analyzes the collected text data using natural language processing (NLP) techniques, tokenizing the text data and extracting entities using the SpaCy library, and summarizing the text using Transformers models.

[1516] 3. Data Summary

[1517] Based on the analysis results, the server summarizes the information in a predefined format, and the summarized data is stored in a database in JSON or XML format.

[1518] 4. Information provision

[1519] The user's device can search for information about a specific organization or transaction through a user interface. When the user enters a search query, the server retrieves relevant summary data from the database and sends it to the device.

[1520] Specific example explanation

[1521] For example, if a user inputs a prompt such as "Show me information about the latest security risks," the server will generate a summary of the latest vulnerabilities and risks from the collected security information and display it on the terminal.

[1522] Hardware and Software Used

[1523] Server: A central device with high-performance processing power for data collection and analysis.

[1524] Requests library: Used to retrieve data from the web.

[1525] BeautifulSoup: Used for HTML parsing.

[1526] SpaCy library: Used for natural language processing.

[1527] Transformers library: Used for generating summaries.

[1528] Database: A system that stores data in JSON or XML format.

[1529] User devices: desktop computers, laptops, tablets, smartphones.

[1530] Prompt Sentence Examples

[1531] "Show me information about the latest security risks"

[1532] "Give me a summary of the latest vulnerability news."

[1533] By using this system, security personnel can quickly obtain high-quality information and take countermeasures efficiently.

[1534] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1535] Step 1:

[1536] The server collects data from official websites of organizations and groups of interest, news sources, and industry databases. Specifically, it uses the Requests library to access these sources and retrieve data in HTML or text format. The input to this step is a list of URLs for each source, and the output is the raw data retrieved.

[1537] Step 2:

[1538] The server uses BeautifulSoup to parse the raw data and extract the necessary information. For example, it extracts the article title, publication date, and body of the article through HTML parsing. The input to this step is the raw data obtained in step 1, and the output is the extracted structured data.

[1539] Step 3:

[1540] The server uses natural language processing (NLP) techniques to parse the extracted structured data. Specifically, it uses the SpaCy library to tokenize the text and extract entities (e.g., vulnerabilities, risks, organization names, etc.). The input of this step is the structured data obtained in step 2, and the output is the parsed data containing entity information.

[1541] Step 4:

[1542] The server uses Transformers to summarize the parsed data. For example, it extracts important parts from the original text and creates a short summary. The input to this step is the parsed data obtained in step 3, and the output is a summary sentence.

[1543] Step 5:

[1544] The server stores the abstract in a database in JSON or XML format. The input of this step is the abstract generated in step 4, and the output is the stored structured data.

[1545] Step 6:

[1546] A user utilizes a terminal to search for information about a particular organization or transaction through a user interface, for example, by entering a prompt phrase such as "Show me information about the latest security risks." The input to this step is the user's search query, and the output is a search request.

[1547] Step 7:

[1548] The server receives the search request and retrieves the relevant summary data from the database. The input to this step is the search query received in step 6, and the output is the search results.

[1549] Step 8:

[1550] The server sends the search results to the user's terminal. The input of this step is the search results obtained in step 7, and the output is the data sent to the terminal.

[1551] Step 9:

[1552] The terminal displays the received data on a user interface, for example visually displaying the summarized data in the form of cards. The input of this step is the data received in step 8, and the output is the displayed information.

[1553] 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.

[1554] The present invention relates to a system for collecting data related to a company or organization, analyzing and summarizing the data, and providing customized information by recognizing user emotions. The system includes a data collection means, a natural language processing (NLP) means, a summary generation means, a database storage means, a user interface, a search means, a display means, and an emotion engine.

[1555] Explanation of program processing

[1556] 1. Data Collection

[1557] The server prepares a list of URLs to access official company and organization websites, news sources, and industry databases.

[1558] The server crawls the URL list to obtain HTML and PDF data, and then uses scraping technology to extract necessary information such as company profiles, press releases, and financial information.

[1559] 2. Data Analysis

[1560] The server analyzes the collected data using natural language processing (NLP) techniques, tokenizing the text and tagging it by part of speech.

[1561] The server utilizes Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates.

[1562] The server uses relational modeling to analyze the relationships between entities.

