system

Abstract

We provide the system. [Solution] A means for collecting data from multiple data sources, including business challenges, competitor information, and IR information, based on keywords received from a terminal. A natural language processing means for analyzing collected data and extracting important information related to specified keywords, A means of filtering the analyzed data to verify its consistency, A means of storing filtered data in a database, A means of building a dashboard to provide stored data to users, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the sales department of a company, the work of collecting business issues, competitive information, and IR information necessary for creating an account plan is very time-consuming and labor-intensive. Furthermore, manually analyzing and filtering the collected information is inefficient and prone to human error. The purpose of the present invention is to provide necessary information efficiently and quickly by automating these information collection, analysis, and filtering operations, and to support the construction of a sales strategy.

Means for Solving the Problems

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

[0006] The system includes means for collecting data from multiple data sources, including business challenges, competitor information, and investor relations information, based on keywords received from a terminal; natural language processing means for analyzing the collected data and extracting important information related to the specified keywords; means for filtering the analyzed data and verifying its consistency; means for storing the filtered data in a database; and means for constructing a dashboard to provide the stored data to the user.

[0007] Furthermore, by including means to cross-check collected data and exclude inconsistent data, highly accurate information can be provided. Additionally, by including means to perform sentiment analysis on collected data and categorize it into positive, negative, and neutral categories, users can more easily understand the importance and emotional impact of the information. This supports the effective development of sales strategies.

[0008] A "terminal" is a computer or device used by a system user to access, enter keywords, or view dashboards.

[0009] A "keyword" is a word or phrase that a user enters to identify the business issues, competitor information, investor relations information, etc., that they are seeking information on.

[0010] "Business challenges" refer to problems that a company must solve or obstacles that it must overcome, and are important pieces of information in strategic planning.

[0011] "Competitive information" refers to information about the products, services, strategies, and activities offered by other companies in the same market.

[0012] "IR information" refers to information that companies disclose to investors and shareholders, including financial information, performance reports, and important announcements.

[0013] "Data sources" refer to publicly available information sources and databases on the internet used to obtain business challenges, competitor information, and investor relations (IR) information.

[0014] "Data collection" refers to the process of gathering necessary information from multiple data sources based on specified keywords.

[0015] Natural Language Processing (NLP) is a general term for the technologies and methods that enable computers to understand and analyze human language.

[0016] "Data analysis" refers to the process of using natural language processing on collected data to extract important information and keywords.

[0017] "Filtering" is the process of removing redundant information and noise from collected data, and eliminating inconsistent data.

[0018] "Consistency verification" is the process of comparing data obtained from multiple sources and eliminating inconsistent information.

[0019] A "database" is a digital storage system for structuring and storing analyzed and filtered data.

[0020] A "dashboard" is a display interface that visualizes the collected and analyzed information so that users can intuitively understand it.

[0021] "Sentiment analysis" is the process of evaluating the positive, negative, and neutral emotions in collected text data and categorizing them based on the results. [Brief explanation of the drawing]

[0022] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0023] [[ID=并41]]Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0024] First, the terms used in the following description will be explained.

[0025] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0026] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0027] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0028] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0029] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0030] [First Embodiment]

[0031] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0032] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0033] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0034] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0035] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0036] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0037] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0038] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0040] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0041] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0042] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0043] This invention relates to an information gathering system for efficiently building sales strategies. Based on keywords specified by the user, this system collects and analyzes business challenges, competitor information, and investor relations (IR) information, and provides this information in a dashboard format. The system's program processing is described below in natural language.

[0044] 1. Start the information gathering task.

[0045] The user uses a device to enter keywords to be collected and initiates the information collection process. For example, they might enter specific keywords such as "new product from competitor X" or "latest industry trends."

[0046] The terminal receives the keyword entered by the user and sends it to the server.

[0047] 2. Execution of information gathering

[0048] Based on the received keywords, the server begins collecting information on specified business challenges, competitors, and investor relations (IR) information. Specifically, it uses a web crawler to collect relevant information from publicly available sources on the internet (news sites, corporate investor relations pages, industry reports, etc.).

[0049] The server collects data from multiple data sources and temporarily stores it in storage.

[0050] 3. Data Analysis

[0051] The server performs natural language processing (NLP) on the collected temporary data to analyze the text data. This involves tokenization and part-of-speech tagging, which helps extract important information that matches keywords.

[0052] For example, information that "competitor company X" has announced "new product Y" is extracted from multiple news articles and then organized that information.

[0053] 4. Filtering and Integrity Verification

[0054] The server further processes the analyzed data, filtering out redundant information and noise. This is crucial for ensuring the consistency of the information.

[0055] The server cross-checks data from multiple sources and filters out inconsistent information. This improves the reliability of the collected information.

[0056] 5. Data Storage

[0057] The server stores the filtered data in a database. The database also stores metadata such as the date and time of collection, the source of the data, and related keywords.

[0058] For example, information such as "Competitor X announces new product Y" is recorded along with a specific date.

[0059] 6. Generating and delivering the dashboard

[0060] The server generates a user-viewable dashboard based on the stored data. This dashboard provides important collected information in a visually easy-to-understand manner.

[0061] The device helps users access the dashboard and ensures that the latest information is displayed in real time.

[0062] Specific example: Gathering information and developing strategies regarding a competitor X's new product announcement.

[0063] 1. Start the task

[0064] The user enters keywords such as "Competitor X," "New Product Announcement," and "Market Reaction" into the device and issues a data collection command.

[0065] 2. Information Gathering

[0066] The server crawls news articles and industry reports on the internet based on these keywords to collect the necessary information.

[0067] 3. Data Analysis

[0068] The server analyzes the collected articles using natural language processing (NLP) to extract information such as "Competitor company X has announced a new product Y." It also evaluates positive and negative market reactions using sentiment analysis.

[0069] 4. Filtering and Integrity Verification

[0070] The server filters out redundant data and verifies the consistency of data collected from multiple sources.

[0071] 5. Data Storage

[0072] The server stores organized information in a database, which also includes metadata.

[0073] 6. Dashboard generation and delivery

[0074] The server visually displays this information on a dashboard, which users can then view on their devices. This enables rapid and effective strategic planning.

[0075] Through the process described above, the present invention can efficiently collect, analyze, and provide to users the information necessary to build sales strategies. This system significantly reduces time and effort and improves the accuracy of strategy development.

[0076] The following describes the processing flow.

[0077] Step 1:

[0078] The user uses their device to enter keywords for information gathering and initiates the data collection process. For example, they might specify specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction."

[0079] Step 2:

[0080] The device receives the keyword entered by the user and sends it to the server. Along with the keyword, it can also send the user ID and the purpose of the data collection.

[0081] Step 3:

[0082] The server will begin collecting information based on the keywords it receives. It will launch a web crawler and search for relevant websites, news articles, corporate investor relations pages, industry reports, and more.

[0083] Step 4:

[0084] The server temporarily stores the text data collected by the web crawler in storage. This data also includes metadata such as the source and date of collection.

[0085] Step 5:

[0086] The server performs natural language processing (NLP) on the temporarily stored text data. It analyzes important text information by performing tokenization, part-of-speech tagging, keyword extraction, and other similar operations.

[0087] Step 6:

[0088] The server filters the analyzed data, removing redundant information and noise, and excluding unreliable information. For example, it eliminates spam data that repeats the same content or irrelevant information.

[0089] Step 7:

[0090] The server cross-checks the filtered data. It compares information from multiple sources to ensure consistency. Inconsistent data is excluded.

[0091] Step 8:

[0092] The server stores the data, whose integrity has been verified, in the database. At the same time, metadata such as the date and time of collection, source information, and related keywords are also stored with the data.

[0093] Step 9:

[0094] The server generates a dashboard based on the stored data. The dashboard is designed to be visually easy to understand, allowing users to see analysis results and important information at a glance.

[0095] Step 10:

[0096] The terminal receives data from the server and displays it to the user. The user can then view the collected information and analyze the results through the dashboard.

[0097] Step 11:

[0098] Users can develop specific sales strategies based on the information in the dashboard. They can also request additional information gathering or the generation of detailed reports as needed.

[0099] The above outlines the series of specific processing steps from information gathering to analysis and provision. This invention allows users to efficiently and quickly collect necessary information and utilize it in building their sales strategies.

[0100] (Example 1)

[0101] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0102] Traditional information gathering systems suffered from the problem of requiring a great deal of time and effort to collect and analyze necessary information. Furthermore, the filtering to ensure the reliability and consistency of the collected information was insufficient, resulting in low accuracy when used for strategy development. In addition, there was a lack of efficient means to visualize the collected information and provide it to users, limiting the processing and utilization of the information.

[0103] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0104] In this invention, the server includes means for collecting information from multiple information sources, including business challenges, competitor information, and investor relations information, based on keywords received from a terminal; natural language processing means for analyzing the collected information and extracting important information related to the specified keywords; and means for filtering the analyzed information and verifying its consistency. This makes it possible to collect necessary information efficiently and accurately and provide reliable data to the user.

[0105] A "terminal" is a computer device used by users to input information and communicate with a server.

[0106] A "keyword" is a specific word or phrase that is the target of information gathering.

[0107] "Business challenges" refer to problems and challenges that companies and organizations face.

[0108] "Competitive information" refers to information and data related to competitors.

[0109] "IR information" refers to information provided by a company to its investors, including data on its financial status and performance.

[0110] "Information sources" refer to websites, databases, and other publicly available resources that provide data and information.

[0111] "Information" is a general term referring to collected data, documents, and reports.

[0112] "Natural language processing methods" refer to technologies and algorithms for analyzing input text data and understanding its meaning.

[0113] "Filtering" refers to the process of removing redundant information and noise from collected data.

[0114] A "database" is a system for storing organized data and retrieving it as needed.

[0115] A "dashboard" is an interface for visually displaying collected and analyzed information.

[0116] Cross-checking is the process of comparing data collected from multiple sources to verify consistency and reliability.

[0117] "Sentiment analysis" refers to a technique that analyzes the content of text data and evaluates its emotional tone.

[0118] "Positive" refers to an evaluation that is affirmative or forward-looking.

[0119] "Negative" refers to an evaluation that is negative or unfavorable.

[0120] "Neutral" means that the evaluation is neither positive nor negative.

[0121] This invention relates to an information gathering system for efficiently building sales strategies. This system collects and analyzes business challenges, competitor information, and investor relations (IR) information based on keywords specified by the user, and provides this information in a dashboard format. The embodiments of this invention are described in detail below.

[0122] System Configuration

[0123] The system is broadly composed of a user interface (terminal), a server that collects and analyzes information, and a database.

[0124] terminal

[0125] A terminal is a device used by users to input information gathering tasks and access a dashboard. This includes hardware such as personal computers, tablets, and smartphones. The software running on the terminal provides a user interface using a web browser or dedicated application.

[0126] server

[0127] The server is the central component that performs information gathering, data analysis, filtering, and data storage. The main processes performed by the server are as follows:

[0128] 1. Information Gathering

[0129] The server collects information using a web crawler implemented in Python. Specifically, it uses libraries such as BeautifulSoup and Scrapy to extract relevant information from news sites, corporate investor relations pages, industry reports, and other sources on the internet.

[0130] 2. Data Analysis

[0131] The server applies natural language processing (NLP) techniques to the collected data. This involves using libraries such as spaCy and NLTK to tokenize text data, tag it with parts of speech, and extract keywords. It also performs sentiment analysis, classifying the data into positive, negative, and neutral categories.

[0132] 3. Filtering and Integrity Verification

[0133] The server filters the analyzed data, removing redundant information and noise. Furthermore, it cross-checks data collected from multiple sources to eliminate inconsistent information.

[0134] 4. Data Storage

[0135] The filtered data is stored in a database such as MySQL® or PostgreSQL. The stored information includes metadata such as the date and time of collection, source, and related keywords.

[0136] Dashboard

[0137] A dashboard is an interface that allows users to visually review collected and analyzed information. It uses data visualization libraries such as D3.js and Chart.js to display important information in graphs and charts. Users can access this dashboard from their devices to view the latest information in real time.

[0138] Specific example

[0139] Gathering information and developing strategies regarding new product announcements from competitors.

[0140] 1. Start the task

[0141] Users enter keywords such as "competitors," "new product announcements," and "market reactions" into their devices and issue commands to collect data.

[0142] 2. Information Gathering

[0143] The server uses a web crawler to collect information based on the keywords mentioned above. For example, it might use BeautifulSoup to extract articles containing specific keywords from the HTML of a news site.

[0144] The collected data will be temporarily stored in an S3 bucket.

[0145] 3. Data Analysis

[0146] The server tokenizes the collected articles using spaCy and extracts information such as "a competitor has announced a new product." It also evaluates the market reaction through sentiment analysis and determines whether it is positive, negative, or neutral.

[0147] 4. Filtering and Integrity Verification

[0148] The server filters out redundant data and removes irrelevant or duplicate information.

[0149] Furthermore, data collected from multiple sources is cross-checked to eliminate inconsistent information.

[0150] 5. Data Storage

[0151] The server stores the organized information in a PostgreSQL database, and simultaneously saves metadata.

[0152] 6. Dashboard generation and delivery

[0153] The server uses D3.js to visually display the extracted information on a dashboard. For example, it can display graphs showing market reactions or time-series data on competitors' new product announcements.

[0154] The device allows users to access the dashboard and view all information in real time.

[0155] Example of a prompt

[0156] "What are the key points for gathering information and formulating strategies to evaluate market reactions when competitors launch new products?"

[0157] As described above, the system of the present invention enables efficient and effective collection and analysis of competitive information. This allows users to quickly develop sales strategies.

[0158] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0159] Step 1: Receiving input

[0160] The user accesses the system interface using a terminal and enters the keywords to be collected. For example, they might enter keywords such as "new product announcement by a competitor."

[0161] Input: Keywords entered by the user

[0162] Output: Input confirmation screen displayed on the terminal

[0163] Step 2: Submitting the Keyword

[0164] The terminal receives the keyword entered by the user, verifies it, and sends it to the server. During this process, it also checks the format of the input data and verifies that there is no invalid input.

[0165] Input: Keyword entered by the user

[0166] Output: Keyword data received by the server

[0167] Step 3: Start gathering information

[0168] The server initiates an information gathering task based on keywords received from the terminal. The server uses a web crawler implemented in Python to collect relevant information from publicly available sources on the internet.

[0169] Operation: Uses libraries such as BeautifulSoup and Scrapy to analyze the HTML structure of a website and extract information related to specified keywords.

[0170] Input: Keyword sent from the device

[0171] Output: Collected raw data (news articles, reports, etc.)

[0172] Step 4: Save map temporarily

[0173] The server temporarily stores the collected data in storage. This often utilizes AWS® S3 buckets or Google® Cloud Storage.

[0174] Input: Collected raw data

[0175] Output: Temporarily saved data file

[0176] Step 5: Perform data analysis

[0177] The server analyzes the collected data using natural language processing (NLP) techniques. Tokenization, part-of-speech tagging, and keyword extraction are performed using libraries such as spaCy and NLTK.

[0178] Function: Text data analysis and extraction of important information

[0179] Input: Temporarily saved data file

[0180] Output: Analyzed information (tokenized data, list of important information)

[0181] Step 6: Filtering and Integrity Check

[0182] The server filters the analyzed data, eliminating redundant information and noise. It also cross-checks information collected from multiple data sources, removing inconsistent data.

[0183] Operation: Data deduplication and cross-checking

[0184] Input: Analyzed information

[0185] Output: Filtered, consistent data

[0186] Step 7: Storage

[0187] The server stores the filtered data in a database. During storage, metadata such as the collection date and time, source information, and related keywords are also saved. Relational databases like MySQL or PostgreSQL are utilized.

[0188] Function: Data organization and metadata addition

[0189] Input: Filtered, consistent data

[0190] Output: Organized data stored in the database

[0191] Step 8: Generate the dashboard

[0192] The server generates a dashboard based on the stored data. It uses D3.js or Chart.js to visually display the collected information.

[0193] Function: Generate data visuals (graphs, charts)

[0194] Input: Organized data stored in the database

[0195] Output: Visually displayed dashboard

[0196] Step 9: Accessing the Dashboard

[0197] The device allows users to access the dashboard and provides real-time updates.

[0198] Input: User access request

[0199] Output: Dashboard screen viewable by the user

[0200] These steps enable the system of the present invention to efficiently and accurately collect necessary information and provide reliable data to the user.

[0201] (Application Example 1)

[0202] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0203] Traditional methods for collecting and analyzing advertising campaign and marketing trend information suffered from a lack of reliability and consistency, making efficient data analysis and visual presentation difficult. Furthermore, the inadequate sentiment analysis of collected data made it difficult to accurately grasp market reactions. This hindered the development of effective advertising strategies.

[0204] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0205] In this invention, the server includes means for collecting data from multiple data sources, including advertising campaigns and marketing trend information, based on keywords received from a terminal; natural language processing means for analyzing the collected data and extracting important information related to the specified keywords; consistency verification means for filtering the analyzed data and excluding redundant or unreliable information; means for storing the filtered data in a database; and means for constructing a dashboard to visually provide the stored data to the user. This enables efficient and reliable data collection and analysis, and supports the construction of advertising strategies.

[0206] An "advertising campaign" is a systematic promotional activity conducted to increase awareness of a product or service.

[0207] "Marketing trends" refer to the latest trends in marketing methods and strategies that arise in response to market trends and changes in customer preferences.

[0208] A "data source" refers to an online source of information, such as news sites, industry reports, or social media posts, that serves as the basis for collecting information.

[0209] "Natural language processing (NLP) techniques" refer to technologies for interpreting collected text data and performing semantic analysis, including tokenization and sentiment analysis.

[0210] "Consistency verification measures" are processes that remove redundant data and unreliable information in order to confirm that the collected data is consistent and accurate.

[0211] A "database" is a system for systematically storing data, and it is a storage system for efficiently saving and managing collected information.

[0212] A "dashboard" is an interface that visually displays data in a format that users can view, and it is a tool for providing information in real time.

[0213] "Redundant information" refers to information that is unnecessary and redundant for analysis, and is an element that hinders data integrity.

[0214] "Unreliable information" refers to data obtained from sources whose reliability is not guaranteed, and which may reduce the accuracy of the analysis results.

[0215] "Sentiment analysis" is an analytical method that evaluates the positive, negative, and neutral sentiment in text data to understand user reactions and market trends.

[0216] "Analysis" is the process of extracting and organizing useful information from collected data.

[0217] This invention relates to a system for efficiently collecting, analyzing, and visually presenting advertising campaign and marketing trend information. Specific embodiments for carrying out the invention are described in detail below.

[0218] System Configuration

[0219] The system primarily uses the following hardware and software:

[0220] 1. Terminal: A device used by the user to enter keywords. Smartphones and personal computers are used.

[0221] 2. Server: A backend system for data collection and analysis.

[0222] 3. Database: A storage system for saving collected data.

[0223] Main software used

[0224] 1. BeautifulSoup: A library for web scraping. It collects data by crawling news articles, industry reports, etc.

[0225] 2. Requests: A library for making HTTP requests. Used for accessing websites and retrieving data.

[0226] 3. NLTK: A library for natural language processing. It primarily performs tokenization, part-of-speech tagging, and sentiment analysis.

[0227] 4. Dash: A library for visualizing data. Used to build dashboards that users can view.