[1563] 3. Summary Generation

[1564] The server generates a summary in a predefined format based on the analysis results, including a company profile and transaction overview.

[1565] The server stores the generated summaries in a database in JSON or XML format.

[1566] 4. User Interface

[1567] The device provides an interface through which the user can enter a search query, either through a web application or a mobile application.

[1568] A user uses the device interface to search for information about a particular company or transaction.

[1569] 5. Search and Display

[1570] The device sends the search query entered by the user to the server via an API request.

[1571] The server searches and retrieves relevant summary information from a database.

[1572] The terminal displays the search results on a user interface in a visually viewable format.

[1573] 6. Use of Emotion Engine

[1574] The server uses an emotion engine to parse emotions from the user's text input. For example, if the user inputs "I'm disappointed with my recent transaction," the emotion engine recognizes the emotion "disappointed."

[1575] The server provides search results and customized feedback that match the user's mood based on the recognized emotions.

[1576] Based on the results of the emotion engine, the device displays appropriate feedback, such as "We suggest improvements for this transaction."

[1577] Specific examples

[1578] 1. Data Collection

[1579] The server collects data from the official website of "Reisha Co., Ltd.", industry news sites, and industry-specific databases. The data includes official announcements, industry trend reports, etc.

[1580] 2. Data Analysis

[1581] The server tokenizes the collected text data, tags it with parts of speech, and uses NER to identify entities such as "example company," "AI technology," and "investment amount."

[1582] 3. Summary Generation

[1583] The server generates the following summary based on the analysis results:

[1584] Company profile: Reisha Co., Ltd. is primarily engaged in software development and has approximately 1,500 employees.

[1585] Transaction Overview: The latest mid-term plan calls for a significant increase in investment in AI technology.

[1586] 4. User Interface

[1587] A user enters a search query such as "I want to see an overview of transactions for example companies."

[1588] The server retrieves the latest information about the sample company from the summary database and transmits the retrieved data to the terminal.

[1589] The terminal displays the acquired summary information in a card format on the user interface.

[1590] 5. Use of Emotion Engine

[1591] The user enters a comment saying, "I am very satisfied with the company's recent performance."

[1592] The server uses an emotion engine to recognize the emotion "satisfaction" and provides the user with positive feedback such as "We'll also introduce you to some new investment opportunities that may interest you."

[1593] The device will then display appropriate feedback or additional information based on the recognized emotion.

[1594] This system not only enables users to quickly obtain high-quality corporate information, but also allows them to more efficiently gather information and prepare for business negotiations by receiving customized feedback tailored to their emotional state.

[1595] The processing flow will be explained below.

[1596] Step 1:

[1597] The server prepares a pre-configured list of URLs for official company websites, news sites, and industry databases to be collected.

[1598] Step 2:

[1599] The server sends an HTTP request to each website based on the URL list to retrieve HTML content or PDF files.

[1600] Step 3:

[1601] The server parses the HTML content and PDF files and uses scraping technology to extract important data such as company profiles, press releases, and financial information.

[1602] Step 4:

[1603] The server then uses natural language processing (NLP) technology to tokenize the extracted text data, breaking it down into sentences and words.

[1604] Step 5:

[1605] The server performs part-of-speech tagging on the tokenized data, which identifies the part of speech (noun, verb, adjective, etc.) of each word.

[1606] Step 6:

[1607] The server uses Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates.

[1608] Step 7:

[1609] The server uses relational modeling techniques to analyze the relationships between the identified entities, e.g., to clarify the relationship between Company A's investment in AI technology.

[1610] Step 8:

[1611] The server generates a summary based on the analysis results in a predefined format, which includes information such as company overview and transaction summary.

[1612] Step 9:

[1613] The server stores the generated summaries in a database in JSON or XML format, with a timestamp attached to distinguish between new and old data.

[1614] Step 10:

[1615] The terminal provides an interface through which the user enters a search query via a web application or a mobile application.

[1616] Step 11:

[1617] The user enters information about a particular company or transaction into the terminal interface and presses the search button.

[1618] Step 12:

[1619] The terminal sends the search query entered by the user to the server in the form of an API request.

[1620] Step 13:

[1621] The server retrieves relevant summary information from the database based on the received search query, retrieving the most recent and relevant data.