[0228] Processing flow

[0229] Based on the keyword received from the terminal, the server executes the following processing steps.

[0230] First, the server uses the specified keywords to collect information from data sources such as news sites and industry reports on the internet. Specifically, it performs web scraping using BeautifulSoup and Requests to collect relevant data.

[0231] Next, the collected data is analyzed using natural language processing (NLTK). Each data point is tokenized and tagged with its part of speech, and then sentiment analysis is performed to evaluate positive, negative, and neutral sentiment scores.

[0232] The analyzed data is filtered through a consistency check process that removes redundant and unreliable information. This process ensures consistent and accurate data.

[0233] The filtered data is stored in a database, and this information is presented to the user visually by building a dashboard using Dash.

[0234] Specific example

[0235] For example, if a marketing professional is planning an advertising campaign for a new product, they might enter keywords like "new product advertising" and "competitor advertising activities" to understand the latest advertising strategies and market trends of their competitors. This will collect relevant news articles and social media posts, which can then be viewed on a visual dashboard along with sentiment analysis results. This information can then be used to help build an effective advertising strategy.

[0236] Example of a prompt

[0237] "New Product Advertisement"

[0238] "Advertising activities of competing companies"

[0239] "Marketing Trends"

[0240] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0241] Step 1:

[0242] Users use their devices to enter keywords related to advertising campaigns and marketing trends that interest them. Examples of keywords include "new product advertising" and "marketing trends." The keywords entered by the user are sent from the device to the server.

[0243] Step 2:

[0244] Based on the received keywords, the server begins collecting information from multiple data sources. Specifically, it uses BeautifulSoup and Requests to collect relevant data from news sites, industry reports, social media posts, and other sources. The input is keywords, and the output is text data of news articles and posts.

[0245] Step 3:

[0246] The server analyzes the collected text data using Natural Language Processing (NLTK). This analysis tokenizes the data, tags it with parts of speech, and performs sentiment analysis. Here, the input is the collected text data, and the output is tokenized data and sentiment scores.

[0247] Step 4:

[0248] The server filters the analyzed data and verifies its consistency. Specifically, it removes redundant and unreliable information to ensure data consistency. The input is the analyzed data, and the output is reliable and consistent data.

[0249] Step 5:

[0250] The server stores the filtered data in a database, along with metadata such as the date and time of collection and the source of the data. The input is consistent, parsed data, and the output is in the format stored in the database.

[0251] Step 6:

[0252] The server builds a user-viewable dashboard based on the stored data. Information is visually organized and displayed in real time using Dash. Users can access the dashboard via their devices to view the latest advertising campaigns and marketing trend information. The input is data stored in the database, and the output is a visual dashboard provided to the user.

[0253] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0254] This invention relates to an information gathering system for efficiently building sales strategies, and incorporates an emotion engine that recognizes user emotions and optimizes information provision. The system's program processing is described below in natural language.

[0255] This system encompasses a series of processes, from collecting business challenges, competitor information, and investor relations (IR) information based on keywords, to data analysis using natural language processing, filtering, data storage, and information delivery. Furthermore, it incorporates an emotion engine to recognize user emotions and customize information delivery according to their emotional state.

[0256] System operation

[0257] 1. Start the information gathering task.

[0258] The user uses their device to input keywords for information gathering and initiates the process. For example, they might specify specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction."

[0259] The terminal receives the keyword entered by the user and sends it to the server.

[0260] 2. Execution of information gathering

[0261] Based on the received keywords, the server begins collecting information on specified business challenges, competitors, and investor relations (IR) information. Specifically, it uses a web crawler to collect relevant information from publicly available sources on the internet (news sites, corporate investor relations pages, industry reports, etc.).

[0262] The server temporarily stores the collected text data in storage.

[0263] 3. Data Analysis

[0264] The server performs natural language processing (NLP) on the collected text data to extract important information that matches keywords. For example, it might organize information such as "Competitor company X announced new product Y" from multiple news articles.

[0265] 4. Filtering and Integrity Verification

[0266] The server filters the analyzed data, removing redundant information and noise. Furthermore, it cross-checks data from multiple sources to ensure consistency.

[0267] 5. Data Storage

[0268] The server stores the verified data in the database. This data also stores metadata such as the date and time of collection, source of information, and related keywords.

[0269] 6. How the Emotion Engine Works

[0270] The server activates the emotion engine based on information stored in the database, as well as user usage history and behavioral data. The emotion engine analyzes user input data and behavioral data to evaluate the user's emotional state.

[0271] The emotion engine uses text analysis and pattern recognition to categorize the emotions a user is currently experiencing, such as stress, excitement, and calmness.

[0272] 7. Creating and customizing the dashboard

[0273] The server customizes the information it provides based on the user's emotional state, as assessed by the emotion engine. For example, if the user is experiencing high stress, it will present only the most important information concisely.

[0274] The server generates information tailored to the user in a dashboard format and sends it to the terminal.

[0275] 8. Interaction with Users

[0276] The terminal presents the generated customized dashboard to the user. The user can intuitively understand the information collected through the dashboard and make decisions for the next step.

[0277] The user checks the information customized by the emotion engine and formulates a business strategy based on it.

[0278] Specific Example: New Product Launch of Competitor Company X and User's Emotional State

[0279] 1. Task Start

[0280] The user inputs keywords such as "Competitor Company X", "New Product Launch", and "Market Reaction" into the terminal to instruct information collection.

[0281] 2. Information Collection and Analysis

[0282] The server executes a web crawler based on these keywords, collects relevant news articles and industry reports, and analyzes them using NLP.

[0283] 3. Data Sorting and Storage

[0284] The server filters redundant data and stores consistent information in the database.

[0285] 4. Emotion Evaluation and Customization of Provided Information

[0286] The server's emotion engine evaluates how the user has viewed this information in the past and the current emotional state, and customizes the information displayed on the dashboard based on this.

[0287] Through the above process, the present invention can quickly and efficiently provide collected and analyzed information according to the user's emotional state, thereby supporting the development of sales strategies. This system achieves more appropriate information provision by considering the user's emotions, improving the accuracy and efficiency of strategy formulation.

[0288] The following describes the processing flow.

[0289] Step 1:

[0290] The user uses their device to enter keywords for information gathering and initiates the data collection process. For example, they might specify specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction."

[0291] Step 2:

[0292] The device receives the keyword entered by the user and sends it to the server. Along with the keyword, it can also send the user ID and the purpose of the data collection.

[0293] Step 3:

[0294] The server will begin collecting information based on the keywords it receives. It will launch a web crawler and search for relevant websites, news articles, corporate investor relations pages, industry reports, and more.

[0295] Step 4:

[0296] The server temporarily stores the text data collected by the web crawler in storage. This data also includes metadata such as the source and date of collection.

[0297] Step 5:

[0298] The server will perform natural language processing (NLP) on the temporarily stored text data. It will perform tokenization, part-of-speech tagging, keyword extraction, etc., and analyze important text information.

[0299] Step 6:

[0300] The server will filter the analyzed data. It will remove redundant information and noise, and exclude unreliable information. For example, it will exclude spam data that repeats the same content and irrelevant information.

[0301] Step 7:

[0302] The server will cross-check the filtered data. It will compare the information obtained from multiple information sources and confirm consistency. Data with contradictions will be excluded.

[0303] Step 8:

[0304] The server will save the data with confirmed consistency to the database. At this time, metadata such as the collection date and time, information source, and related keywords will also be saved together with the data.

[0305] Step 9:

[0306] The server will activate the sentiment engine based on the information stored in the database, the user's usage history, and behavioral data. The sentiment engine will analyze the user's input data and behavioral data and evaluate the user's emotional state.

[0307] Step 10:

[0308] The server's sentiment engine will use text analysis and pattern recognition to evaluate and categorize the emotions such as stress, excitement, and calmness that the user is currently experiencing.

[0309] Step 11:

[0310] The server customizes the information it provides based on the user's emotional state, as assessed by the emotion engine. For example, if the user is experiencing high stress, it will present only the most important information concisely.

[0311] Step 12:

[0312] The server creates a dashboard based on customized information. The dashboard is visually designed so that analysis results and important information can be viewed at a glance.

[0313] Step 13:

[0314] The device receives a dashboard generated from the server and displays it to the user. The dashboard presents information that takes the user's emotional state into consideration.

[0315] Step 14:

[0316] Users can intuitively grasp the information collected through the dashboard and formulate sales strategies. Furthermore, they can request additional information collection and detailed reports as needed.

[0317] Step 15:

[0318] Based on feedback from the emotion engine, the server provides follow-up information and alerts tailored to the user's emotional state. For example, it provides real-time notifications in case of changes in the basic situation or new competitive information.

[0319] This allows users to quickly develop more appropriate and effective sales strategies. The system of the present invention can improve the accuracy and efficiency of information provision and enhance the user experience by taking into account the user's emotional state.

[0320] (Example 2)

[0321] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0322] In today's business environment, companies need to collect and analyze data from diverse sources to quickly and efficiently develop sales strategies. However, conventional systems are time-consuming for data collection and analysis, and lack mechanisms to consider the emotional state of users, meaning the information provided is not always optimal for the user. A system is needed to solve this problem and provide information efficiently and accurately.

[0323] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0324] In this invention, the server includes means for collecting data from multiple data sources, including business challenges, competitor information, and information disclosure materials, based on keywords received from a terminal; natural language processing means for analyzing the collected data and extracting important information related to the specified keywords; means for filtering the analyzed data and verifying its consistency; emotion engine means for analyzing the user's usage history and behavioral data and evaluating their emotional state; means for customizing the information provided to the user based on the evaluated emotional state; and means for constructing a dashboard that displays the customized information. This enables efficient and optimal information provision that takes into account the user's emotional state.

[0325] A "terminal" is an electronic device used by users to input information and communicate with a server.

[0326] A "server" is a computer system that processes data received from users and provides analysis results.

[0327] A "keyword" is a specific word or phrase that a user enters to specify the type of information they want to collect.

[0328] "Business challenges" refer to problems and challenges that a company faces, and are information that influences the formulation of sales strategies.

[0329] "Competitive information" refers to information about competitors, and is data used to understand the trends and activities of competitors.

[0330] "Disclosure documents" are official documents, primarily financial and strategic information, that companies make public.

[0331] A "data source" is the location or database where the original data used for information gathering resides.

[0332] "Means of data collection" refer to methods and tools for obtaining necessary information from multiple data sources.

[0333] "Natural language processing methods" are technologies used to analyze collected text data and extract specific information.

[0334] "Filtering methods" refer to methods or tools used to remove unnecessary information or noise from analyzed data.

[0335] "Means of storing data in a database" refers to methods and systems for long-term storage of data whose consistency has been verified.

[0336] "Usage history" refers to a record of actions a user has taken or information they have accessed in the past.

[0337] "Behavioral data" refers to data about the operations and actions that users perform within the system.

[0338] An "emotional engine" refers to technologies and tools that analyze user input data and behavioral data to evaluate the user's emotional state.

[0339] "Means of customization" refer to methods and tools for adjusting the information provided based on the user's emotional state and displaying it in the most optimal way.

[0340] "Methods for building dashboards" refer to the technologies and tools used to create interfaces for visually displaying analysis results and customized information.

[0341] This invention is an information provision system for efficiently building sales strategies, and it features an emotion engine that recognizes user emotions and optimizes information accordingly. This system encompasses a series of processes, from collecting business challenges, competitor information, and disclosure materials based on keywords, to data analysis using natural language processing, filtering, data storage, and information provision. Furthermore, it incorporates an emotion engine that recognizes user emotions and customizes and provides information according to their emotional state.

[0342] The system's implementation includes the following steps:

[0343] First, the user enters keywords for information collection using their device. For example, they specify specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction." The device then sends the entered keywords to the server.

[0344] The server uses a web crawler (e.g., Scrapy) based on the received keywords to collect relevant data from publicly available sources such as news sites, corporate investor relations pages, and industry reports on the internet. The collected data is temporarily stored in storage (e.g., Amazon S3).

[0345] Next, the server analyzes the collected data using natural language processing (NLP) tools (e.g., spaCy or BERT) to extract important information related to the specified keywords. The analyzed data is then filtered to remove redundant information and noise. Furthermore, data obtained from multiple sources is cross-checked to ensure consistency.

[0346] The server stores the verified data in a database (e.g., PostgreSQL). This data also includes metadata such as the date and time of collection, source information, and related keywords.

[0347] Next, the server activates an emotion engine (e.g., TextBlob or Keras) to analyze information stored in the database, as well as the user's usage history and behavioral data. The emotion engine evaluates the user's emotional state based on the user's input data and behavioral data. For example, if a user is in a high-stress state, the emotion engine will detect this.

[0348] Based on the user's emotional state assessed by the emotion engine, the server customizes the information it provides. For example, if the user is experiencing high stress, it will present only the most important information concisely. The customized information is then generated in a dashboard format (e.g., using Grafana) and sent to the user's device.

[0349] The device presents the user with a generated, customized dashboard, which the user can use to intuitively grasp the collected information and make decisions for the next steps. The user can review the information customized by the emotion engine and develop sales strategies based on it.

[0350] Specific example

[0351] For example, consider a scenario where a user enters keywords such as "competitor X," "new product announcement," and "market reaction" into their device to request information gathering. The server uses these keywords to run a web crawler, collects relevant news articles and industry reports, and analyzes them using NLP tools. The analyzed data is filtered, and consistent information is stored in a database. The emotion engine evaluates the user's emotional state, and if it determines that the user is experiencing high stress, it displays only the most important information concisely on the dashboard.

[0352] Examples of prompt statements to input into a generative AI model include the following:

[0353] "Explain how to gather the latest information on competitor X's new product launches and generate a customized dashboard based on user sentiment in order to develop a sales strategy."

[0354] This system can provide more appropriate information by taking into account the user's emotional state, thereby improving the accuracy and efficiency of strategic planning.

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] The user logs into the terminal and enters keywords for which information should be collected. For example, they might enter specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction." These keywords are sent to the server as instructions for information collection. Input is done through the terminal's interface, and keyword data is generated.

[0358] Step 2:

[0359] The terminal sends the keywords entered by the user to the server. The terminal converts the keyword data into a packet format and transfers it to the server over the network. The output is the keyword data that arrives on the server.

[0360] Step 3:

[0361] The server launches a web crawler (e.g., Scrapy) based on the received keywords to collect relevant data from multiple sources on the internet. For example, it searches news sites, corporate investor relations pages, and industry reports. The collected data is temporarily stored in storage (e.g., Amazon S3). The input is keyword data, and the output is the collected raw text data.

[0362] Step 4:

[0363] The server analyzes the collected text data using natural language processing (NLP) tools (e.g., spaCy or BERT). It tokenizes the text data and extracts important keywords and phrases. For example, it might extract information such as "Competitor company X announced new product Y" from multiple news articles. The input is the collected text data, and the output is the analyzed key information.

[0364] Step 5:

[0365] The server filters the analyzed data, removing redundant information and noise. It cross-checks data from multiple sources to ensure consistency. This includes handling synonyms and eliminating irrelevant data. The input is the analyzed, important information, and the output is clean, consistent data.

[0366] Step 6:

[0367] The server stores the filtered data in a database (e.g., PostgreSQL). This data also stores metadata such as the date and time of collection, source of information, and related keywords. The input is clean data with verified integrity, and the output is structured data stored in the database.

[0368] Step 7:

[0369] The server activates an emotion engine (e.g., TextBlob or Keras) and analyzes information stored in the database, as well as user usage history and behavioral data. The emotion engine evaluates the user's emotional state based on user input data and behavioral data. For example, it analyzes what information the user has clicked on in the past and how long they spent on it. The input is user behavioral data and stored information, and the output is the user's emotion evaluation result.

[0370] Step 8:

[0371] The server customizes the information it provides based on the user's emotional state, as assessed by the emotion engine. For example, for a user experiencing high stress, it adjusts the presentation to show only the most important information concisely. The input consists of the user's emotion assessment results and stored information, while the output is the customized information.

[0372] Step 9:

[0373] The server generates customized information in a dashboard format (e.g., using Grafana) and sends it to the device. This allows users to intuitively grasp the information. The input is customized information, and the output is a visually displayed dashboard.

[0374] Step 10:

[0375] The device presents the user with a generated, customized dashboard. The user reviews the collected information on the dashboard and makes decisions about the next steps. The input is the visually displayed dashboard, and the output is the user's decision.

[0376] Through the above series of steps, users can obtain efficient and accurate information and formulate sales strategies. This enables the provision of optimal information that takes into account the user's emotional state.

[0377] (Application Example 2)

[0378] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0379] Current information gathering systems do not consider user emotions when providing information, making it difficult to provide optimal information tailored to the user's state. Furthermore, when users receive too much information or when it includes information of low importance, it can take a considerable amount of time and effort to make efficient decisions. This invention aims to solve these problems and realize rapid and accurate information provision based on the user's emotional state.

[0380] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0381] In this invention, the server includes means for collecting data from multiple data sources, including business challenges, competitor information, and investor relations information, based on keywords received from a terminal; natural language processing means for analyzing the collected data and extracting important information related to the specified keywords; means for filtering the analyzed data and verifying its consistency; means for storing the filtered data in a database; means for constructing a dashboard for providing the stored data to the user; emotion recognition means for evaluating the user's emotions from facial recognition and voice; and means for providing recommendation information based on the evaluated emotional state. This enables the provision of optimal information according to the user's emotional state.

[0382] A "terminal" is a computing device used by a user to input information or receive analysis results.

[0383] "Business challenges" refer to the economic, operational, and strategic problems and difficulties that companies and organizations face.

[0384] "Competitive information" refers to data about companies and products that compete with each other in the market.

[0385] "Investor relations information" refers to information used to maintain and strengthen relationships with investors, such as a company's financial situation and management strategy.

[0386] "Multiple data sources" refers to diverse locations and media that provide different information, such as websites, news articles, and reports.

[0387] Natural language processing is a technology used by computers to understand, analyze, and generate human language.

[0388] "Filtering" is the process of selecting useful data and removing unnecessary data.

[0389] A "database" is a computer system used to systematically store collected and analyzed data and manage it in a way that makes it easy to search and use.

[0390] A "dashboard" is an interface designed to allow users to quickly grasp information visually.

[0391] "Emotion recognition" is a technology that analyzes a user's facial expressions and voice data to evaluate their emotional state.

[0392] "Recommended information" refers to content that provides information and products deemed appropriate and useful based on the user's needs and emotional state.

[0393] This invention relates to an information gathering system that includes an emotion recognition system that recognizes user emotions and optimizes information provision. This system is configured as follows.

[0394] First, the terminal receives keywords from the user. The user inputs keywords related to business challenges, competitor information, and investor relations information that they have specified in advance. For example, specific keywords such as "competitors," "new product announcements," and "market reactions" can be entered. These entered keywords are then sent from the terminal to the server.

[0395] Next, the server collects relevant information from multiple data sources based on the received keywords. At this stage, it uses web crawlers and APIs to retrieve data from publicly available sources such as news sites, official company pages, and industry reports. The collected data is temporarily stored in storage.

[0396] The server analyzes the collected data using natural language processing (NLP) techniques. Specifically, it extracts important information related to specified keywords from text data. The analyzed data is filtered to remove redundant information and noise, and only information that has been verified for consistency is stored in the database.

[0397] The server then evaluates the user's emotions using facial recognition and audio data. This uses data acquired through the camera and microphone. Facial recognition utilizes libraries such as OpenCV and dlib, while natural language processing techniques are used for audio data analysis. Based on this, the user's emotional state (e.g., stress, excitement, calmness) is evaluated.

[0398] Based on the assessed emotional state, the server generates a dashboard to select and deliver the most relevant information to the user. If the user is stressed, the dashboard is customized to present only the most important information concisely. This dashboard is sent to the user's device and designed to allow them to quickly grasp the information visually.

[0399] For example, if a user enters keywords such as "competitors," "new product announcement," or "market reaction," the server will collect and analyze news articles and reports related to these keywords. Furthermore, it will evaluate the user's emotional state through facial recognition and voice analysis, and provide appropriate information in a dashboard format based on the results.

[0400] A possible prompt message could include a review such as, "I'm very happy with my recent purchase. The new smartphone case is especially great!" Based on this prompt message, the system analyzes the user's emotional state and suggests the most suitable product.

[0401] This invention enables the rapid and accurate provision of information tailored to the user's emotional state, significantly improving the efficiency of decision-making.

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The user enters keywords to be collected from their device and instructs the system to begin data collection. Input data may include terms such as "competitors," "new product announcements," and "market reactions." The server receives these keywords. The entered keyword data is transmitted, and the server prepares to collect the data.

[0405] Step 2:

[0406] The server collects data from multiple data sources based on the received keywords. It utilizes web crawlers and APIs to retrieve information from news sites, official company pages, industry reports, and other sources. The retrieved data is temporarily stored in storage. Here, the web crawler traverses web pages, searching for and retrieving new information sources.

[0407] Step 3:

[0408] The server performs natural language processing (NLP) on the collected text data. Specifically, it extracts important information related to specified keywords from the collected data. For example, it analyzes the content of an article to identify topics such as "new product announcements by competitors." The analysis results are stored as temporary data. Major text analysis processing is performed, and the data that should be highlighted is revealed.

[0409] Step 4:

[0410] The server performs filtering and data integrity checks. It removes redundant information and noise from the analyzed data. Furthermore, it cross-checks data from multiple sources to ensure consistency. Unnecessary data is removed, and data whose consistency has been verified is listed as a candidate for recommendation.

[0411] Step 5:

[0412] The server stores the filtered data in a database. This data also stores metadata such as the date and time of collection, source, and related keywords. The stored data is managed securely and efficiently so that it can be used for later analysis and provision to users.

[0413] Step 6:

[0414] The server evaluates the user's emotions based on facial recognition and voice data. It uses a camera and microphone to perform facial expression and voice analysis. Software used includes OpenCV and dlib. Emotional data is categorized into states such as "stress," "excitement," and "calmness." For example, the server evaluates the user's emotional state from facial images captured by the camera and classifies them into the appropriate category.

[0415] Step 7:

[0416] The server selects the most relevant information based on the assessed emotional state and generates a dashboard. If the user is stressed, the dashboard is customized to present highly important information concisely. The generated dashboard is sent to the device in a visually easy-to-understand format. The information displayed changes dynamically based on the user's emotional state, improving the user experience. For example, if the emotional state is assessed as "stressed," a dashboard highlighting only the most important information is provided.

[0417] Step 8:

[0418] The device displays the generated dashboard to the user. The user can quickly grasp information and make decisions for the next steps through a visually intuitive interface. The user can retrieve relevant information from the dashboard and act accordingly. For example, they can quickly review important data regarding a competitor's new product launch and formulate a strategy based on that information.

[0419] The above describes the flow of the program's processing steps for the system that implements the application example, and the specific operation of each step.

[0420] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0421] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0422] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0423] [Second Embodiment]

[0424] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0425] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0426] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0427] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0428] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0429] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0430] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0431] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0432] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0433] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0434] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0435] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0436] This invention relates to an information gathering system for efficiently building sales strategies. Based on keywords specified by the user, this system collects and analyzes business challenges, competitor information, and investor relations (IR) information, and provides this information in a dashboard format. The system's program processing is described below in natural language.

[0437] 1. Start the information gathering task.

[0438] The user uses a device to enter keywords to be collected and initiates the information collection process. For example, they might enter specific keywords such as "new product from competitor X" or "latest industry trends."

[0439] The terminal receives the keyword entered by the user and sends it to the server.

[0440] 2. Execution of information gathering

[0441] Based on the received keywords, the server begins collecting information on specified business challenges, competitors, and investor relations (IR) information. Specifically, it uses a web crawler to collect relevant information from publicly available sources on the internet (news sites, corporate investor relations pages, industry reports, etc.).

[0442] The server collects data from multiple data sources and temporarily stores it in storage.

[0443] 3. Data Analysis

[0444] The server performs natural language processing (NLP) on the collected temporary data to analyze the text data. This involves tokenization and part-of-speech tagging, which helps extract important information that matches keywords.

[0445] For example, information that "competitor company X" has announced "new product Y" is extracted from multiple news articles and then organized that information.

[0446] 4. Filtering and Integrity Verification

[0447] The server further processes the analyzed data, filtering out redundant information and noise. This is crucial for ensuring the consistency of the information.

[0448] The server cross-checks data from multiple sources and filters out inconsistent information. This improves the reliability of the collected information.

[0449] 5. Data Storage

[0450] The server stores the filtered data in a database. The database also stores metadata such as the date and time of collection, the source of the data, and related keywords.

[0451] For example, information such as "Competitor X announces new product Y" is recorded along with a specific date.

[0452] 6. Generating and delivering the dashboard

[0453] The server generates a user-viewable dashboard based on the stored data. This dashboard provides important collected information in a visually easy-to-understand manner.

[0454] The device helps users access the dashboard and ensures that the latest information is displayed in real time.

[0455] Specific example: Gathering information and developing strategies regarding a competitor X's new product announcement.

[0456] 1. Start the task

[0457] The user enters keywords such as "Competitor X," "New Product Announcement," and "Market Reaction" into the device and issues a data collection command.

[0458] 2. Information Gathering

[0459] The server crawls news articles and industry reports on the internet based on these keywords to collect the necessary information.

[0460] 3. Data Analysis

[0461] The server analyzes the collected articles using natural language processing (NLP) to extract information such as "Competitor company X has announced a new product Y." It also evaluates positive and negative market reactions using sentiment analysis.

[0462] 4. Filtering and Integrity Verification

[0463] The server filters out redundant data and verifies the consistency of data collected from multiple sources.

[0464] 5. Data Storage

[0465] The server stores organized information in a database, which also includes metadata.

[0466] 6. Dashboard generation and delivery

[0467] The server visually displays this information on a dashboard, which users can then view on their devices. This enables rapid and effective strategic planning.

[0468] Through the process described above, the present invention can efficiently collect, analyze, and provide to users the information necessary to build sales strategies. This system significantly reduces time and effort and improves the accuracy of strategy development.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] The user uses their device to enter keywords for information gathering and initiates the data collection process. For example, they might specify specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction."

[0472] Step 2:

[0473] The device receives the keyword entered by the user and sends it to the server. Along with the keyword, it can also send the user ID and the purpose of the data collection.

[0474] Step 3:

[0475] The server will begin collecting information based on the keywords it receives. It will launch a web crawler and search for relevant websites, news articles, corporate investor relations pages, industry reports, and more.

[0476] Step 4:

[0477] The server temporarily stores the text data collected by the web crawler in storage. This data also includes metadata such as the source and date of collection.

[0478] Step 5:

[0479] The server performs natural language processing (NLP) on the temporarily stored text data. It analyzes important text information by performing tokenization, part-of-speech tagging, keyword extraction, and other similar operations.

[0480] Step 6:

[0481] The server filters the analyzed data, removing redundant information and noise, and excluding unreliable information. For example, it eliminates spam data that repeats the same content or irrelevant information.

[0482] Step 7:

[0483] The server cross-checks the filtered data. It compares information from multiple sources to ensure consistency. Inconsistent data is excluded.

[0484] Step 8:

[0485] The server stores the data, whose integrity has been verified, in the database. At the same time, metadata such as the date and time of collection, source information, and related keywords are also stored with the data.

[0486] Step 9:

[0487] The server generates a dashboard based on the stored data. The dashboard is designed to be visually easy to understand, allowing users to see analysis results and important information at a glance.

[0488] Step 10:

[0489] The terminal receives data from the server and displays it to the user. The user can then view the collected information and analyze the results through the dashboard.

[0490] Step 11:

[0491] Users can develop specific sales strategies based on the information in the dashboard. They can also request additional information gathering or the generation of detailed reports as needed.

[0492] The above outlines the series of specific processing steps from information gathering to analysis and provision. This invention allows users to efficiently and quickly collect necessary information and utilize it in building their sales strategies.

[0493] (Example 1)

[0494] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0495] Traditional information gathering systems suffered from the problem of requiring a great deal of time and effort to collect and analyze necessary information. Furthermore, the filtering to ensure the reliability and consistency of the collected information was insufficient, resulting in low accuracy when used for strategy development. In addition, there was a lack of efficient means to visualize the collected information and provide it to users, limiting the processing and utilization of the information.

[0496] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0497] In this invention, the server includes means for collecting information from multiple information sources, including business challenges, competitor information, and investor relations information, based on keywords received from a terminal; natural language processing means for analyzing the collected information and extracting important information related to the specified keywords; and means for filtering the analyzed information and verifying its consistency. This makes it possible to collect necessary information efficiently and accurately and provide reliable data to the user.

[0498] A "terminal" is a computer device used by users to input information and communicate with a server.

[0499] A "keyword" is a specific word or phrase that is the target of information gathering.

[0500] "Business challenges" refer to problems and challenges that companies and organizations face.

[0501] "Competitive information" refers to information and data related to competitors.

[0502] "IR information" refers to information provided by a company to its investors, including data on its financial status and performance.

[0503] "Information sources" refer to websites, databases, and other publicly available resources that provide data and information.

[0504] "Information" is a general term referring to collected data, documents, and reports.

[0505] "Natural language processing methods" refer to technologies and algorithms for analyzing input text data and understanding its meaning.

[0506] "Filtering" refers to the process of removing redundant information and noise from collected data.

[0507] A "database" is a system for storing organized data and retrieving it as needed.

[0508] A "dashboard" is an interface for visually displaying collected and analyzed information.

[0509] Cross-checking is the process of comparing data collected from multiple sources to verify consistency and reliability.

[0510] "Sentiment analysis" refers to a technique that analyzes the content of text data and evaluates its emotional tone.

[0511] "Positive" refers to an evaluation that is affirmative or forward-looking.

[0512] "Negative" refers to an evaluation that is negative or unfavorable.

[0513] "Neutral" means that the evaluation is neither positive nor negative.

[0514] This invention relates to an information gathering system for efficiently building sales strategies. This system collects and analyzes business challenges, competitor information, and investor relations (IR) information based on keywords specified by the user, and provides this information in a dashboard format. The embodiments of this invention are described in detail below.

[0515] System Configuration

[0516] The system is broadly composed of a user interface (terminal), a server that collects and analyzes information, and a database.

[0517] terminal

[0518] A terminal is a device used by users to input information gathering tasks and access a dashboard. This includes hardware such as personal computers, tablets, and smartphones. The software running on the terminal provides a user interface using a web browser or dedicated application.

[0519] server

[0520] The server is the central component that performs information gathering, data analysis, filtering, and data storage. The main processes performed by the server are as follows:

[0521] 1. Information Gathering

[0522] The server collects information using a web crawler implemented in Python. Specifically, it uses libraries such as BeautifulSoup and Scrapy to extract relevant information from news sites, corporate investor relations pages, industry reports, and other sources on the internet.

[0523] 2. Data Analysis

[0524] The server applies natural language processing (NLP) techniques to the collected data. This involves using libraries such as spaCy and NLTK to tokenize text data, tag it with parts of speech, and extract keywords. It also performs sentiment analysis, classifying the data into positive, negative, and neutral categories.

[0525] 3. Filtering and Integrity Verification

[0526] The server filters the analyzed data, removing redundant information and noise. Furthermore, it cross-checks data collected from multiple sources to eliminate inconsistent information.

[0527] 4. Data Storage

[0528] The filtered data is stored in a database such as MySQL or PostgreSQL. The stored information also includes metadata such as the date and time of collection, source, and related keywords.

[0529] Dashboard

[0530] A dashboard is an interface that allows users to visually review collected and analyzed information. It uses data visualization libraries such as D3.js and Chart.js to display important information in graphs and charts. Users can access this dashboard from their devices to view the latest information in real time.

[0531] Specific example

[0532] Gathering information and developing strategies regarding new product announcements from competitors.

[0533] 1. Start the task

[0534] Users enter keywords such as "competitors," "new product announcements," and "market reactions" into their devices and issue commands to collect data.

[0535] 2. Information Gathering

[0536] The server uses a web crawler to collect information based on the keywords mentioned above. For example, it might use BeautifulSoup to extract articles containing specific keywords from the HTML of a news site.

[0537] The collected data will be temporarily stored in an S3 bucket.

[0538] 3. Data Analysis

[0539] The server tokenizes the collected articles using spaCy and extracts information such as "a competitor has announced a new product." It also evaluates the market reaction through sentiment analysis and determines whether it is positive, negative, or neutral.

[0540] 4. Filtering and Integrity Verification

[0541] The server filters out redundant data and removes irrelevant or duplicate information.

[0542] Furthermore, data collected from multiple sources is cross-checked to eliminate inconsistent information.

[0543] 5. Data Storage

[0544] The server stores the organized information in a PostgreSQL database, and simultaneously saves metadata.

[0545] 6. Dashboard generation and delivery

[0546] The server uses D3.js to visually display the extracted information on a dashboard. For example, it can display graphs showing market reactions or time-series data on competitors' new product announcements.

[0547] The device allows users to access the dashboard and view all information in real time.

[0548] Example of a prompt

[0549] "What are the key points for gathering information and formulating strategies to evaluate market reactions when competitors launch new products?"

[0550] As described above, the system of the present invention enables efficient and effective collection and analysis of competitive information. This allows users to quickly develop sales strategies.

[0551] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0552] Step 1: Receiving input

[0553] The user accesses the system interface using a terminal and enters the keywords to be collected. For example, they might enter keywords such as "new product announcement by a competitor."

[0554] Input: Keywords entered by the user

[0555] Output: Input confirmation screen displayed on the terminal

[0556] Step 2: Submitting the Keyword

[0557] The terminal receives the keyword entered by the user, verifies it, and sends it to the server. During this process, it also checks the format of the input data and verifies that there is no invalid input.

[0558] Input: Keyword entered by the user

[0559] Output: Keyword data received by the server

[0560] Step 3: Start gathering information

[0561] The server initiates an information gathering task based on keywords received from the terminal. The server uses a web crawler implemented in Python to collect relevant information from publicly available sources on the internet.

[0562] Operation: Uses libraries such as BeautifulSoup and Scrapy to analyze the HTML structure of a website and extract information related to specified keywords.

[0563] Input: Keyword sent from the device

[0564] Output: Collected raw data (news articles, reports, etc.)

[0565] Step 4: Save map temporarily

[0566] The server temporarily stores the collected data in storage. This often utilizes AWS S3 buckets or Google Cloud Storage.

[0567] Input: Collected raw data

[0568] Output: Temporarily saved data file

[0569] Step 5: Perform data analysis

[0570] The server analyzes the collected data using natural language processing (NLP) techniques. Tokenization, part-of-speech tagging, and keyword extraction are performed using libraries such as spaCy and NLTK.

[0571] Function: Text data analysis and extraction of important information

[0572] Input: Temporarily saved data file

[0573] Output: Analyzed information (tokenized data, list of important information)

[0574] Step 6: Filtering and Integrity Check

[0575] The server filters the analyzed data, eliminating redundant information and noise. It also cross-checks information collected from multiple data sources, removing inconsistent data.

[0576] Operation: Data deduplication and cross-checking

[0577] Input: Analyzed information

[0578] Output: Filtered, consistent data

[0579] Step 7: Storage

[0580] The server stores the filtered data in a database. During storage, metadata such as the collection date and time, source information, and related keywords are also saved. Relational databases like MySQL or PostgreSQL are utilized.

[0581] Function: Data organization and metadata addition

[0582] Input: Filtered, consistent data

[0583] Output: Organized data stored in the database

[0584] Step 8: Generate the dashboard

[0585] The server generates a dashboard based on the stored data. It uses D3.js or Chart.js to visually display the collected information.

[0586] Function: Generate data visuals (graphs, charts)

[0587] Input: Organized data stored in the database

[0588] Output: Visually displayed dashboard

[0589] Step 9: Accessing the Dashboard

[0590] The device allows users to access the dashboard and provides real-time updates.

[0591] Input: User access request

[0592] Output: Dashboard screen viewable by the user

[0593] These steps enable the system of the present invention to efficiently and accurately collect necessary information and provide reliable data to the user.

[0594] (Application Example 1)

[0595] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0596] Traditional methods for collecting and analyzing advertising campaign and marketing trend information suffered from a lack of reliability and consistency, making efficient data analysis and visual presentation difficult. Furthermore, the inadequate sentiment analysis of collected data made it difficult to accurately grasp market reactions. This hindered the development of effective advertising strategies.

[0597] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0598] In this invention, the server includes means for collecting data from multiple data sources, including advertising campaigns and marketing trend information, based on keywords received from a terminal; natural language processing means for analyzing the collected data and extracting important information related to the specified keywords; consistency verification means for filtering the analyzed data and excluding redundant or unreliable information; means for storing the filtered data in a database; and means for constructing a dashboard to visually provide the stored data to the user. This enables efficient and reliable data collection and analysis, and supports the construction of advertising strategies.

[0599] An "advertising campaign" is a systematic promotional activity conducted to increase awareness of a product or service.

[0600] "Marketing trends" refer to the latest trends in marketing methods and strategies that arise in response to market trends and changes in customer preferences.

[0601] A "data source" refers to an online source of information, such as news sites, industry reports, or social media posts, that serves as the basis for collecting information.

[0602] "Natural language processing (NLP) techniques" refer to technologies for interpreting collected text data and performing semantic analysis, including tokenization and sentiment analysis.

[0603] "Consistency verification measures" are processes that remove redundant data and unreliable information in order to confirm that the collected data is consistent and accurate.

[0604] A "database" is a system for systematically storing data, and it is a storage system for efficiently saving and managing collected information.

[0605] A "dashboard" is an interface that visually displays data in a format that users can view, and it is a tool for providing information in real time.

[0606] "Redundant information" refers to information that is unnecessary and redundant for analysis, and is an element that hinders data integrity.

[0607] "Unreliable information" refers to data obtained from sources whose reliability is not guaranteed, and which may reduce the accuracy of the analysis results.

[0608] "Sentiment analysis" is an analytical method that evaluates the positive, negative, and neutral sentiment in text data to understand user reactions and market trends.

[0609] "Analysis" is the process of extracting and organizing useful information from collected data.

[0610] This invention relates to a system for efficiently collecting, analyzing, and visually presenting advertising campaign and marketing trend information. Specific embodiments for carrying out the invention are described in detail below.

[0611] System Configuration

[0612] The system primarily uses the following hardware and software:

[0613] 1. Terminal: A device used by the user to enter keywords. Smartphones and personal computers are used.