[1622] Step 14:

[1623] The server sends summary information of the search results in JSON format to the terminal.

[1624] Step 15:

[1625] The terminal visually displays the received summary information on the user interface in an easy-to-read format such as cards or tables.

[1626] Step 16:

[1627] The server analyzes the user's text input using an emotion engine that recognizes emotions (e.g., joy, sadness, disappointment, etc.) from the user's input text.

[1628] Step 17:

[1629] The server customizes search results and feedback based on the user's emotions, as recognized by the emotion engine. For example, if a user expresses "disappointment," the server suggests related topics and solutions.

[1630] Step 18:

[1631] The device presents appropriate feedback to the user based on the results of the emotion engine, allowing the user to receive information according to their emotions.

[1632] Example 2

[1633] 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."

[1634] Conventional corporate information gathering systems have made it difficult for users to quickly and accurately obtain the information they need. Furthermore, they lack the ability to analyze and summarize the collected information, and provide customized information based on the user's feelings, resulting in issues with the usefulness of the information and ease of access. As a result, efficiency in preparing for business negotiations and making decisions has decreased, and gathering information has taken a lot of time.

[1635] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1636] In this invention, the server includes: means for collecting data from official websites, information sources, and industry databases of target organizations; means for analyzing the collected text data using natural language processing technology; means for summarizing the analyzed data in a predefined format; means for storing the summarized data in a database and making it accessible to users; means for users to search for information on specific organizations or transactions through a user interface; means for displaying search results on the user's terminal; and means including an emotion engine for analyzing user emotions from text input and providing customized information. This allows users to not only quickly obtain high-quality corporate information but also receive customized feedback tailored to their emotional state.

[1637] "Covered Entity" means the company, association, or other legal entity identified as the subject of data collection, analysis, or abstraction.

[1638] "Sources" refers to websites and databases that contain information related to an organization, such as news sites, industry reports, and social media.

[1639] "Industry database" refers to an online database that centrally manages and provides data related to a specific industry.

[1640] "Data collection methods" refers to the techniques and methods used to obtain the desired data from websites and databases, such as using web crawlers or API requests.

[1641] "Natural language processing technology" refers to machine learning algorithms and text analysis technologies for analyzing and understanding human language.

[1642] "Summarization means" refers to algorithms or methods for succinctly presenting collected and analyzed data as a summary statement.

[1643] "Means of database storage" refers to the techniques and methods used to store data in a structured format in a database to facilitate search and retrieval.

[1644] "User Interface" refers to the interactive screen or application through which a user interacts with a system, inputs information, and obtains results.

[1645] "Terminal" refers to a device such as a computer or smartphone that allows a user to input data and display results.

[1646] An "emotion engine" refers to technology or algorithms that analyze a user's emotional state from text and provide appropriate feedback based on the results.

[1647] "Relational modeling technology" refers to a data analysis technique for analyzing the relationships between entities and visualizing them as a network.

[1648] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a lightweight data exchange format for structuring and storing data.

[1649] "XML format" is an abbreviation for eXtensible Markup Language, and refers to a general-purpose markup language for hierarchically structuring and storing data.

[1650] The present invention is a system that collects information about organizations and groups, analyzes and summarizes it, and further recognizes the user's emotions to provide customized information. This system consists of a server, a terminal, and a user. The roles and operations of each component are described in detail below.

[1651] server

[1652] 1. Data Collection

[1653] The server collects data from official websites, information sources, and industry databases. Specifically, it uses a web crawler such as Selenium to access a specified list of URLs and retrieves data in HTML or PDF format. From the retrieved data, it extracts necessary information such as company profiles, press releases, and financial information using tools such as BeautifulSoup and PyPDF2.

[1654] 2. Data Analysis

[1655] The server analyzes the collected text data using natural language processing techniques. For example, it uses libraries such as spaCy and NLTK to tokenize the text data and tag parts of speech. It also uses Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates. It then analyzes the relationships between entities using relational modeling tools such as NetworkX.

[1656] 3. Summary Generation

[1657] The server generates a summary in a predefined format based on the analysis results. This summary includes a company profile and a business overview. For example, it generates a summary statement in the following format: "Reisha Co., Ltd. is primarily engaged in software development and has approximately 1,500 employees. Its latest medium-term plan calls for a significant increase in investment in AI technology." The generated summary is stored in a database in JSON or XML format.