[0614] 2. Server: A backend system for data collection and analysis.

[0615] 3. Database: A storage system for saving collected data.

[0616] Main software used

[0617] 1. BeautifulSoup: A library for web scraping. It collects data by crawling news articles, industry reports, etc.

[0618] 2. Requests: A library for making HTTP requests. Used for accessing websites and retrieving data.

[0619] 3. NLTK: A library for natural language processing. It primarily performs tokenization, part-of-speech tagging, and sentiment analysis.

[0620] 4. Dash: A library for visualizing data. Used to build dashboards that users can view.

[0621] Processing flow

[0622] Based on the keyword received from the terminal, the server executes the following processing steps.

[0623] First, the server uses the specified keywords to collect information from data sources such as news sites and industry reports on the internet. Specifically, it performs web scraping using BeautifulSoup and Requests to collect relevant data.

[0624] Next, the collected data is analyzed using natural language processing (NLTK). Each data point is tokenized and tagged with its part of speech, and then sentiment analysis is performed to evaluate positive, negative, and neutral sentiment scores.

[0625] The analyzed data is filtered through a consistency check process that removes redundant and unreliable information. This process ensures consistent and accurate data.

[0626] The filtered data is stored in a database, and this information is presented to the user visually by building a dashboard using Dash.

[0627] Specific example

[0628] For example, if a marketing professional is planning an advertising campaign for a new product, they might enter keywords like "new product advertising" and "competitor advertising activities" to understand the latest advertising strategies and market trends of their competitors. This will collect relevant news articles and social media posts, which can then be viewed on a visual dashboard along with sentiment analysis results. This information can then be used to help build an effective advertising strategy.

[0629] Example of a prompt

[0630] "New Product Advertisement"

[0631] "Advertising activities of competing companies"

[0632] "Marketing Trends"

[0633] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0634] Step 1:

[0635] Users use their devices to enter keywords related to advertising campaigns and marketing trends that interest them. Examples of keywords include "new product advertising" and "marketing trends." The keywords entered by the user are sent from the device to the server.

[0636] Step 2:

[0637] Based on the received keywords, the server begins collecting information from multiple data sources. Specifically, it uses BeautifulSoup and Requests to collect relevant data from news sites, industry reports, social media posts, and other sources. The input is keywords, and the output is text data of news articles and posts.

[0638] Step 3:

[0639] The server analyzes the collected text data using Natural Language Processing (NLTK). This analysis tokenizes the data, tags it with parts of speech, and performs sentiment analysis. Here, the input is the collected text data, and the output is tokenized data and sentiment scores.

[0640] Step 4:

[0641] The server filters the analyzed data and verifies its consistency. Specifically, it removes redundant and unreliable information to ensure data consistency. The input is the analyzed data, and the output is reliable and consistent data.

[0642] Step 5:

[0643] The server stores the filtered data in a database, along with metadata such as the date and time of collection and the source of the data. The input is consistent, parsed data, and the output is in the format stored in the database.

[0644] Step 6:

[0645] The server builds a user-viewable dashboard based on the stored data. Information is visually organized and displayed in real time using Dash. Users can access the dashboard via their devices to view the latest advertising campaigns and marketing trend information. The input is data stored in the database, and the output is a visual dashboard provided to the user.

[0646] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0647] This invention relates to an information gathering system for efficiently building sales strategies, and incorporates an emotion engine that recognizes user emotions and optimizes information provision. The system's program processing is described below in natural language.

[0648] This system encompasses a series of processes, from collecting business challenges, competitor information, and investor relations (IR) information based on keywords, to data analysis using natural language processing, filtering, data storage, and information delivery. Furthermore, it incorporates an emotion engine to recognize user emotions and customize information delivery according to their emotional state.

[0649] System operation

[0650] 1. Start the information gathering task.

[0651] The user uses their device to input keywords for information gathering and initiates the process. For example, they might specify specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction."

[0652] The terminal receives the keyword entered by the user and sends it to the server.

[0653] 2. Execution of information gathering

[0654] Based on the received keywords, the server begins collecting information on specified business challenges, competitors, and investor relations (IR) information. Specifically, it uses a web crawler to collect relevant information from publicly available sources on the internet (news sites, corporate investor relations pages, industry reports, etc.).

[0655] The server temporarily stores the collected text data in storage.

[0656] 3. Data Analysis

[0657] The server performs natural language processing (NLP) on the collected text data to extract important information that matches keywords. For example, it might organize information such as "Competitor company X announced new product Y" from multiple news articles.

[0658] 4. Filtering and Integrity Verification

[0659] The server filters the analyzed data, removing redundant information and noise. Furthermore, it cross-checks data from multiple sources to ensure consistency.

[0660] 5. Data Storage

[0661] The server stores the verified data in the database. This data also stores metadata such as the date and time of collection, source of information, and related keywords.

[0662] 6. How the Emotion Engine Works

[0663] The server activates the emotion engine based on information stored in the database, as well as user usage history and behavioral data. The emotion engine analyzes user input data and behavioral data to evaluate the user's emotional state.

[0664] The emotion engine uses text analysis and pattern recognition to categorize the emotions a user is currently experiencing, such as stress, excitement, and calmness.

[0665] 7. Creating and customizing the dashboard

[0666] The server customizes the information it provides based on the user's emotional state, as assessed by the emotion engine. For example, if the user is experiencing high stress, it will present only the most important information concisely.

[0667] The server generates information tailored to the user in a dashboard format and sends it to the terminal.

[0668] 8. User Interaction

[0669] The device then presents the user with a generated, customized dashboard. Through the dashboard, the user can intuitively grasp the information gathered and make decisions about the next steps.

[0670] Users will review information customized by an emotion engine and then develop sales strategies based on that information.

[0671] Specific example: Competitor X's new product announcement and the emotional state of users.

[0672] 1. Start the task

[0673] The user simply enters keywords such as "Competitor X," "New Product Announcement," and "Market Reaction" into the device and instructs it to gather information.

[0674] 2. Information Gathering and Analysis

[0675] The server runs a web crawler based on these keywords, collects relevant news articles and industry reports, and analyzes them using NLP.

[0676] 3. Data organization and storage

[0677] The server filters out redundant data and stores consistent information in the database.

[0678] 4. Sentimental assessment and customization of information provided

[0679] The server's sentiment engine evaluates how the user has viewed this information in the past and their current emotional state, and then customizes the information displayed on the dashboard based on that.

[0680] Through the above process, the present invention can quickly and efficiently provide collected and analyzed information according to the user's emotional state, thereby supporting the development of sales strategies. This system achieves more appropriate information provision by considering the user's emotions, improving the accuracy and efficiency of strategy formulation.

[0681] The following describes the processing flow.

[0682] Step 1:

[0683] The user uses their device to enter keywords for information gathering and initiates the data collection process. For example, they might specify specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction."

[0684] Step 2:

[0685] The device receives the keyword entered by the user and sends it to the server. Along with the keyword, it can also send the user ID and the purpose of the data collection.

[0686] Step 3:

[0687] The server will begin collecting information based on the keywords it receives. It will launch a web crawler and search for relevant websites, news articles, corporate investor relations pages, industry reports, and more.

[0688] Step 4:

[0689] The server temporarily stores the text data collected by the web crawler in storage. This data also includes metadata such as the source and date of collection.

[0690] Step 5:

[0691] The server performs natural language processing (NLP) on the temporarily stored text data. It analyzes important text information by performing tokenization, part-of-speech tagging, keyword extraction, and other similar operations.

[0692] Step 6:

[0693] The server filters the analyzed data, removing redundant information and noise, and excluding unreliable information. For example, it eliminates spam data that repeats the same content or irrelevant information.

[0694] Step 7:

[0695] The server cross-checks the filtered data. It compares information from multiple sources to ensure consistency. Inconsistent data is excluded.

[0696] Step 8:

[0697] The server stores the data, whose integrity has been verified, in the database. At the same time, metadata such as the date and time of collection, source information, and related keywords are also stored with the data.

[0698] Step 9:

[0699] The server activates the emotion engine based on information stored in the database, as well as user usage history and behavioral data. The emotion engine analyzes user input data and behavioral data to evaluate the user's emotional state.

[0700] Step 10:

[0701] The server's emotion engine uses text analysis and pattern recognition to evaluate and categorize the emotions the user is currently experiencing, such as stress, excitement, and calmness.

[0702] Step 11:

[0703] The server customizes the information it provides based on the user's emotional state, as assessed by the emotion engine. For example, if the user is experiencing high stress, it will present only the most important information concisely.

[0704] Step 12:

[0705] The server creates a dashboard based on customized information. The dashboard is visually designed so that analysis results and important information can be viewed at a glance.

[0706] Step 13:

[0707] The device receives a dashboard generated from the server and displays it to the user. The dashboard presents information that takes the user's emotional state into consideration.

[0708] Step 14:

[0709] Users can intuitively grasp the information collected through the dashboard and formulate sales strategies. Furthermore, they can request additional information collection and detailed reports as needed.

[0710] Step 15:

[0711] Based on feedback from the emotion engine, the server provides follow-up information and alerts tailored to the user's emotional state. For example, it provides real-time notifications in case of changes in the basic situation or new competitive information.

[0712] This allows users to quickly develop more appropriate and effective sales strategies. The system of the present invention can improve the accuracy and efficiency of information provision and enhance the user experience by taking into account the user's emotional state.

[0713] (Example 2)

[0714] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0715] In today's business environment, companies need to collect and analyze data from diverse sources to quickly and efficiently develop sales strategies. However, conventional systems are time-consuming for data collection and analysis, and lack mechanisms to consider the emotional state of users, meaning the information provided is not always optimal for the user. A system is needed to solve this problem and provide information efficiently and accurately.

[0716] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0717] In this invention, the server includes means for collecting data from multiple data sources, including business challenges, competitor information, and information disclosure materials, based on keywords received from a terminal; natural language processing means for analyzing the collected data and extracting important information related to the specified keywords; means for filtering the analyzed data and verifying its consistency; emotion engine means for analyzing the user's usage history and behavioral data and evaluating their emotional state; means for customizing the information provided to the user based on the evaluated emotional state; and means for constructing a dashboard that displays the customized information. This enables efficient and optimal information provision that takes into account the user's emotional state.

[0718] A "terminal" is an electronic device used by users to input information and communicate with a server.

[0719] A "server" is a computer system that processes data received from users and provides analysis results.

[0720] A "keyword" is a specific word or phrase that a user enters to specify the type of information they want to collect.

[0721] "Business challenges" refer to problems and challenges that a company faces, and are information that influences the formulation of sales strategies.

[0722] "Competitive information" refers to information about competitors, and is data used to understand the trends and activities of competitors.

[0723] "Disclosure documents" are official documents, primarily financial and strategic information, that companies make public.

[0724] A "data source" is the location or database where the original data used for information gathering resides.

[0725] "Means of data collection" refer to methods and tools for obtaining necessary information from multiple data sources.

[0726] "Natural language processing methods" are technologies used to analyze collected text data and extract specific information.

[0727] "Filtering methods" refer to methods or tools used to remove unnecessary information or noise from analyzed data.

[0728] "Means of storing data in a database" refers to methods and systems for long-term storage of data whose consistency has been verified.

[0729] "Usage history" refers to a record of actions a user has taken or information they have accessed in the past.

[0730] "Behavioral data" refers to data about the operations and actions that users perform within the system.

[0731] An "emotional engine" refers to technologies and tools that analyze user input data and behavioral data to evaluate the user's emotional state.

[0732] "Means of customization" refer to methods and tools for adjusting the information provided based on the user's emotional state and displaying it in the most optimal way.

[0733] "Methods for building dashboards" refer to the technologies and tools used to create interfaces for visually displaying analysis results and customized information.

[0734] This invention is an information provision system for efficiently building sales strategies, and it features an emotion engine that recognizes user emotions and optimizes information accordingly. This system encompasses a series of processes, from collecting business challenges, competitor information, and disclosure materials based on keywords, to data analysis using natural language processing, filtering, data storage, and information provision. Furthermore, it incorporates an emotion engine that recognizes user emotions and customizes and provides information according to their emotional state.

[0735] The system's implementation includes the following steps:

[0736] First, the user enters keywords for information collection using their device. For example, they specify specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction." The device then sends the entered keywords to the server.

[0737] The server uses a web crawler (e.g., Scrapy) based on the received keywords to collect relevant data from publicly available sources such as news sites, corporate investor relations pages, and industry reports on the internet. The collected data is temporarily stored in storage (e.g., Amazon S3).

[0738] Next, the server analyzes the collected data using natural language processing (NLP) tools (e.g., spaCy or BERT) to extract important information related to the specified keywords. The analyzed data is then filtered to remove redundant information and noise. Furthermore, data obtained from multiple sources is cross-checked to ensure consistency.

[0739] The server stores the verified data in a database (e.g., PostgreSQL). This data also includes metadata such as the date and time of collection, source information, and related keywords.

[0740] Next, the server activates an emotion engine (e.g., TextBlob or Keras) to analyze information stored in the database, as well as the user's usage history and behavioral data. The emotion engine evaluates the user's emotional state based on the user's input data and behavioral data. For example, if a user is in a high-stress state, the emotion engine will detect this.

[0741] Based on the user's emotional state assessed by the emotion engine, the server customizes the information it provides. For example, if the user is experiencing high stress, it will present only the most important information concisely. The customized information is then generated in a dashboard format (e.g., using Grafana) and sent to the user's device.

[0742] The device presents the user with a generated, customized dashboard, which the user can use to intuitively grasp the collected information and make decisions for the next steps. The user can review the information customized by the emotion engine and develop sales strategies based on it.

[0743] Specific example

[0744] For example, consider a scenario where a user enters keywords such as "competitor X," "new product announcement," and "market reaction" into their device to request information gathering. The server uses these keywords to run a web crawler, collects relevant news articles and industry reports, and analyzes them using NLP tools. The analyzed data is filtered, and consistent information is stored in a database. The emotion engine evaluates the user's emotional state, and if it determines that the user is experiencing high stress, it displays only the most important information concisely on the dashboard.

[0745] Examples of prompt statements to input into a generative AI model include the following:

[0746] "Explain how to gather the latest information on competitor X's new product launches and generate a customized dashboard based on user sentiment in order to develop a sales strategy."

[0747] This system can provide more appropriate information by taking into account the user's emotional state, thereby improving the accuracy and efficiency of strategic planning.

[0748] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0749] Step 1:

[0750] The user logs into the terminal and enters keywords for which information should be collected. For example, they might enter specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction." These keywords are sent to the server as instructions for information collection. Input is done through the terminal's interface, and keyword data is generated.

[0751] Step 2:

[0752] The terminal sends the keywords entered by the user to the server. The terminal converts the keyword data into a packet format and transfers it to the server over the network. The output is the keyword data that arrives on the server.

[0753] Step 3:

[0754] The server launches a web crawler (e.g., Scrapy) based on the received keywords to collect relevant data from multiple sources on the internet. For example, it searches news sites, corporate investor relations pages, and industry reports. The collected data is temporarily stored in storage (e.g., Amazon S3). The input is keyword data, and the output is the collected raw text data.

[0755] Step 4:

[0756] The server analyzes the collected text data using natural language processing (NLP) tools (e.g., spaCy or BERT). It tokenizes the text data and extracts important keywords and phrases. For example, it might extract information such as "Competitor company X announced new product Y" from multiple news articles. The input is the collected text data, and the output is the analyzed key information.

[0757] Step 5:

[0758] The server filters the analyzed data, removing redundant information and noise. It cross-checks data from multiple sources to ensure consistency. This includes handling synonyms and eliminating irrelevant data. The input is the analyzed, important information, and the output is clean, consistent data.

[0759] Step 6:

[0760] The server stores the filtered data in a database (e.g., PostgreSQL). This data also stores metadata such as the date and time of collection, source of information, and related keywords. The input is clean data with verified integrity, and the output is structured data stored in the database.

[0761] Step 7:

[0762] The server activates an emotion engine (e.g., TextBlob or Keras) and analyzes information stored in the database, as well as user usage history and behavioral data. The emotion engine evaluates the user's emotional state based on user input data and behavioral data. For example, it analyzes what information the user has clicked on in the past and how long they spent on it. The input is user behavioral data and stored information, and the output is the user's emotion evaluation result.

[0763] Step 8:

[0764] The server customizes the information it provides based on the user's emotional state, as assessed by the emotion engine. For example, for a user experiencing high stress, it adjusts the presentation to show only the most important information concisely. The input consists of the user's emotion assessment results and stored information, while the output is the customized information.

[0765] Step 9:

[0766] The server generates customized information in a dashboard format (e.g., using Grafana) and sends it to the device. This allows users to intuitively grasp the information. The input is customized information, and the output is a visually displayed dashboard.

[0767] Step 10:

[0768] The device presents the user with a generated, customized dashboard. The user reviews the collected information on the dashboard and makes decisions about the next steps. The input is the visually displayed dashboard, and the output is the user's decision.

[0769] Through the above series of steps, users can obtain efficient and accurate information and formulate sales strategies. This enables the provision of optimal information that takes into account the user's emotional state.

[0770] (Application Example 2)

[0771] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0772] Current information gathering systems do not consider user emotions when providing information, making it difficult to provide optimal information tailored to the user's state. Furthermore, when users receive too much information or when it includes information of low importance, it can take a considerable amount of time and effort to make efficient decisions. This invention aims to solve these problems and realize rapid and accurate information provision based on the user's emotional state.

[0773] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0774] In this invention, the server includes means for collecting data from multiple data sources, including business challenges, competitor information, and investor relations information, based on keywords received from a terminal; natural language processing means for analyzing the collected data and extracting important information related to the specified keywords; means for filtering the analyzed data and verifying its consistency; means for storing the filtered data in a database; means for constructing a dashboard for providing the stored data to the user; emotion recognition means for evaluating the user's emotions from facial recognition and voice; and means for providing recommendation information based on the evaluated emotional state. This enables the provision of optimal information according to the user's emotional state.

[0775] A "terminal" is a computing device used by a user to input information or receive analysis results.

[0776] "Business challenges" refer to the economic, operational, and strategic problems and difficulties that companies and organizations face.

[0777] "Competitive information" refers to data about companies and products that compete with each other in the market.

[0778] "Investor relations information" refers to information used to maintain and strengthen relationships with investors, such as a company's financial situation and management strategy.

[0779] "Multiple data sources" refers to diverse locations and media that provide different information, such as websites, news articles, and reports.

[0780] Natural language processing is a technology used by computers to understand, analyze, and generate human language.

[0781] "Filtering" is the process of selecting useful data and removing unnecessary data.

[0782] A "database" is a computer system used to systematically store collected and analyzed data and manage it in a way that makes it easy to search and use.

[0783] A "dashboard" is an interface designed to allow users to quickly grasp information visually.

[0784] "Emotion recognition" is a technology that analyzes a user's facial expressions and voice data to evaluate their emotional state.

[0785] "Recommended information" refers to content that provides information and products deemed appropriate and useful based on the user's needs and emotional state.

[0786] This invention relates to an information gathering system that includes an emotion recognition system that recognizes user emotions and optimizes information provision. This system is configured as follows.

[0787] First, the terminal receives keywords from the user. The user inputs keywords related to business challenges, competitor information, and investor relations information that they have specified in advance. For example, specific keywords such as "competitors," "new product announcements," and "market reactions" can be entered. These entered keywords are then sent from the terminal to the server.

[0788] Next, the server collects relevant information from multiple data sources based on the received keywords. At this stage, it uses web crawlers and APIs to retrieve data from publicly available sources such as news sites, official company pages, and industry reports. The collected data is temporarily stored in storage.

[0789] The server analyzes the collected data using natural language processing (NLP) techniques. Specifically, it extracts important information related to specified keywords from text data. The analyzed data is filtered to remove redundant information and noise, and only information that has been verified for consistency is stored in the database.

[0790] The server then evaluates the user's emotions using facial recognition and audio data. This uses data acquired through the camera and microphone. Facial recognition utilizes libraries such as OpenCV and dlib, while natural language processing techniques are used for audio data analysis. Based on this, the user's emotional state (e.g., stress, excitement, calmness) is evaluated.

[0791] Based on the assessed emotional state, the server generates a dashboard to select and deliver the most relevant information to the user. If the user is stressed, the dashboard is customized to present only the most important information concisely. This dashboard is sent to the user's device and designed to allow them to quickly grasp the information visually.

[0792] For example, if a user enters keywords such as "competitors," "new product announcement," or "market reaction," the server will collect and analyze news articles and reports related to these keywords. Furthermore, it will evaluate the user's emotional state through facial recognition and voice analysis, and provide appropriate information in a dashboard format based on the results.

[0793] A possible prompt message could include a review such as, "I'm very happy with my recent purchase. The new smartphone case is especially great!" Based on this prompt message, the system analyzes the user's emotional state and suggests the most suitable product.

[0794] This invention enables the rapid and accurate provision of information tailored to the user's emotional state, significantly improving the efficiency of decision-making.

[0795] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0796] Step 1:

[0797] The user enters keywords to be collected from their device and instructs the system to begin data collection. Input data may include terms such as "competitors," "new product announcements," and "market reactions." The server receives these keywords. The entered keyword data is transmitted, and the server prepares to collect the data.

[0798] Step 2:

[0799] The server collects data from multiple data sources based on the received keywords. It utilizes web crawlers and APIs to retrieve information from news sites, official company pages, industry reports, and other sources. The retrieved data is temporarily stored in storage. Here, the web crawler traverses web pages, searching for and retrieving new information sources.

[0800] Step 3:

[0801] The server performs natural language processing (NLP) on the collected text data. Specifically, it extracts important information related to specified keywords from the collected data. For example, it analyzes the content of an article to identify topics such as "new product announcements by competitors." The analysis results are stored as temporary data. Major text analysis processing is performed, and the data that should be highlighted is revealed.

[0802] Step 4:

[0803] The server performs filtering and data integrity checks. It removes redundant information and noise from the analyzed data. Furthermore, it cross-checks data from multiple sources to ensure consistency. Unnecessary data is removed, and data whose consistency has been verified is listed as a candidate for recommendation.

[0804] Step 5:

[0805] The server stores the filtered data in a database. This data also stores metadata such as the date and time of collection, source, and related keywords. The stored data is managed securely and efficiently so that it can be used for later analysis and provision to users.

[0806] Step 6:

[0807] The server evaluates the user's emotions based on facial recognition and voice data. It uses a camera and microphone to perform facial expression and voice analysis. Software used includes OpenCV and dlib. Emotional data is categorized into states such as "stress," "excitement," and "calmness." For example, the server evaluates the user's emotional state from facial images captured by the camera and classifies them into the appropriate category.

[0808] Step 7:

[0809] The server selects the most relevant information based on the assessed emotional state and generates a dashboard. If the user is stressed, the dashboard is customized to present highly important information concisely. The generated dashboard is sent to the device in a visually easy-to-understand format. The information displayed changes dynamically based on the user's emotional state, improving the user experience. For example, if the emotional state is assessed as "stressed," a dashboard highlighting only the most important information is provided.

[0810] Step 8:

[0811] The device displays the generated dashboard to the user. The user can quickly grasp information and make decisions for the next steps through a visually intuitive interface. The user can retrieve relevant information from the dashboard and act accordingly. For example, they can quickly review important data regarding a competitor's new product launch and formulate a strategy based on that information.

[0812] The above describes the flow of the program's processing steps for the system that implements the application example, and the specific operation of each step.

[0813] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0814] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0815] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0816] [Third Embodiment]

[0817] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0818] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0819] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0820] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0821] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0822] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0823] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0824] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0825] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0826] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0827] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0828] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0829] This invention relates to an information gathering system for efficiently building sales strategies. Based on keywords specified by the user, this system collects and analyzes business challenges, competitor information, and investor relations (IR) information, and provides this information in a dashboard format. The system's program processing is described below in natural language.

[0830] 1. Start the information gathering task.

[0831] The user uses a device to enter keywords to be collected and initiates the information collection process. For example, they might enter specific keywords such as "new product from competitor X" or "latest industry trends."

[0832] The terminal receives the keyword entered by the user and sends it to the server.

[0833] 2. Execution of information gathering

[0834] Based on the received keywords, the server begins collecting information on specified business challenges, competitors, and investor relations (IR) information. Specifically, it uses a web crawler to collect relevant information from publicly available sources on the internet (news sites, corporate investor relations pages, industry reports, etc.).

[0835] The server collects data from multiple data sources and temporarily stores it in storage.

[0836] 3. Data Analysis

[0837] The server performs natural language processing (NLP) on the collected temporary data to analyze the text data. This involves tokenization and part-of-speech tagging, which helps extract important information that matches keywords.

[0838] For example, information that "competitor company X" has announced "new product Y" is extracted from multiple news articles and then organized that information.

[0839] 4. Filtering and Integrity Verification

[0840] The server further processes the analyzed data, filtering out redundant information and noise. This is crucial for ensuring the consistency of the information.

[0841] The server cross-checks data from multiple sources and filters out inconsistent information. This improves the reliability of the collected information.

[0842] 5. Data Storage

[0843] The server stores the filtered data in a database. The database also stores metadata such as the date and time of collection, the source of the data, and related keywords.

[0844] For example, information such as "Competitor X announces new product Y" is recorded along with a specific date.

[0845] 6. Generating and delivering the dashboard

[0846] The server generates a user-viewable dashboard based on the stored data. This dashboard provides important collected information in a visually easy-to-understand manner.

[0847] The device helps users access the dashboard and ensures that the latest information is displayed in real time.

[0848] Specific example: Gathering information and developing strategies regarding a competitor X's new product announcement.

[0849] 1. Start the task

[0850] The user enters keywords such as "Competitor X," "New Product Announcement," and "Market Reaction" into the device and issues a data collection command.

[0851] 2. Information Gathering

[0852] The server crawls news articles and industry reports on the internet based on these keywords to collect the necessary information.

[0853] 3. Data Analysis

[0854] The server analyzes the collected articles using natural language processing (NLP) to extract information such as "Competitor company X has announced a new product Y." It also evaluates positive and negative market reactions using sentiment analysis.

[0855] 4. Filtering and Integrity Verification

[0856] The server filters out redundant data and verifies the consistency of data collected from multiple sources.

[0857] 5. Data Storage

[0858] The server stores organized information in a database, which also includes metadata.

[0859] 6. Dashboard generation and delivery

[0860] The server visually displays this information on a dashboard, which users can then view on their devices. This enables rapid and effective strategic planning.

[0861] Through the process described above, the present invention can efficiently collect, analyze, and provide to users the information necessary to build sales strategies. This system significantly reduces time and effort and improves the accuracy of strategy development.

[0862] The following describes the processing flow.

[0863] Step 1:

[0864] The user uses their device to enter keywords for information gathering and initiates the data collection process. For example, they might specify specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction."

[0865] Step 2:

[0866] The device receives the keyword entered by the user and sends it to the server. Along with the keyword, it can also send the user ID and the purpose of the data collection.

[0867] Step 3:

[0868] The server will begin collecting information based on the keywords it receives. It will launch a web crawler and search for relevant websites, news articles, corporate investor relations pages, industry reports, and more.

[0869] Step 4:

[0870] The server temporarily stores the text data collected by the web crawler in storage. This data also includes metadata such as the source and date of collection.

[0871] Step 5:

[0872] The server performs natural language processing (NLP) on the temporarily stored text data. It analyzes important text information by performing tokenization, part-of-speech tagging, keyword extraction, and other similar operations.

[0873] Step 6:

[0874] The server filters the analyzed data, removing redundant information and noise, and excluding unreliable information. For example, it eliminates spam data that repeats the same content or irrelevant information.

[0875] Step 7:

[0876] The server cross-checks the filtered data. It compares information from multiple sources to ensure consistency. Inconsistent data is excluded.

[0877] Step 8:

[0878] The server stores the data, whose integrity has been verified, in the database. At the same time, metadata such as the date and time of collection, source information, and related keywords are also stored with the data.

[0879] Step 9:

[0880] The server generates a dashboard based on the stored data. The dashboard is designed to be visually easy to understand, allowing users to see analysis results and important information at a glance.

[0881] Step 10:

[0882] The terminal receives data from the server and displays it to the user. The user can then view the collected information and analyze the results through the dashboard.

[0883] Step 11:

[0884] Users can develop specific sales strategies based on the information in the dashboard. They can also request additional information gathering or the generation of detailed reports as needed.

[0885] The above outlines the series of specific processing steps from information gathering to analysis and provision. This invention allows users to efficiently and quickly collect necessary information and utilize it in building their sales strategies.

[0886] (Example 1)

[0887] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0888] Traditional information gathering systems suffered from the problem of requiring a great deal of time and effort to collect and analyze necessary information. Furthermore, the filtering to ensure the reliability and consistency of the collected information was insufficient, resulting in low accuracy when used for strategy development. In addition, there was a lack of efficient means to visualize the collected information and provide it to users, limiting the processing and utilization of the information.

[0889] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0890] In this invention, the server includes means for collecting information from multiple information sources, including business challenges, competitor information, and investor relations information, based on keywords received from a terminal; natural language processing means for analyzing the collected information and extracting important information related to the specified keywords; and means for filtering the analyzed information and verifying its consistency. This makes it possible to collect necessary information efficiently and accurately and provide reliable data to the user.

[0891] A "terminal" is a computer device used by users to input information and communicate with a server.

[0892] A "keyword" is a specific word or phrase that is the target of information gathering.

[0893] "Business challenges" refer to problems and challenges that companies and organizations face.

[0894] "Competitive information" refers to information and data related to competitors.

[0895] "IR information" refers to information provided by a company to its investors, including data on its financial status and performance.

[0896] "Information sources" refer to websites, databases, and other publicly available resources that provide data and information.

[0897] "Information" is a general term referring to collected data, documents, and reports.

[0898] "Natural language processing methods" refer to technologies and algorithms for analyzing input text data and understanding its meaning.

[0899] "Filtering" refers to the process of removing redundant information and noise from collected data.

[0900] A "database" is a system for storing organized data and retrieving it as needed.

[0901] A "dashboard" is an interface for visually displaying collected and analyzed information.

[0902] Cross-checking is the process of comparing data collected from multiple sources to verify consistency and reliability.

[0903] "Sentiment analysis" refers to a technique that analyzes the content of text data and evaluates its emotional tone.

[0904] "Positive" refers to an evaluation that is affirmative or forward-looking.

[0905] "Negative" refers to an evaluation that is negative or unfavorable.

[0906] "Neutral" means that the evaluation is neither positive nor negative.

[0907] This invention relates to an information gathering system for efficiently building sales strategies. This system collects and analyzes business challenges, competitor information, and investor relations (IR) information based on keywords specified by the user, and provides this information in a dashboard format. The embodiments of this invention are described in detail below.

[0908] System Configuration

[0909] The system is broadly composed of a user interface (terminal), a server that collects and analyzes information, and a database.

[0910] terminal

[0911] A terminal is a device used by users to input information gathering tasks and access a dashboard. This includes hardware such as personal computers, tablets, and smartphones. The software running on the terminal provides a user interface using a web browser or dedicated application.

[0912] server

[0913] The server is the central component that performs information gathering, data analysis, filtering, and data storage. The main processes performed by the server are as follows:

[0914] 1. Information Gathering

[0915] The server collects information using a web crawler implemented in Python. Specifically, it uses libraries such as BeautifulSoup and Scrapy to extract relevant information from news sites, corporate investor relations pages, industry reports, and other sources on the internet.

[0916] 2. Data Analysis

[0917] The server applies natural language processing (NLP) techniques to the collected data. This involves using libraries such as spaCy and NLTK to tokenize text data, tag it with parts of speech, and extract keywords. It also performs sentiment analysis, classifying the data into positive, negative, and neutral categories.

[0918] 3. Filtering and Integrity Verification

[0919] The server filters the analyzed data, removing redundant information and noise. Furthermore, it cross-checks data collected from multiple sources to eliminate inconsistent information.

[0920] 4. Data Storage

[0921] The filtered data is stored in a database such as MySQL or PostgreSQL. The stored information also includes metadata such as the date and time of collection, source, and related keywords.

[0922] Dashboard

[0923] A dashboard is an interface that allows users to visually review collected and analyzed information. It uses data visualization libraries such as D3.js and Chart.js to display important information in graphs and charts. Users can access this dashboard from their devices to view the latest information in real time.

[0924] Specific example

[0925] Gathering information and developing strategies regarding new product announcements from competitors.

[0926] 1. Start the task

[0927] Users enter keywords such as "competitors," "new product announcements," and "market reactions" into their devices and issue commands to collect data.

[0928] 2. Information Gathering

[0929] The server uses a web crawler to collect information based on the keywords mentioned above. For example, it might use BeautifulSoup to extract articles containing specific keywords from the HTML of a news site.

[0930] The collected data will be temporarily stored in an S3 bucket.

[0931] 3. Data Analysis

[0932] The server tokenizes the collected articles using spaCy and extracts information such as "a competitor has announced a new product." It also evaluates the market reaction through sentiment analysis and determines whether it is positive, negative, or neutral.

[0933] 4. Filtering and Integrity Verification

[0934] The server filters out redundant data and removes irrelevant or duplicate information.

[0935] Furthermore, data collected from multiple sources is cross-checked to eliminate inconsistent information.

[0936] 5. Data Storage

[0937] The server stores the organized information in a PostgreSQL database, and simultaneously saves metadata.

[0938] 6. Dashboard generation and delivery

[0939] The server uses D3.js to visually display the extracted information on a dashboard. For example, it can display graphs showing market reactions or time-series data on competitors' new product announcements.

[0940] The device allows users to access the dashboard and view all information in real time.

[0941] Example of a prompt

[0942] "What are the key points for gathering information and formulating strategies to evaluate market reactions when competitors launch new products?"

[0943] As described above, the system of the present invention enables efficient and effective collection and analysis of competitive information. This allows users to quickly develop sales strategies.

[0944] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0945] Step 1: Receiving input

[0946] The user accesses the system interface using a terminal and enters the keywords to be collected. For example, they might enter keywords such as "new product announcement by a competitor."

[0947] Input: Keywords entered by the user

[0948] Output: Input confirmation screen displayed on the terminal

[0949] Step 2: Submitting the Keyword

[0950] The terminal receives the keyword entered by the user, verifies it, and sends it to the server. During this process, it also checks the format of the input data and verifies that there is no invalid input.

[0951] Input: Keyword entered by the user

[0952] Output: Keyword data received by the server

[0953] Step 3: Start gathering information

[0954] The server initiates an information gathering task based on keywords received from the terminal. The server uses a web crawler implemented in Python to collect relevant information from publicly available sources on the internet.

[0955] Operation: Uses libraries such as BeautifulSoup and Scrapy to analyze the HTML structure of a website and extract information related to specified keywords.

[0956] Input: Keyword sent from the device

[0957] Output: Collected raw data (news articles, reports, etc.)

[0958] Step 4: Save map temporarily

[0959] The server temporarily stores the collected data in storage. This often utilizes AWS S3 buckets or Google Cloud Storage.

[0960] Input: Collected raw data

[0961] Output: Temporarily saved data file

[0962] Step 5: Perform data analysis

[0963] The server analyzes the collected data using natural language processing (NLP) techniques. Tokenization, part-of-speech tagging, and keyword extraction are performed using libraries such as spaCy and NLTK.

[0964] Function: Text data analysis and extraction of important information

[0965] Input: Temporarily saved data file

[0966] Output: Analyzed information (tokenized data, list of important information)

[0967] Step 6: Filtering and Integrity Check

[0968] The server filters the analyzed data, eliminating redundant information and noise. It also cross-checks information collected from multiple data sources, removing inconsistent data.

[0969] Operation: Data deduplication and cross-checking

[0970] Input: Analyzed information

[0971] Output: Filtered, consistent data

[0972] Step 7: Storage

[0973] The server stores the filtered data in a database. During storage, metadata such as the collection date and time, source information, and related keywords are also saved. Relational databases like MySQL or PostgreSQL are utilized.

[0974] Function: Data organization and metadata addition

[0975] Input: Filtered, consistent data

[0976] Output: Organized data stored in the database

[0977] Step 8: Generate the dashboard

[0978] The server generates a dashboard based on the stored data. It uses D3.js or Chart.js to visually display the collected information.

[0979] Function: Generate data visuals (graphs, charts)

[0980] Input: Organized data stored in the database

[0981] Output: Visually displayed dashboard

[0982] Step 9: Accessing the Dashboard

[0983] The device allows users to access the dashboard and provides real-time updates.

[0984] Input: User access request

[0985] Output: Dashboard screen viewable by the user

[0986] These steps enable the system of the present invention to efficiently and accurately collect necessary information and provide reliable data to the user.

[0987] (Application Example 1)

[0988] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0989] Traditional methods for collecting and analyzing advertising campaign and marketing trend information suffered from a lack of reliability and consistency, making efficient data analysis and visual presentation difficult. Furthermore, the inadequate sentiment analysis of collected data made it difficult to accurately grasp market reactions. This hindered the development of effective advertising strategies.

[0990] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0991] In this invention, the server includes means for collecting data from multiple data sources, including advertising campaigns and marketing trend information, based on keywords received from a terminal; natural language processing means for analyzing the collected data and extracting important information related to the specified keywords; consistency verification means for filtering the analyzed data and excluding redundant or unreliable information; means for storing the filtered data in a database; and means for constructing a dashboard to visually provide the stored data to the user. This enables efficient and reliable data collection and analysis, and supports the construction of advertising strategies.

[0992] An "advertising campaign" is a systematic promotional activity conducted to increase awareness of a product or service.

[0993] "Marketing trends" refer to the latest trends in marketing methods and strategies that arise in response to market trends and changes in customer preferences.

[0994] A "data source" refers to an online source of information, such as news sites, industry reports, or social media posts, that serves as the basis for collecting information.

[0995] "Natural language processing (NLP) techniques" refer to technologies for interpreting collected text data and performing semantic analysis, including tokenization and sentiment analysis.

[0996] "Consistency verification measures" are processes that remove redundant data and unreliable information in order to confirm that the collected data is consistent and accurate.

[0997] A "database" is a system for systematically storing data, and it is a storage system for efficiently saving and managing collected information.

[0998] A "dashboard" is an interface that visually displays data in a format that users can view, and it is a tool for providing information in real time.

[0999] "Redundant information" refers to information that is unnecessary and redundant for analysis, and is an element that hinders data integrity.

[1000] "Unreliable information" refers to data obtained from sources whose reliability is not guaranteed, and which may reduce the accuracy of the analysis results.