[1658] 4. Use of Emotion Engine

[1659] The server uses an emotion engine to analyze emotions from the user's text input. For example, if a user enters a comment such as "I'm very satisfied with the recent performance of the company," the emotion engine recognizes the emotion as "satisfied." Based on the results, the server generates customized feedback and provides it in the form of, for example, "We'll also introduce you to some new investment opportunities that may be of interest to you."

[1660] Terminal

[1661] 1. Interface provision

[1662] The terminal provides an interface, including a web application or a mobile application, through which a user can enter a search query. For example, the terminal may provide an interface through which a user can enter a query such as "Show me a trading summary for company X."

[1663] 2. Search and Display

[1664] The device sends the search query entered by the user to the server and visually displays the search results received from the server. The search results are displayed in a card format, providing a concise and visually easy-to-understand format.

[1665] User

[1666] 1. Information Search

[1667] Users use the device interface to search for information about a particular organization or transaction, for example, by entering a search query such as "Show me an overview of transactions for this company."

[1668] Specific examples

[1669] The server collects data from the official website of "Example Company," industry news sites, and industry-specific databases. For example, it obtains and analyzes data such as official announcements and industry trend reports to generate a summary such as "Example Company announced plans to invest 50 billion yen in AI technology." If a user inputs "I would like to see an overview of Example Company's transactions," this summary is displayed on the device. Furthermore, if a user inputs a comment such as "I am very satisfied with Example Company's recent performance," the emotion engine recognizes that emotion and provides feedback such as "I would also like to introduce some new investment opportunities that may be of interest to you."

[1670] Example prompt for a generative AI model:

[1671] Could you please provide the latest trading information for Example Co., Ltd.? Also, could you please provide a system that analyzes the sentiment of user reviews about recent business performance and provides positive feedback?

[1672] In this way, the present invention enables the rapid acquisition of high-quality corporate information and improves the efficiency of information gathering and decision-making by providing customized feedback that is tailored to the user's emotions.

[1673] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1674] Step 1:

[1675] Data collection

[1676] 1. The server prepares a list of URLs to collect data from official websites of organizations, information sources, and industry databases. The input is the URL list, and the server sets up a web crawler based on it.

[1677] 2. The server launches a web crawler using Selenium to retrieve data in HTML or PDF format from the specified URL list, and the output is the retrieved raw web data.

[1678] 3. The server uses BeautifulSoup and PyPDF2 to parse the HTML or PDF data and extract the necessary information, such as company profiles, press releases, financial information, etc. The input is raw web data, and the output is extracted text data.

[1679] Step 2:

[1680] Data analysis

[1681] 1. The server analyzes the collected text data using natural language processing technology. The input is the extracted text data, and the output is the analyzed text data.

[1682] 2. The server uses spaCy and NLTK to tokenize the text data and tag it with parts of speech. Specifically, it splits sentences such as "Reisha Co., Ltd. invests in AI technology" into words and tags each word with a part of speech.

[1683] 3. The server uses Named Entity Recognition (NER) to identify important entities such as company names, product names, people's names, and dates. The input is tokenized text data, and the output is text data with identified entities.

[1684] 4. The server uses NetworkX to analyze the relationships between entities using relational modeling techniques. The input is text data with identified entities, and the output is data with analyzed relationships.

[1685] Step 3:

[1686] Summary Generation

[1687] 1. The server generates a summary in a predefined format based on the analysis results. The input is the analyzed text data, and the output is the generated summary sentence.

[1688] 2. The server saves the generated summary in a database in JSON or XML format. The input is the generated summary sentence, and the output is the summary data saved in the database.

[1689] Step 4:

[1690] User Search Interface

[1691] 1. The device provides an interface where the user can enter a search query. Specifically, it provides a web page or mobile application that includes a search bar and a search button. The input is the user's search query, and the output is an API request from the device to the server.

[1692] 2. The user enters a query about a specific organization or transaction, such as "Show me an overview of transactions for this company."

[1693] Step 5:

[1694] Search and Display

[1695] 1. The device sends the search query entered by the user to the server. The input is the user's search query, and the output is an API request to the server.