[1001] "Sentiment analysis" is an analytical method that evaluates the positive, negative, and neutral sentiment in text data to understand user reactions and market trends.

[1002] "Analysis" is the process of extracting and organizing useful information from collected data.

[1003] This invention relates to a system for efficiently collecting, analyzing, and visually presenting advertising campaign and marketing trend information. Specific embodiments for carrying out the invention are described in detail below.

[1004] System Configuration

[1005] The system primarily uses the following hardware and software:

[1006] 1. Terminal: A device used by the user to enter keywords. Smartphones and personal computers are used.

[1007] 2. Server: A backend system for data collection and analysis.

[1008] 3. Database: A storage system for saving collected data.

[1009] Main software used

[1010] 1. BeautifulSoup: A library for web scraping. It collects data by crawling news articles, industry reports, etc.

[1011] 2. Requests: A library for making HTTP requests. Used for accessing websites and retrieving data.

[1012] 3. NLTK: A library for natural language processing. It primarily performs tokenization, part-of-speech tagging, and sentiment analysis.

[1013] 4. Dash: A library for visualizing data. Used to build dashboards that users can view.

[1014] Processing flow

[1015] Based on the keyword received from the terminal, the server executes the following processing steps.

[1016] First, the server uses the specified keywords to collect information from data sources such as news sites and industry reports on the internet. Specifically, it performs web scraping using BeautifulSoup and Requests to collect relevant data.

[1017] Next, the collected data is analyzed using natural language processing (NLTK). Each data point is tokenized and tagged with its part of speech, and then sentiment analysis is performed to evaluate positive, negative, and neutral sentiment scores.

[1018] The analyzed data is filtered through a consistency check process that removes redundant and unreliable information. This process ensures consistent and accurate data.

[1019] The filtered data is stored in a database, and this information is presented to the user visually by building a dashboard using Dash.

[1020] Specific example

[1021] For example, if a marketing professional is planning an advertising campaign for a new product, they might enter keywords like "new product advertising" and "competitor advertising activities" to understand the latest advertising strategies and market trends of their competitors. This will collect relevant news articles and social media posts, which can then be viewed on a visual dashboard along with sentiment analysis results. This information can then be used to help build an effective advertising strategy.

[1022] Example of a prompt

[1023] "New Product Advertisement"

[1024] "Advertising activities of competing companies"

[1025] "Marketing Trends"

[1026] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1027] Step 1:

[1028] Users use their devices to enter keywords related to advertising campaigns and marketing trends that interest them. Examples of keywords include "new product advertising" and "marketing trends." The keywords entered by the user are sent from the device to the server.

[1029] Step 2:

[1030] Based on the received keywords, the server begins collecting information from multiple data sources. Specifically, it uses BeautifulSoup and Requests to collect relevant data from news sites, industry reports, social media posts, and other sources. The input is keywords, and the output is text data of news articles and posts.

[1031] Step 3:

[1032] The server analyzes the collected text data using Natural Language Processing (NLTK). This analysis tokenizes the data, tags it with parts of speech, and performs sentiment analysis. Here, the input is the collected text data, and the output is tokenized data and sentiment scores.

[1033] Step 4:

[1034] The server filters the analyzed data and verifies its consistency. Specifically, it removes redundant and unreliable information to ensure data consistency. The input is the analyzed data, and the output is reliable and consistent data.

[1035] Step 5:

[1036] The server stores the filtered data in a database, along with metadata such as the date and time of collection and the source of the data. The input is consistent, parsed data, and the output is in the format stored in the database.

[1037] Step 6:

[1038] The server builds a user-viewable dashboard based on the stored data. Information is visually organized and displayed in real time using Dash. Users can access the dashboard via their devices to view the latest advertising campaigns and marketing trend information. The input is data stored in the database, and the output is a visual dashboard provided to the user.

[1039] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1040] This invention relates to an information gathering system for efficiently building sales strategies, and incorporates an emotion engine that recognizes user emotions and optimizes information provision. The system's program processing is described below in natural language.

[1041] This system encompasses a series of processes, from collecting business challenges, competitor information, and investor relations (IR) information based on keywords, to data analysis using natural language processing, filtering, data storage, and information delivery. Furthermore, it incorporates an emotion engine to recognize user emotions and customize information delivery according to their emotional state.

[1042] System operation

[1043] 1. Start the information gathering task.

[1044] The user uses their device to input keywords for information gathering and initiates the process. For example, they might specify specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction."

[1045] The terminal receives the keyword entered by the user and sends it to the server.

[1046] 2. Execution of information gathering

[1047] Based on the received keywords, the server begins collecting information on specified business challenges, competitors, and investor relations (IR) information. Specifically, it uses a web crawler to collect relevant information from publicly available sources on the internet (news sites, corporate investor relations pages, industry reports, etc.).

[1048] The server temporarily stores the collected text data in storage.

[1049] 3. Data Analysis

[1050] The server performs natural language processing (NLP) on the collected text data to extract important information that matches keywords. For example, it might organize information such as "Competitor company X announced new product Y" from multiple news articles.

[1051] 4. Filtering and Integrity Verification

[1052] The server filters the analyzed data, removing redundant information and noise. Furthermore, it cross-checks data from multiple sources to ensure consistency.

[1053] 5. Data Storage

[1054] The server stores the verified data in the database. This data also stores metadata such as the date and time of collection, source of information, and related keywords.

[1055] 6. How the Emotion Engine Works

[1056] The server activates the emotion engine based on information stored in the database, as well as user usage history and behavioral data. The emotion engine analyzes user input data and behavioral data to evaluate the user's emotional state.

[1057] The emotion engine uses text analysis and pattern recognition to categorize the emotions a user is currently experiencing, such as stress, excitement, and calmness.

[1058] 7. Creating and customizing the dashboard

[1059] The server customizes the information it provides based on the user's emotional state, as assessed by the emotion engine. For example, if the user is experiencing high stress, it will present only the most important information concisely.

[1060] The server generates information tailored to the user in a dashboard format and sends it to the terminal.

[1061] 8. User Interaction

[1062] The device then presents the user with a generated, customized dashboard. Through the dashboard, the user can intuitively grasp the information gathered and make decisions about the next steps.

[1063] Users will review information customized by an emotion engine and then develop sales strategies based on that information.

[1064] Specific example: Competitor X's new product announcement and the emotional state of users.

[1065] 1. Start the task

[1066] The user simply enters keywords such as "Competitor X," "New Product Announcement," and "Market Reaction" into the device and instructs it to gather information.

[1067] 2. Information Gathering and Analysis

[1068] The server runs a web crawler based on these keywords, collects relevant news articles and industry reports, and analyzes them using NLP.

[1069] 3. Data organization and storage

[1070] The server filters out redundant data and stores consistent information in the database.

[1071] 4. Sentimental assessment and customization of information provided

[1072] The server's sentiment engine evaluates how the user has viewed this information in the past and their current emotional state, and then customizes the information displayed on the dashboard based on that.

[1073] Through the above process, the present invention can quickly and efficiently provide collected and analyzed information according to the user's emotional state, thereby supporting the development of sales strategies. This system achieves more appropriate information provision by considering the user's emotions, improving the accuracy and efficiency of strategy formulation.

[1074] The following describes the processing flow.

[1075] Step 1:

[1076] The user uses their device to enter keywords for information gathering and initiates the data collection process. For example, they might specify specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction."

[1077] Step 2:

[1078] The device receives the keyword entered by the user and sends it to the server. Along with the keyword, it can also send the user ID and the purpose of the data collection.

[1079] Step 3:

[1080] The server will begin collecting information based on the keywords it receives. It will launch a web crawler and search for relevant websites, news articles, corporate investor relations pages, industry reports, and more.

[1081] Step 4:

[1082] The server temporarily stores the text data collected by the web crawler in storage. This data also includes metadata such as the source and date of collection.

[1083] Step 5:

[1084] The server performs natural language processing (NLP) on the temporarily stored text data. It analyzes important text information by performing tokenization, part-of-speech tagging, keyword extraction, and other similar operations.

[1085] Step 6:

[1086] The server filters the analyzed data, removing redundant information and noise, and excluding unreliable information. For example, it eliminates spam data that repeats the same content or irrelevant information.

[1087] Step 7:

[1088] The server cross-checks the filtered data. It compares information from multiple sources to ensure consistency. Inconsistent data is excluded.

[1089] Step 8:

[1090] The server stores the data, whose integrity has been verified, in the database. At the same time, metadata such as the date and time of collection, source information, and related keywords are also stored with the data.

[1091] Step 9:

[1092] The server activates the emotion engine based on information stored in the database, as well as user usage history and behavioral data. The emotion engine analyzes user input data and behavioral data to evaluate the user's emotional state.

[1093] Step 10:

[1094] The server's emotion engine uses text analysis and pattern recognition to evaluate and categorize the emotions the user is currently experiencing, such as stress, excitement, and calmness.

[1095] Step 11:

[1096] The server customizes the information it provides based on the user's emotional state, as assessed by the emotion engine. For example, if the user is experiencing high stress, it will present only the most important information concisely.

[1097] Step 12:

[1098] The server creates a dashboard based on customized information. The dashboard is visually designed so that analysis results and important information can be viewed at a glance.

[1099] Step 13:

[1100] The device receives a dashboard generated from the server and displays it to the user. The dashboard presents information that takes the user's emotional state into consideration.

[1101] Step 14:

[1102] Users can intuitively grasp the information collected through the dashboard and formulate sales strategies. Furthermore, they can request additional information collection and detailed reports as needed.

[1103] Step 15:

[1104] Based on feedback from the emotion engine, the server provides follow-up information and alerts tailored to the user's emotional state. For example, it provides real-time notifications in case of changes in the basic situation or new competitive information.

[1105] This allows users to quickly develop more appropriate and effective sales strategies. The system of the present invention can improve the accuracy and efficiency of information provision and enhance the user experience by taking into account the user's emotional state.

[1106] (Example 2)

[1107] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1108] In today's business environment, companies need to collect and analyze data from diverse sources to quickly and efficiently develop sales strategies. However, conventional systems are time-consuming for data collection and analysis, and lack mechanisms to consider the emotional state of users, meaning the information provided is not always optimal for the user. A system is needed to solve this problem and provide information efficiently and accurately.

[1109] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1110] In this invention, the server includes means for collecting data from multiple data sources, including business challenges, competitor information, and information disclosure materials, based on keywords received from a terminal; natural language processing means for analyzing the collected data and extracting important information related to the specified keywords; means for filtering the analyzed data and verifying its consistency; emotion engine means for analyzing the user's usage history and behavioral data and evaluating their emotional state; means for customizing the information provided to the user based on the evaluated emotional state; and means for constructing a dashboard that displays the customized information. This enables efficient and optimal information provision that takes into account the user's emotional state.

[1111] A "terminal" is an electronic device used by users to input information and communicate with a server.

[1112] A "server" is a computer system that processes data received from users and provides analysis results.

[1113] A "keyword" is a specific word or phrase that a user enters to specify the type of information they want to collect.

[1114] "Business challenges" refer to problems and challenges that a company faces, and are information that influences the formulation of sales strategies.

[1115] "Competitive information" refers to information about competitors, and is data used to understand the trends and activities of competitors.

[1116] "Disclosure documents" are official documents, primarily financial and strategic information, that companies make public.

[1117] A "data source" is the location or database where the original data used for information gathering resides.

[1118] "Means of data collection" refer to methods and tools for obtaining necessary information from multiple data sources.

[1119] "Natural language processing methods" are technologies used to analyze collected text data and extract specific information.

[1120] "Filtering methods" refer to methods or tools used to remove unnecessary information or noise from analyzed data.

[1121] "Means of storing data in a database" refers to methods and systems for long-term storage of data whose consistency has been verified.

[1122] "Usage history" refers to a record of actions a user has taken or information they have accessed in the past.

[1123] "Behavioral data" refers to data about the operations and actions that users perform within the system.

[1124] An "emotional engine" refers to technologies and tools that analyze user input data and behavioral data to evaluate the user's emotional state.

[1125] "Means of customization" refer to methods and tools for adjusting the information provided based on the user's emotional state and displaying it in the most optimal way.

[1126] "Methods for building dashboards" refer to the technologies and tools used to create interfaces for visually displaying analysis results and customized information.

[1127] This invention is an information provision system for efficiently building sales strategies, and it features an emotion engine that recognizes user emotions and optimizes information accordingly. This system encompasses a series of processes, from collecting business challenges, competitor information, and disclosure materials based on keywords, to data analysis using natural language processing, filtering, data storage, and information provision. Furthermore, it incorporates an emotion engine that recognizes user emotions and customizes and provides information according to their emotional state.

[1128] The system's implementation includes the following steps:

[1129] First, the user enters keywords for information collection using their device. For example, they specify specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction." The device then sends the entered keywords to the server.

[1130] The server uses a web crawler (e.g., Scrapy) based on the received keywords to collect relevant data from publicly available sources such as news sites, corporate investor relations pages, and industry reports on the internet. The collected data is temporarily stored in storage (e.g., Amazon S3).

[1131] Next, the server analyzes the collected data using natural language processing (NLP) tools (e.g., spaCy or BERT) to extract important information related to the specified keywords. The analyzed data is then filtered to remove redundant information and noise. Furthermore, data obtained from multiple sources is cross-checked to ensure consistency.

[1132] The server stores the verified data in a database (e.g., PostgreSQL). This data also includes metadata such as the date and time of collection, source information, and related keywords.

[1133] Next, the server activates an emotion engine (e.g., TextBlob or Keras) to analyze information stored in the database, as well as the user's usage history and behavioral data. The emotion engine evaluates the user's emotional state based on the user's input data and behavioral data. For example, if a user is in a high-stress state, the emotion engine will detect this.

[1134] Based on the user's emotional state assessed by the emotion engine, the server customizes the information it provides. For example, if the user is experiencing high stress, it will present only the most important information concisely. The customized information is then generated in a dashboard format (e.g., using Grafana) and sent to the user's device.

[1135] The device presents the user with a generated, customized dashboard, which the user can use to intuitively grasp the collected information and make decisions for the next steps. The user can review the information customized by the emotion engine and develop sales strategies based on it.

[1136] Specific example

[1137] For example, consider a scenario where a user enters keywords such as "competitor X," "new product announcement," and "market reaction" into their device to request information gathering. The server uses these keywords to run a web crawler, collects relevant news articles and industry reports, and analyzes them using NLP tools. The analyzed data is filtered, and consistent information is stored in a database. The emotion engine evaluates the user's emotional state, and if it determines that the user is experiencing high stress, it displays only the most important information concisely on the dashboard.

[1138] Examples of prompt statements to input into a generative AI model include the following:

[1139] "Explain how to gather the latest information on competitor X's new product launches and generate a customized dashboard based on user sentiment in order to develop a sales strategy."

[1140] This system can provide more appropriate information by taking into account the user's emotional state, thereby improving the accuracy and efficiency of strategic planning.

[1141] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1142] Step 1:

[1143] The user logs into the terminal and enters keywords for which information should be collected. For example, they might enter specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction." These keywords are sent to the server as instructions for information collection. Input is done through the terminal's interface, and keyword data is generated.

[1144] Step 2:

[1145] The terminal sends the keywords entered by the user to the server. The terminal converts the keyword data into a packet format and transfers it to the server over the network. The output is the keyword data that arrives on the server.

[1146] Step 3:

[1147] The server launches a web crawler (e.g., Scrapy) based on the received keywords to collect relevant data from multiple sources on the internet. For example, it searches news sites, corporate investor relations pages, and industry reports. The collected data is temporarily stored in storage (e.g., Amazon S3). The input is keyword data, and the output is the collected raw text data.

[1148] Step 4:

[1149] The server analyzes the collected text data using natural language processing (NLP) tools (e.g., spaCy or BERT). It tokenizes the text data and extracts important keywords and phrases. For example, it might extract information such as "Competitor company X announced new product Y" from multiple news articles. The input is the collected text data, and the output is the analyzed key information.

[1150] Step 5:

[1151] The server filters the analyzed data, removing redundant information and noise. It cross-checks data from multiple sources to ensure consistency. This includes handling synonyms and eliminating irrelevant data. The input is the analyzed, important information, and the output is clean, consistent data.

[1152] Step 6:

[1153] The server stores the filtered data in a database (e.g., PostgreSQL). This data also stores metadata such as the date and time of collection, source of information, and related keywords. The input is clean data with verified integrity, and the output is structured data stored in the database.

[1154] Step 7:

[1155] The server activates an emotion engine (e.g., TextBlob or Keras) and analyzes information stored in the database, as well as user usage history and behavioral data. The emotion engine evaluates the user's emotional state based on user input data and behavioral data. For example, it analyzes what information the user has clicked on in the past and how long they spent on it. The input is user behavioral data and stored information, and the output is the user's emotion evaluation result.

[1156] Step 8:

[1157] The server customizes the information it provides based on the user's emotional state, as assessed by the emotion engine. For example, for a user experiencing high stress, it adjusts the presentation to show only the most important information concisely. The input consists of the user's emotion assessment results and stored information, while the output is the customized information.

[1158] Step 9:

[1159] The server generates customized information in a dashboard format (e.g., using Grafana) and sends it to the device. This allows users to intuitively grasp the information. The input is customized information, and the output is a visually displayed dashboard.

[1160] Step 10:

[1161] The device presents the user with a generated, customized dashboard. The user reviews the collected information on the dashboard and makes decisions about the next steps. The input is the visually displayed dashboard, and the output is the user's decision.

[1162] Through the above series of steps, users can obtain efficient and accurate information and formulate sales strategies. This enables the provision of optimal information that takes into account the user's emotional state.

[1163] (Application Example 2)

[1164] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1165] Current information gathering systems do not consider user emotions when providing information, making it difficult to provide optimal information tailored to the user's state. Furthermore, when users receive too much information or when it includes information of low importance, it can take a considerable amount of time and effort to make efficient decisions. This invention aims to solve these problems and realize rapid and accurate information provision based on the user's emotional state.

[1166] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1167] In this invention, the server includes means for collecting data from multiple data sources, including business challenges, competitor information, and investor relations information, based on keywords received from a terminal; natural language processing means for analyzing the collected data and extracting important information related to the specified keywords; means for filtering the analyzed data and verifying its consistency; means for storing the filtered data in a database; means for constructing a dashboard for providing the stored data to the user; emotion recognition means for evaluating the user's emotions from facial recognition and voice; and means for providing recommendation information based on the evaluated emotional state. This enables the provision of optimal information according to the user's emotional state.

[1168] A "terminal" is a computing device used by a user to input information or receive analysis results.

[1169] "Business challenges" refer to the economic, operational, and strategic problems and difficulties that companies and organizations face.

[1170] "Competitive information" refers to data about companies and products that compete with each other in the market.

[1171] "Investor relations information" refers to information used to maintain and strengthen relationships with investors, such as a company's financial situation and management strategy.

[1172] "Multiple data sources" refers to diverse locations and media that provide different information, such as websites, news articles, and reports.

[1173] Natural language processing is a technology used by computers to understand, analyze, and generate human language.

[1174] "Filtering" is the process of selecting useful data and removing unnecessary data.

[1175] A "database" is a computer system used to systematically store collected and analyzed data and manage it in a way that makes it easy to search and use.

[1176] A "dashboard" is an interface designed to allow users to quickly grasp information visually.

[1177] "Emotion recognition" is a technology that analyzes a user's facial expressions and voice data to evaluate their emotional state.

[1178] "Recommended information" refers to content that provides information and products deemed appropriate and useful based on the user's needs and emotional state.

[1179] This invention relates to an information gathering system that includes an emotion recognition system that recognizes user emotions and optimizes information provision. This system is configured as follows.

[1180] First, the terminal receives keywords from the user. The user inputs keywords related to business challenges, competitor information, and investor relations information that they have specified in advance. For example, specific keywords such as "competitors," "new product announcements," and "market reactions" can be entered. These entered keywords are then sent from the terminal to the server.

[1181] Next, the server collects relevant information from multiple data sources based on the received keywords. At this stage, it uses web crawlers and APIs to retrieve data from publicly available sources such as news sites, official company pages, and industry reports. The collected data is temporarily stored in storage.

[1182] The server analyzes the collected data using natural language processing (NLP) techniques. Specifically, it extracts important information related to specified keywords from text data. The analyzed data is filtered to remove redundant information and noise, and only information that has been verified for consistency is stored in the database.

[1183] The server then evaluates the user's emotions using facial recognition and audio data. This uses data acquired through the camera and microphone. Facial recognition utilizes libraries such as OpenCV and dlib, while natural language processing techniques are used for audio data analysis. Based on this, the user's emotional state (e.g., stress, excitement, calmness) is evaluated.

[1184] Based on the assessed emotional state, the server generates a dashboard to select and deliver the most relevant information to the user. If the user is stressed, the dashboard is customized to present only the most important information concisely. This dashboard is sent to the user's device and designed to allow them to quickly grasp the information visually.

[1185] For example, if a user enters keywords such as "competitors," "new product announcement," or "market reaction," the server will collect and analyze news articles and reports related to these keywords. Furthermore, it will evaluate the user's emotional state through facial recognition and voice analysis, and provide appropriate information in a dashboard format based on the results.

[1186] A possible prompt message could include a review such as, "I'm very happy with my recent purchase. The new smartphone case is especially great!" Based on this prompt message, the system analyzes the user's emotional state and suggests the most suitable product.

[1187] This invention enables the rapid and accurate provision of information tailored to the user's emotional state, significantly improving the efficiency of decision-making.

[1188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1189] Step 1:

[1190] The user enters keywords to be collected from their device and instructs the system to begin data collection. Input data may include terms such as "competitors," "new product announcements," and "market reactions." The server receives these keywords. The entered keyword data is transmitted, and the server prepares to collect the data.

[1191] Step 2:

[1192] The server collects data from multiple data sources based on the received keywords. It utilizes web crawlers and APIs to retrieve information from news sites, official company pages, industry reports, and other sources. The retrieved data is temporarily stored in storage. Here, the web crawler traverses web pages, searching for and retrieving new information sources.

[1193] Step 3:

[1194] The server performs natural language processing (NLP) on the collected text data. Specifically, it extracts important information related to specified keywords from the collected data. For example, it analyzes the content of an article to identify topics such as "new product announcements by competitors." The analysis results are stored as temporary data. Major text analysis processing is performed, and the data that should be highlighted is revealed.

[1195] Step 4:

[1196] The server performs filtering and data integrity checks. It removes redundant information and noise from the analyzed data. Furthermore, it cross-checks data from multiple sources to ensure consistency. Unnecessary data is removed, and data whose consistency has been verified is listed as a candidate for recommendation.

[1197] Step 5:

[1198] The server stores the filtered data in a database. This data also stores metadata such as the date and time of collection, source, and related keywords. The stored data is managed securely and efficiently so that it can be used for later analysis and provision to users.

[1199] Step 6:

[1200] The server evaluates the user's emotions based on facial recognition and voice data. It uses a camera and microphone to perform facial expression and voice analysis. Software used includes OpenCV and dlib. Emotional data is categorized into states such as "stress," "excitement," and "calmness." For example, the server evaluates the user's emotional state from facial images captured by the camera and classifies them into the appropriate category.

[1201] Step 7:

[1202] The server selects the most relevant information based on the assessed emotional state and generates a dashboard. If the user is stressed, the dashboard is customized to present highly important information concisely. The generated dashboard is sent to the device in a visually easy-to-understand format. The information displayed changes dynamically based on the user's emotional state, improving the user experience. For example, if the emotional state is assessed as "stressed," a dashboard highlighting only the most important information is provided.

[1203] Step 8:

[1204] The device displays the generated dashboard to the user. The user can quickly grasp information and make decisions for the next steps through a visually intuitive interface. The user can retrieve relevant information from the dashboard and act accordingly. For example, they can quickly review important data regarding a competitor's new product launch and formulate a strategy based on that information.

[1205] The above describes the flow of the program's processing steps for the system that implements the application example, and the specific operation of each step.

[1206] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1207] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1208] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1209] [Fourth Embodiment]

[1210] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1211] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1212] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1213] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1214] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1215] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1216] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1217] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1218] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1219] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1220] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1221] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1222] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1223] This invention relates to an information gathering system for efficiently building sales strategies. Based on keywords specified by the user, this system collects and analyzes business challenges, competitor information, and investor relations (IR) information, and provides this information in a dashboard format. The system's program processing is described below in natural language.

[1224] 1. Start the information gathering task.

[1225] The user uses a device to enter keywords to be collected and initiates the information collection process. For example, they might enter specific keywords such as "new product from competitor X" or "latest industry trends."

[1226] The terminal receives the keyword entered by the user and sends it to the server.

[1227] 2. Execution of information gathering

[1228] Based on the received keywords, the server begins collecting information on specified business challenges, competitors, and investor relations (IR) information. Specifically, it uses a web crawler to collect relevant information from publicly available sources on the internet (news sites, corporate investor relations pages, industry reports, etc.).

[1229] The server collects data from multiple data sources and temporarily stores it in storage.

[1230] 3. Data Analysis

[1231] The server performs natural language processing (NLP) on the collected temporary data to analyze the text data. This involves tokenization and part-of-speech tagging, which helps extract important information that matches keywords.

[1232] For example, information that "competitor company X" has announced "new product Y" is extracted from multiple news articles and then organized that information.

[1233] 4. Filtering and Integrity Verification

[1234] The server further processes the analyzed data, filtering out redundant information and noise. This is crucial for ensuring the consistency of the information.

[1235] The server cross-checks data from multiple sources and filters out inconsistent information. This improves the reliability of the collected information.

[1236] 5. Data Storage

[1237] The server stores the filtered data in a database. The database also stores metadata such as the date and time of collection, the source of the data, and related keywords.

[1238] For example, information such as "Competitor X announces new product Y" is recorded along with a specific date.

[1239] 6. Generating and delivering the dashboard

[1240] The server generates a user-viewable dashboard based on the stored data. This dashboard provides important collected information in a visually easy-to-understand manner.

[1241] The device helps users access the dashboard and ensures that the latest information is displayed in real time.

[1242] Specific example: Gathering information and developing strategies regarding a competitor X's new product announcement.

[1243] 1. Start the task

[1244] The user enters keywords such as "Competitor X," "New Product Announcement," and "Market Reaction" into the device and issues a data collection command.

[1245] 2. Information Gathering

[1246] The server crawls news articles and industry reports on the internet based on these keywords to collect the necessary information.

[1247] 3. Data Analysis

[1248] The server analyzes the collected articles using natural language processing (NLP) to extract information such as "Competitor company X has announced a new product Y." It also evaluates positive and negative market reactions using sentiment analysis.

[1249] 4. Filtering and Integrity Verification

[1250] The server filters out redundant data and verifies the consistency of data collected from multiple sources.

[1251] 5. Data Storage

[1252] The server stores organized information in a database, which also includes metadata.

[1253] 6. Dashboard generation and delivery

[1254] The server visually displays this information on a dashboard, which users can then view on their devices. This enables rapid and effective strategic planning.

[1255] Through the process described above, the present invention can efficiently collect, analyze, and provide to users the information necessary to build sales strategies. This system significantly reduces time and effort and improves the accuracy of strategy development.

[1256] The following describes the processing flow.

[1257] Step 1:

[1258] The user uses their device to enter keywords for information gathering and initiates the data collection process. For example, they might specify specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction."

[1259] Step 2:

[1260] The device receives the keyword entered by the user and sends it to the server. Along with the keyword, it can also send the user ID and the purpose of the data collection.

[1261] Step 3:

[1262] The server will begin collecting information based on the keywords it receives. It will launch a web crawler and search for relevant websites, news articles, corporate investor relations pages, industry reports, and more.

[1263] Step 4:

[1264] The server temporarily stores the text data collected by the web crawler in storage. This data also includes metadata such as the source and date of collection.

[1265] Step 5:

[1266] The server performs natural language processing (NLP) on the temporarily stored text data. It analyzes important text information by performing tokenization, part-of-speech tagging, keyword extraction, and other similar operations.

[1267] Step 6:

[1268] The server filters the analyzed data, removing redundant information and noise, and excluding unreliable information. For example, it eliminates spam data that repeats the same content or irrelevant information.

[1269] Step 7:

[1270] The server cross-checks the filtered data. It compares information from multiple sources to ensure consistency. Inconsistent data is excluded.

[1271] Step 8:

[1272] The server stores the data, whose integrity has been verified, in the database. At the same time, metadata such as the date and time of collection, source information, and related keywords are also stored with the data.

[1273] Step 9:

[1274] The server generates a dashboard based on the stored data. The dashboard is designed to be visually easy to understand, allowing users to see analysis results and important information at a glance.

[1275] Step 10:

[1276] The terminal receives data from the server and displays it to the user. The user can then view the collected information and analyze the results through the dashboard.

[1277] Step 11:

[1278] Users can develop specific sales strategies based on the information in the dashboard. They can also request additional information gathering or the generation of detailed reports as needed.

[1279] The above outlines the series of specific processing steps from information gathering to analysis and provision. This invention allows users to efficiently and quickly collect necessary information and utilize it in building their sales strategies.

[1280] (Example 1)

[1281] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1282] Traditional information gathering systems suffered from the problem of requiring a great deal of time and effort to collect and analyze necessary information. Furthermore, the filtering to ensure the reliability and consistency of the collected information was insufficient, resulting in low accuracy when used for strategy development. In addition, there was a lack of efficient means to visualize the collected information and provide it to users, limiting the processing and utilization of the information.

[1283] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1284] In this invention, the server includes means for collecting information from multiple information sources, including business challenges, competitor information, and investor relations information, based on keywords received from a terminal; natural language processing means for analyzing the collected information and extracting important information related to the specified keywords; and means for filtering the analyzed information and verifying its consistency. This makes it possible to collect necessary information efficiently and accurately and provide reliable data to the user.

[1285] A "terminal" is a computer device used by users to input information and communicate with a server.

[1286] A "keyword" is a specific word or phrase that is the target of information gathering.

[1287] "Business challenges" refer to problems and challenges that companies and organizations face.

[1288] "Competitive information" refers to information and data related to competitors.

[1289] "IR information" refers to information provided by a company to its investors, including data on its financial status and performance.

[1290] "Information sources" refer to websites, databases, and other publicly available resources that provide data and information.

[1291] "Information" is a general term referring to collected data, documents, and reports.

[1292] "Natural language processing methods" refer to technologies and algorithms for analyzing input text data and understanding its meaning.

[1293] "Filtering" refers to the process of removing redundant information and noise from collected data.

[1294] A "database" is a system for storing organized data and retrieving it as needed.

[1295] A "dashboard" is an interface for visually displaying collected and analyzed information.

[1296] Cross-checking is the process of comparing data collected from multiple sources to verify consistency and reliability.

[1297] "Sentiment analysis" refers to a technique that analyzes the content of text data and evaluates its emotional tone.

[1298] "Positive" refers to an evaluation that is affirmative or forward-looking.

[1299] "Negative" refers to an evaluation that is negative or unfavorable.

[1300] "Neutral" means that the evaluation is neither positive nor negative.

[1301] This invention relates to an information gathering system for efficiently building sales strategies. This system collects and analyzes business challenges, competitor information, and investor relations (IR) information based on keywords specified by the user, and provides this information in a dashboard format. The embodiments of this invention are described in detail below.

[1302] System Configuration

[1303] The system is broadly composed of a user interface (terminal), a server that collects and analyzes information, and a database.

[1304] terminal

[1305] A terminal is a device used by users to input information gathering tasks and access a dashboard. This includes hardware such as personal computers, tablets, and smartphones. The software running on the terminal provides a user interface using a web browser or dedicated application.

[1306] server

[1307] The server is the central component that performs information gathering, data analysis, filtering, and data storage. The main processes performed by the server are as follows:

[1308] 1. Information Gathering

[1309] The server collects information using a web crawler implemented in Python. Specifically, it uses libraries such as BeautifulSoup and Scrapy to extract relevant information from news sites, corporate investor relations pages, industry reports, and other sources on the internet.

[1310] 2. Data Analysis

[1311] The server applies natural language processing (NLP) techniques to the collected data. This involves using libraries such as spaCy and NLTK to tokenize text data, tag it with parts of speech, and extract keywords. It also performs sentiment analysis, classifying the data into positive, negative, and neutral categories.

[1312] 3. Filtering and Integrity Verification

[1313] The server filters the analyzed data, removing redundant information and noise. Furthermore, it cross-checks data collected from multiple sources to eliminate inconsistent information.

[1314] 4. Data Storage

[1315] The filtered data is stored in a database such as MySQL or PostgreSQL. The stored information also includes metadata such as the date and time of collection, source, and related keywords.

[1316] Dashboard

[1317] A dashboard is an interface that allows users to visually review collected and analyzed information. It uses data visualization libraries such as D3.js and Chart.js to display important information in graphs and charts. Users can access this dashboard from their devices to view the latest information in real time.

[1318] Specific example

[1319] Gathering information and developing strategies regarding new product announcements from competitors.

[1320] 1. Start the task

[1321] Users enter keywords such as "competitors," "new product announcements," and "market reactions" into their devices and issue commands to collect data.

[1322] 2. Information Gathering

[1323] The server uses a web crawler to collect information based on the keywords mentioned above. For example, it might use BeautifulSoup to extract articles containing specific keywords from the HTML of a news site.

[1324] The collected data will be temporarily stored in an S3 bucket.

[1325] 3. Data Analysis

[1326] The server tokenizes the collected articles using spaCy and extracts information such as "a competitor has announced a new product." It also evaluates the market reaction through sentiment analysis and determines whether it is positive, negative, or neutral.

[1327] 4. Filtering and Integrity Verification

[1328] The server filters out redundant data and removes irrelevant or duplicate information.

[1329] Furthermore, data collected from multiple sources is cross-checked to eliminate inconsistent information.

[1330] 5. Data Storage

[1331] The server stores the organized information in a PostgreSQL database, and simultaneously saves metadata.

[1332] 6. Dashboard generation and delivery

[1333] The server uses D3.js to visually display the extracted information on a dashboard. For example, it can display graphs showing market reactions or time-series data on competitors' new product announcements.

[1334] The device allows users to access the dashboard and view all information in real time.

[1335] Example of a prompt

[1336] "What are the key points for gathering information and formulating strategies to evaluate market reactions when competitors launch new products?"

[1337] As described above, the system of the present invention enables efficient and effective collection and analysis of competitive information. This allows users to quickly develop sales strategies.

[1338] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1339] Step 1: Receiving input

[1340] The user accesses the system interface using a terminal and enters the keywords to be collected. For example, they might enter keywords such as "new product announcement by a competitor."

[1341] Input: Keywords entered by the user

[1342] Output: Input confirmation screen displayed on the terminal

[1343] Step 2: Submitting the Keyword

[1344] The terminal receives the keyword entered by the user, verifies it, and sends it to the server. During this process, it also checks the format of the input data and verifies that there is no invalid input.

[1345] Input: Keyword entered by the user

[1346] Output: Keyword data received by the server

[1347] Step 3: Start gathering information

[1348] The server initiates an information gathering task based on keywords received from the terminal. The server uses a web crawler implemented in Python to collect relevant information from publicly available sources on the internet.

[1349] Operation: Uses libraries such as BeautifulSoup and Scrapy to analyze the HTML structure of a website and extract information related to specified keywords.

[1350] Input: Keyword sent from the device

[1351] Output: Collected raw data (news articles, reports, etc.)

[1352] Step 4: Save map temporarily

[1353] The server temporarily stores the collected data in storage. This often utilizes AWS S3 buckets or Google Cloud Storage.

[1354] Input: Collected raw data

[1355] Output: Temporarily saved data file

[1356] Step 5: Perform data analysis

[1357] The server analyzes the collected data using natural language processing (NLP) techniques. Tokenization, part-of-speech tagging, and keyword extraction are performed using libraries such as spaCy and NLTK.

[1358] Function: Text data analysis and extraction of important information

[1359] Input: Temporarily saved data file

[1360] Output: Analyzed information (tokenized data, list of important information)

[1361] Step 6: Filtering and Integrity Check

[1362] The server filters the analyzed data, eliminating redundant information and noise. It also cross-checks information collected from multiple data sources, removing inconsistent data.

[1363] Operation: Data deduplication and cross-checking

[1364] Input: Analyzed information

[1365] Output: Filtered, consistent data

[1366] Step 7: Storage

[1367] The server stores the filtered data in a database. During storage, metadata such as the collection date and time, source information, and related keywords are also saved. Relational databases like MySQL or PostgreSQL are utilized.

[1368] Function: Data organization and metadata addition

[1369] Input: Filtered, consistent data

[1370] Output: Organized data stored in the database

[1371] Step 8: Generate the dashboard

[1372] The server generates a dashboard based on the stored data. It uses D3.js or Chart.js to visually display the collected information.

[1373] Function: Generate data visuals (graphs, charts)

[1374] Input: Organized data stored in the database

[1375] Output: Visually displayed dashboard

[1376] Step 9: Accessing the Dashboard

[1377] The device allows users to access the dashboard and provides real-time updates.

[1378] Input: User access request

[1379] Output: Dashboard screen viewable by the user

[1380] These steps enable the system of the present invention to efficiently and accurately collect necessary information and provide reliable data to the user.

[1381] (Application Example 1)

[1382] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1383] Traditional methods for collecting and analyzing advertising campaign and marketing trend information suffered from a lack of reliability and consistency, making efficient data analysis and visual presentation difficult. Furthermore, the inadequate sentiment analysis of collected data made it difficult to accurately grasp market reactions. This hindered the development of effective advertising strategies.

[1384] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1385] In this invention, the server includes means for collecting data from multiple data sources, including advertising campaigns and marketing trend information, based on keywords received from a terminal; natural language processing means for analyzing the collected data and extracting important information related to the specified keywords; consistency verification means for filtering the analyzed data and excluding redundant or unreliable information; means for storing the filtered data in a database; and means for constructing a dashboard to visually provide the stored data to the user. This enables efficient and reliable data collection and analysis, and supports the construction of advertising strategies.

[1386] An "advertising campaign" is a systematic promotional activity conducted to increase awareness of a product or service.

[1387] "Marketing trends" refer to the latest trends in marketing methods and strategies that arise in response to market trends and changes in customer preferences.

[1388] A "data source" refers to an online source of information, such as news sites, industry reports, or social media posts, that serves as the basis for collecting information.