[1696] 2. The server searches the database for summary information related to the user's query and retrieves it in JSON format. The input is the API request to the server, and the output is the retrieved summary information.

[1697] 3. The terminal visually displays the acquired summary information. Specifically, it presents the information in a card format. The input is summary information in JSON format, and the output is summary information displayed on the user interface.

[1698] Step 6:

[1699] Using the Emotion Engine

[1700] 1. The server sends the user's text input to the emotion engine for emotion analysis. The input is the user's text comment, and the output is the analyzed emotion data.

[1701] 2. The server generates customized feedback based on the analysis results of the emotion engine. For example, if the analyzed emotion is "satisfied," it generates feedback such as "We'll also introduce you to some new investment opportunities that may interest you." The input is the analyzed emotion data, and the output is the generated feedback.

[1702] 3. The terminal displays the feedback received from the server on the user interface. The input is the feedback from the server, and the output is the feedback displayed on the user interface.

[1703] (Application example 2)

[1704] 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."

[1705] In modern society, the rapid increase in the amount of information is making it difficult for users to quickly and efficiently obtain the information they need. There is also a growing need for personalized information provision, and there is also a demand for information that is customized based on the user's emotions. However, current systems do not adequately provide information that meets these needs. Therefore, it is necessary to provide a system that allows users to efficiently search for specific information and obtain information that matches their emotional state.

[1706] 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.

[1707] In this invention, the server includes means for collecting data from official websites, information sources, and industry databases of target organizations and groups, means for analyzing the collected text data using natural language processing technology, means for summarizing the analyzed data in a predefined format, means for storing the summarized data in a database and making it accessible to users, means for users to search for information on specific organizations and transactions through a user interface, means for displaying search results on the user's terminal, and means for analyzing emotions from text entered by the user and customizing search results based on the emotions, thereby enabling users to not only quickly obtain high-quality information but also receive information customized based on their emotions.

[1708] An "organization" is a group formed to achieve a specific purpose, and includes companies, groups, institutions, etc.

[1709] A "group" is a collection of people or things with a common purpose or interest.

[1710] A "source" is a medium or platform that provides information such as news, data, and statistics.

[1711] An "industry database" is a database that collects and manages information related to a specific industry.

[1712] "Data collection" is the process of gathering data needed for a specific purpose.

[1713] "Natural language processing technology" is a technology that enables computers to understand and process human language, and includes language analysis, sentiment analysis, summary generation, and more.

[1714] "Analysis" is the process of understanding the content of data and extracting meaning from it.

[1715] A "summary" is a shortened version of text or information that extracts only the main points.

[1716] A "database" is a system that centrally manages and stores data and allows quick access when needed.

[1717] "User interface" refers to the screens and operating methods that users use to interact with the system.

[1718] "Searching" is the process of searching a database or the Internet to find specific information.

[1719] A "terminal" is a hardware device that allows a user to enter information and display results.

[1720] "Emotion analysis" is a technology that extracts and understands user emotions from text, voice, etc.

[1721] "Customization" means adjusting functionality and display content to suit a user's specific needs and preferences.

[1722] This invention is a system that uses a server and a terminal to allow a user to efficiently collect information about an organization or group and provide customized information based on that information. This system includes the following processes.

[1723] First, the server collects data from the target organization's or group's official website, information sources, and industry databases using web scraping techniques such as the requests library and BeautifulSoup, and saves the collected data in text format.

[1724] The collected data is then analyzed on the server using natural language processing techniques, specifically using NLP libraries such as Spacy to tokenize the text data, tag parts of speech, and perform entity recognition, and sentiment analysis using a pipeline of Transformers libraries.

[1725] The parsed data is summarized in a predefined format, including key points and entity information, and stored in a database in JSON or XML format.

[1726] When a user searches for information about a particular organization or transaction, they enter a search query through a user interface, which can be a web or mobile application.

[1727] The server retrieves relevant summary information from a database based on the input search query and sends it to the terminal, which displays the information on a user interface.

[1728] Furthermore, when a user inputs their thoughts or opinions through their device, the server-side sentiment analysis engine analyzes them and customizes search results based on the user's emotions. For example, if a user inputs a sentiment such as "I'm satisfied with my recent transaction," the server will recognize the emotion "satisfied" and provide the user with additional, more positive information.

[1729] As a specific example, if a user responds to the prompt, "Please tell us how you felt after reading recent news about AI technology," by typing, "I'm very satisfied with the recent progress in AI technology. I'd like to hear other positive news," the server will analyze this sentiment and provide additional positive news.

[1730] This system allows users to efficiently obtain information of interest and also receive customized information based on their own emotions.

[1731] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1732] Step 1:

[1733] The server collects data from the official websites of target organizations and groups, information sources, and industry databases. It receives a list of URLs and a search query as input, which determines the content of the web pages to collect. It issues HTTP requests using the requests library to retrieve HTML or PDF data from the specified websites. It uses BeautifulSoup to extract the necessary text information from the retrieved data. The extracted text information is output and passed to the next analysis step.

[1734] Step 2:

[1735] The server analyzes the collected text data using natural language processing technology. It receives the text information extracted in step 1 as input. It uses an NLP library such as Spacy to tokenize the text data and perform part-of-speech tagging and entity recognition. This identifies entities such as nouns, verbs, company names, and dates in the text, and outputs the relationships between these entities and the text.

[1736] Step 3:

[1737] The server summarizes the analyzed data into a predefined format. It receives as input the analysis results (entities and relationships) obtained in step 2. It uses a summary generation algorithm to generate a shortened text that includes key points and entity information. The generated summary text is output in a format (JSON or XML) that can be stored in the database.

[1738] Step 4:

[1739] The server stores the summarized data in a database and makes it accessible to users. As input, it receives the summary text generated in step 3. The server stores this in the database in the specified format (JSON or XML). The stored data can be later used for searching and display.

[1740] Step 5:

[1741] A user inputs a search query for information about a specific organization or transaction into the user interface via a terminal. The user inputs a specific query into the interface using a keyboard or mouse. The query is sent from the terminal to the server. The server analyzes the query, searches the database for relevant summary information, and outputs the search results.

[1742] Step 6:

[1743] The server sends the search results to the terminal, which displays them on its user interface. As input, it receives the search results obtained in step 5. The terminal visually displays the search results using HTML and CSS, allowing the user to view the results on the screen.

[1744] Step 7:

[1745] The user inputs their thoughts and opinions through the terminal. The user inputs their thoughts and opinions in the text box and presses the send button. This input data is sent from the terminal to the server.

[1746] Step 8:

[1747] The server uses a sentiment analysis engine to analyze the sentiment from the text entered by the user. As input, it receives the user's text data obtained in step 7. Using a pipeline of the Transformers library, it classifies the sentiment in the text into categories such as positive or negative. The analyzed sentiment is obtained as output.

[1748] Step 9:

[1749] The server customizes search results based on the analyzed emotions and provides them to the user. As input, it receives the emotion analysis results obtained in step 8. If the emotion is positive, it searches for and selects additional information containing positive content and adds it to the search results. The customized search results are output and sent back to the terminal. The user can receive information that suits their emotions.

[1750] As a specific example of how it works, if the server displays a prompt on the device saying, "Please tell us how you felt after reading recent news about AI technology," and the user enters, "I'm very satisfied with the recent progress in AI technology. I'd like to hear other positive news," the server will recognize this emotion as "POSITIVE" and provide additional positive news.

[1751] 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.

[1752] 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.

[1753] 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.

[1754] 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.

[1755] 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.

[1756] 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.

[1757] 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).

[1758] 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.

[1759] 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."

[1760] 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.

[1761] 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).

[1762] 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.

[1763] 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.

[1764] 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.

[1765] 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.

[1766] 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.

[1767] 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.

[1768] 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.

[1769] 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.

[1770] 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.

[1771] 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.

[1772] The following is further disclosed regarding the above embodiment.

[1773] (Claim 1)

[1774] Collect data from official websites of target companies and organizations, news sources, and industry databases;

[1775] A means of analyzing the collected text data using natural language processing (NLP) techniques;

[1776] a means for summarizing the analyzed data in a predefined format;

[1777] a means for storing the summarized data in a database and making it accessible to users;

[1778] a means for users to search for information about specific companies or transactions through a user interface;

[1779] means for displaying search results on a user's device;

[1780] A system including:

[1781] (Claim 2)

[1782] 10. The system of claim 1, further comprising means for analyzing the collected data for relationships between entities using relational modeling techniques.

[1783] (Claim 3)

[1784] 10. The system of claim 1, further comprising means for saving the summarized data in JSON or XML format.

[1785] "Example 1"

[1786] (Claim 1)

[1787] Collect data from official websites of target organizations and groups, news platforms, and industry databases;

[1788] a means for using natural language processing (NLP) techniques to analyze the collected data;

[1789] a means of summarizing the analyzed data in a predefined format;

[1790] a means for storing the summarized data in a database and making it accessible to users;

[1791] a means for users to search for information about a particular organization or transaction through a user interface;

[1792] means for displaying search results on a user's device;

[1793] A system including:

[1794] (Claim 2)

[1795] 10. The system of claim 1, further comprising means for analyzing the collected data for relationships between entities using relational modeling techniques.

[1796] (Claim 3)

[1797] 10. The system of claim 1, further comprising means for saving the summarized data in JSON or XML format.

[1798] "Application Example 1"

[1799] (Claim 1)

[1800] Collect data from official websites of target organizations and groups, news sources, and industry databases;

[1801] A means of analyzing the collected text data using natural language processing (NLP) technology;

[1802] a means for summarizing the analyzed data in a predefined format;

[1803] a means for storing the summarized data in a database and making it accessible to users;

[1804] A means for users to search for information about specific organizations or transactions through a user interface;

[1805] a means for displaying search results on a user's device;

[1806] A means of collecting and analyzing security information and providing summaries in real time;

[1807] A system including:

[1808] (Claim 2)

[1809] 10. The system of claim 1, further comprising means for analyzing the collected data for relationships between entities using relational modeling techniques.

[1810] (Claim 3)

[1811] 10. The system of claim 1, further comprising means for saving the summarized data in JSON or XML format.

[1812] "Example 2: Combining Emotion Engines"

[1813] (Claim 1)

[1814] means of collecting data from official websites of target organizations, sources, and industry databases;

[1815] A means for analyzing the collected text data using natural language processing technology;

[1816] a means for summarizing the analyzed data in a predefined format;

[1817] a means for storing the summarized data in a database and making it accessible to users;

[1818] A means for users to search for information about specific organizations or transactions through a user interface;

[1819] a means for displaying search results on a user's device;

[1820] means for analyzing a user's emotions from a text input and providing customized information, the means including an emotion engine;

[1821] A system including:

[1822] (Claim 2)

[1823] 10. The system of claim 1, further comprising means for analyzing the collected data for relationships between entities using relational modeling techniques.

[1824] (Claim 3)

[1825] 10. The system of claim 1, further comprising means for saving the summarized data in JSON or XML format.

[1826] "Application example 2 when combining emotion engines"

[1827] (Claim 1)

[1828] Collect data from official websites, sources, and industry databases of the target organizations and groups;

[1829] A means for analyzing the collected text data using natural language processing technology;

[1830] a means for summarizing the analyzed data in a predefined format;

[1831] a means for storing the summarized data in a database and making it accessible to users;

[1832] A means for users to search for information about specific organizations or transactions through a user interface;

[1833] a means for displaying search results on a user's device;

[1834] A means for analyzing sentiment from user-entered text and customizing search results based on sentiment;

[1835] A system including:

[1836] (Claim 2)

[1837] 10. The system of claim 1, further comprising means for analyzing the collected data for relationships between entities using relational modeling techniques.

[1838] (Claim 3)

[1839] 10. The system of claim 1, further comprising means for saving the summarized data in JSON or XML format. [Explanation of symbols]

[1840] 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. Collect data from official websites of target companies and organizations, news sources, and industry databases; A means for analyzing the collected text data using natural language processing technology; a means for summarizing the analyzed data in a predefined format; a means for storing the summarized data in a database and making it accessible to users; a means for users to search for information about specific companies or transactions through a user interface; means for displaying search results on a user's device; A system including:

2. 10. The system of claim 1, further comprising means for analyzing the collected data for relationships between entities using relational modeling techniques.

3. 10. The system of claim 1, further comprising means for saving the summarized data in JSON or XML format.

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