[1389] "Natural language processing (NLP) techniques" refer to technologies for interpreting collected text data and performing semantic analysis, including tokenization and sentiment analysis.

[1390] "Consistency verification measures" are processes that remove redundant data and unreliable information in order to confirm that the collected data is consistent and accurate.

[1391] A "database" is a system for systematically storing data, and it is a storage system for efficiently saving and managing collected information.

[1392] A "dashboard" is an interface that visually displays data in a format that users can view, and it is a tool for providing information in real time.

[1393] "Redundant information" refers to information that is unnecessary and redundant for analysis, and is an element that hinders data integrity.

[1394] "Unreliable information" refers to data obtained from sources whose reliability is not guaranteed, and which may reduce the accuracy of the analysis results.

[1395] "Sentiment analysis" is an analytical method that evaluates the positive, negative, and neutral sentiment in text data to understand user reactions and market trends.

[1396] "Analysis" is the process of extracting and organizing useful information from collected data.

[1397] This invention relates to a system for efficiently collecting, analyzing, and visually presenting advertising campaign and marketing trend information. Specific embodiments for carrying out the invention are described in detail below.

[1398] System Configuration

[1399] The system primarily uses the following hardware and software:

[1400] 1. Terminal: A device used by the user to enter keywords. Smartphones and personal computers are used.

[1401] 2. Server: A backend system for data collection and analysis.

[1402] 3. Database: A storage system for saving collected data.

[1403] Main software used

[1404] 1. BeautifulSoup: A library for web scraping. It collects data by crawling news articles, industry reports, etc.

[1405] 2. Requests: A library for making HTTP requests. Used for accessing websites and retrieving data.

[1406] 3. NLTK: A library for natural language processing. It primarily performs tokenization, part-of-speech tagging, and sentiment analysis.

[1407] 4. Dash: A library for visualizing data. Used to build dashboards that users can view.

[1408] Processing flow

[1409] Based on the keyword received from the terminal, the server executes the following processing steps.

[1410] First, the server uses the specified keywords to collect information from data sources such as news sites and industry reports on the internet. Specifically, it performs web scraping using BeautifulSoup and Requests to collect relevant data.

[1411] Next, the collected data is analyzed using natural language processing (NLTK). Each data point is tokenized and tagged with its part of speech, and then sentiment analysis is performed to evaluate positive, negative, and neutral sentiment scores.

[1412] The analyzed data is filtered through a consistency check process that removes redundant and unreliable information. This process ensures consistent and accurate data.

[1413] The filtered data is stored in a database, and this information is presented to the user visually by building a dashboard using Dash.

[1414] Specific example

[1415] For example, if a marketing professional is planning an advertising campaign for a new product, they might enter keywords like "new product advertising" and "competitor advertising activities" to understand the latest advertising strategies and market trends of their competitors. This will collect relevant news articles and social media posts, which can then be viewed on a visual dashboard along with sentiment analysis results. This information can then be used to help build an effective advertising strategy.

[1416] Example of a prompt

[1417] "New Product Advertisement"

[1418] "Advertising activities of competing companies"

[1419] "Marketing Trends"

[1420] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1421] Step 1:

[1422] Users use their devices to enter keywords related to advertising campaigns and marketing trends that interest them. Examples of keywords include "new product advertising" and "marketing trends." The keywords entered by the user are sent from the device to the server.

[1423] Step 2:

[1424] Based on the received keywords, the server begins collecting information from multiple data sources. Specifically, it uses BeautifulSoup and Requests to collect relevant data from news sites, industry reports, social media posts, and other sources. The input is keywords, and the output is text data of news articles and posts.

[1425] Step 3:

[1426] The server analyzes the collected text data using Natural Language Processing (NLTK). This analysis tokenizes the data, tags it with parts of speech, and performs sentiment analysis. Here, the input is the collected text data, and the output is tokenized data and sentiment scores.

[1427] Step 4:

[1428] The server filters the analyzed data and verifies its consistency. Specifically, it removes redundant and unreliable information to ensure data consistency. The input is the analyzed data, and the output is reliable and consistent data.

[1429] Step 5:

[1430] The server stores the filtered data in a database, along with metadata such as the date and time of collection and the source of the data. The input is consistent, parsed data, and the output is in the format stored in the database.

[1431] Step 6:

[1432] The server builds a user-viewable dashboard based on the stored data. Information is visually organized and displayed in real time using Dash. Users can access the dashboard via their devices to view the latest advertising campaigns and marketing trend information. The input is data stored in the database, and the output is a visual dashboard provided to the user.

[1433] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1434] This invention relates to an information gathering system for efficiently building sales strategies, and incorporates an emotion engine that recognizes user emotions and optimizes information provision. The system's program processing is described below in natural language.

[1435] This system encompasses a series of processes, from collecting business challenges, competitor information, and investor relations (IR) information based on keywords, to data analysis using natural language processing, filtering, data storage, and information delivery. Furthermore, it incorporates an emotion engine to recognize user emotions and customize information delivery according to their emotional state.

[1436] System operation

[1437] 1. Start the information gathering task.

[1438] The user uses their device to input keywords for information gathering and initiates the process. For example, they might specify specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction."

[1439] The terminal receives the keyword entered by the user and sends it to the server.

[1440] 2. Execution of information gathering

[1441] Based on the received keywords, the server begins collecting information on specified business challenges, competitors, and investor relations (IR) information. Specifically, it uses a web crawler to collect relevant information from publicly available sources on the internet (news sites, corporate investor relations pages, industry reports, etc.).

[1442] The server temporarily stores the collected text data in storage.

[1443] 3. Data Analysis

[1444] The server performs natural language processing (NLP) on the collected text data to extract important information that matches keywords. For example, it might organize information such as "Competitor company X announced new product Y" from multiple news articles.

[1445] 4. Filtering and Integrity Verification

[1446] The server filters the analyzed data, removing redundant information and noise. Furthermore, it cross-checks data from multiple sources to ensure consistency.

[1447] 5. Data Storage

[1448] The server stores the verified data in the database. This data also stores metadata such as the date and time of collection, source of information, and related keywords.

[1449] 6. How the Emotion Engine Works

[1450] The server activates the emotion engine based on information stored in the database, as well as user usage history and behavioral data. The emotion engine analyzes user input data and behavioral data to evaluate the user's emotional state.

[1451] The emotion engine uses text analysis and pattern recognition to categorize the emotions a user is currently experiencing, such as stress, excitement, and calmness.

[1452] 7. Creating and customizing the dashboard

[1453] The server customizes the information it provides based on the user's emotional state, as assessed by the emotion engine. For example, if the user is experiencing high stress, it will present only the most important information concisely.

[1454] The server generates information tailored to the user in a dashboard format and sends it to the terminal.

[1455] 8. User Interaction

[1456] The device then presents the user with a generated, customized dashboard. Through the dashboard, the user can intuitively grasp the information gathered and make decisions about the next steps.

[1457] Users will review information customized by an emotion engine and then develop sales strategies based on that information.

[1458] Specific example: Competitor X's new product announcement and the emotional state of users.

[1459] 1. Start the task

[1460] The user simply enters keywords such as "Competitor X," "New Product Announcement," and "Market Reaction" into the device and instructs it to gather information.

[1461] 2. Information Gathering and Analysis

[1462] The server runs a web crawler based on these keywords, collects relevant news articles and industry reports, and analyzes them using NLP.

[1463] 3. Data organization and storage

[1464] The server filters out redundant data and stores consistent information in the database.

[1465] 4. Sentimental assessment and customization of information provided

[1466] The server's sentiment engine evaluates how the user has viewed this information in the past and their current emotional state, and then customizes the information displayed on the dashboard based on that.

[1467] Through the above process, the present invention can quickly and efficiently provide collected and analyzed information according to the user's emotional state, thereby supporting the development of sales strategies. This system achieves more appropriate information provision by considering the user's emotions, improving the accuracy and efficiency of strategy formulation.

[1468] The following describes the processing flow.

[1469] Step 1:

[1470] The user uses their device to enter keywords for information gathering and initiates the data collection process. For example, they might specify specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction."

[1471] Step 2:

[1472] The device receives the keyword entered by the user and sends it to the server. Along with the keyword, it can also send the user ID and the purpose of the data collection.

[1473] Step 3:

[1474] The server will begin collecting information based on the keywords it receives. It will launch a web crawler and search for relevant websites, news articles, corporate investor relations pages, industry reports, and more.

[1475] Step 4:

[1476] The server temporarily stores the text data collected by the web crawler in storage. This data also includes metadata such as the source and date of collection.

[1477] Step 5:

[1478] The server performs natural language processing (NLP) on the temporarily stored text data. It analyzes important text information by performing tokenization, part-of-speech tagging, keyword extraction, and other similar operations.

[1479] Step 6:

[1480] The server filters the analyzed data, removing redundant information and noise, and excluding unreliable information. For example, it eliminates spam data that repeats the same content or irrelevant information.

[1481] Step 7:

[1482] The server cross-checks the filtered data. It compares information from multiple sources to ensure consistency. Inconsistent data is excluded.

[1483] Step 8:

[1484] The server stores the data, whose integrity has been verified, in the database. At the same time, metadata such as the date and time of collection, source information, and related keywords are also stored with the data.

[1485] Step 9:

[1486] The server activates the emotion engine based on information stored in the database, as well as user usage history and behavioral data. The emotion engine analyzes user input data and behavioral data to evaluate the user's emotional state.

[1487] Step 10:

[1488] The server's emotion engine uses text analysis and pattern recognition to evaluate and categorize the emotions the user is currently experiencing, such as stress, excitement, and calmness.

[1489] Step 11:

[1490] The server customizes the information it provides based on the user's emotional state, as assessed by the emotion engine. For example, if the user is experiencing high stress, it will present only the most important information concisely.

[1491] Step 12:

[1492] The server creates a dashboard based on customized information. The dashboard is visually designed so that analysis results and important information can be viewed at a glance.

[1493] Step 13:

[1494] The device receives a dashboard generated from the server and displays it to the user. The dashboard presents information that takes the user's emotional state into consideration.

[1495] Step 14:

[1496] Users can intuitively grasp the information collected through the dashboard and formulate sales strategies. Furthermore, they can request additional information collection and detailed reports as needed.

[1497] Step 15:

[1498] Based on feedback from the emotion engine, the server provides follow-up information and alerts tailored to the user's emotional state. For example, it provides real-time notifications in case of changes in the basic situation or new competitive information.

[1499] This allows users to quickly develop more appropriate and effective sales strategies. The system of the present invention can improve the accuracy and efficiency of information provision and enhance the user experience by taking into account the user's emotional state.

[1500] (Example 2)

[1501] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1502] In today's business environment, companies need to collect and analyze data from diverse sources to quickly and efficiently develop sales strategies. However, conventional systems are time-consuming for data collection and analysis, and lack mechanisms to consider the emotional state of users, meaning the information provided is not always optimal for the user. A system is needed to solve this problem and provide information efficiently and accurately.

[1503] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1504] In this invention, the server includes means for collecting data from multiple data sources, including business challenges, competitor information, and information disclosure materials, based on keywords received from a terminal; natural language processing means for analyzing the collected data and extracting important information related to the specified keywords; means for filtering the analyzed data and verifying its consistency; emotion engine means for analyzing the user's usage history and behavioral data and evaluating their emotional state; means for customizing the information provided to the user based on the evaluated emotional state; and means for constructing a dashboard that displays the customized information. This enables efficient and optimal information provision that takes into account the user's emotional state.

[1505] A "terminal" is an electronic device used by users to input information and communicate with a server.

[1506] A "server" is a computer system that processes data received from users and provides analysis results.

[1507] A "keyword" is a specific word or phrase that a user enters to specify the type of information they want to collect.

[1508] "Business challenges" refer to problems and challenges that a company faces, and are information that influences the formulation of sales strategies.

[1509] "Competitive information" refers to information about competitors, and is data used to understand the trends and activities of competitors.

[1510] "Disclosure documents" are official documents, primarily financial and strategic information, that companies make public.

[1511] A "data source" is the location or database where the original data used for information gathering resides.

[1512] "Means of data collection" refer to methods and tools for obtaining necessary information from multiple data sources.

[1513] "Natural language processing methods" are technologies used to analyze collected text data and extract specific information.

[1514] "Filtering methods" refer to methods or tools used to remove unnecessary information or noise from analyzed data.

[1515] "Means of storing data in a database" refers to methods and systems for long-term storage of data whose consistency has been verified.

[1516] "Usage history" refers to a record of actions a user has taken or information they have accessed in the past.

[1517] "Behavioral data" refers to data about the operations and actions that users perform within the system.

[1518] An "emotional engine" refers to technologies and tools that analyze user input data and behavioral data to evaluate the user's emotional state.

[1519] "Means of customization" refer to methods and tools for adjusting the information provided based on the user's emotional state and displaying it in the most optimal way.

[1520] "Methods for building dashboards" refer to the technologies and tools used to create interfaces for visually displaying analysis results and customized information.

[1521] This invention is an information provision system for efficiently building sales strategies, and it features an emotion engine that recognizes user emotions and optimizes information accordingly. This system encompasses a series of processes, from collecting business challenges, competitor information, and disclosure materials based on keywords, to data analysis using natural language processing, filtering, data storage, and information provision. Furthermore, it incorporates an emotion engine that recognizes user emotions and customizes and provides information according to their emotional state.

[1522] The system's implementation includes the following steps:

[1523] First, the user enters keywords for information collection using their device. For example, they specify specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction." The device then sends the entered keywords to the server.

[1524] The server uses a web crawler (e.g., Scrapy) based on the received keywords to collect relevant data from publicly available sources such as news sites, corporate investor relations pages, and industry reports on the internet. The collected data is temporarily stored in storage (e.g., Amazon S3).

[1525] Next, the server analyzes the collected data using natural language processing (NLP) tools (e.g., spaCy or BERT) to extract important information related to the specified keywords. The analyzed data is then filtered to remove redundant information and noise. Furthermore, data obtained from multiple sources is cross-checked to ensure consistency.

[1526] The server stores the verified data in a database (e.g., PostgreSQL). This data also includes metadata such as the date and time of collection, source information, and related keywords.

[1527] Next, the server activates an emotion engine (e.g., TextBlob or Keras) to analyze information stored in the database, as well as the user's usage history and behavioral data. The emotion engine evaluates the user's emotional state based on the user's input data and behavioral data. For example, if a user is in a high-stress state, the emotion engine will detect this.

[1528] Based on the user's emotional state assessed by the emotion engine, the server customizes the information it provides. For example, if the user is experiencing high stress, it will present only the most important information concisely. The customized information is then generated in a dashboard format (e.g., using Grafana) and sent to the user's device.

[1529] The device presents the user with a generated, customized dashboard, which the user can use to intuitively grasp the collected information and make decisions for the next steps. The user can review the information customized by the emotion engine and develop sales strategies based on it.

[1530] Specific example

[1531] For example, consider a scenario where a user enters keywords such as "competitor X," "new product announcement," and "market reaction" into their device to request information gathering. The server uses these keywords to run a web crawler, collects relevant news articles and industry reports, and analyzes them using NLP tools. The analyzed data is filtered, and consistent information is stored in a database. The emotion engine evaluates the user's emotional state, and if it determines that the user is experiencing high stress, it displays only the most important information concisely on the dashboard.

[1532] Examples of prompt statements to input into a generative AI model include the following:

[1533] "Explain how to gather the latest information on competitor X's new product launches and generate a customized dashboard based on user sentiment in order to develop a sales strategy."

[1534] This system can provide more appropriate information by taking into account the user's emotional state, thereby improving the accuracy and efficiency of strategic planning.

[1535] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1536] Step 1:

[1537] The user logs into the terminal and enters keywords for which information should be collected. For example, they might enter specific keywords such as "Competitor X," "New Product Announcement," or "Market Reaction." These keywords are sent to the server as instructions for information collection. Input is done through the terminal's interface, and keyword data is generated.

[1538] Step 2:

[1539] The terminal sends the keywords entered by the user to the server. The terminal converts the keyword data into a packet format and transfers it to the server over the network. The output is the keyword data that arrives on the server.

[1540] Step 3:

[1541] The server launches a web crawler (e.g., Scrapy) based on the received keywords to collect relevant data from multiple sources on the internet. For example, it searches news sites, corporate investor relations pages, and industry reports. The collected data is temporarily stored in storage (e.g., Amazon S3). The input is keyword data, and the output is the collected raw text data.

[1542] Step 4:

[1543] The server analyzes the collected text data using natural language processing (NLP) tools (e.g., spaCy or BERT). It tokenizes the text data and extracts important keywords and phrases. For example, it might extract information such as "Competitor company X announced new product Y" from multiple news articles. The input is the collected text data, and the output is the analyzed key information.

[1544] Step 5:

[1545] The server filters the analyzed data, removing redundant information and noise. It cross-checks data from multiple sources to ensure consistency. This includes handling synonyms and eliminating irrelevant data. The input is the analyzed, important information, and the output is clean, consistent data.

[1546] Step 6:

[1547] The server stores the filtered data in a database (e.g., PostgreSQL). This data also stores metadata such as the date and time of collection, source of information, and related keywords. The input is clean data with verified integrity, and the output is structured data stored in the database.

[1548] Step 7:

[1549] The server activates an emotion engine (e.g., TextBlob or Keras) and analyzes information stored in the database, as well as user usage history and behavioral data. The emotion engine evaluates the user's emotional state based on user input data and behavioral data. For example, it analyzes what information the user has clicked on in the past and how long they spent on it. The input is user behavioral data and stored information, and the output is the user's emotion evaluation result.

[1550] Step 8:

[1551] The server customizes the information it provides based on the user's emotional state, as assessed by the emotion engine. For example, for a user experiencing high stress, it adjusts the presentation to show only the most important information concisely. The input consists of the user's emotion assessment results and stored information, while the output is the customized information.

[1552] Step 9:

[1553] The server generates customized information in a dashboard format (e.g., using Grafana) and sends it to the device. This allows users to intuitively grasp the information. The input is customized information, and the output is a visually displayed dashboard.

[1554] Step 10:

[1555] The device presents the user with a generated, customized dashboard. The user reviews the collected information on the dashboard and makes decisions about the next steps. The input is the visually displayed dashboard, and the output is the user's decision.

[1556] Through the above series of steps, users can obtain efficient and accurate information and formulate sales strategies. This enables the provision of optimal information that takes into account the user's emotional state.

[1557] (Application Example 2)

[1558] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1559] Current information gathering systems do not consider user emotions when providing information, making it difficult to provide optimal information tailored to the user's state. Furthermore, when users receive too much information or when it includes information of low importance, it can take a considerable amount of time and effort to make efficient decisions. This invention aims to solve these problems and realize rapid and accurate information provision based on the user's emotional state.

[1560] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1561] In this invention, the server includes means for collecting data from multiple data sources, including business challenges, competitor information, and investor relations information, based on keywords received from a terminal; natural language processing means for analyzing the collected data and extracting important information related to the specified keywords; means for filtering the analyzed data and verifying its consistency; means for storing the filtered data in a database; means for constructing a dashboard for providing the stored data to the user; emotion recognition means for evaluating the user's emotions from facial recognition and voice; and means for providing recommendation information based on the evaluated emotional state. This enables the provision of optimal information according to the user's emotional state.

[1562] A "terminal" is a computing device used by a user to input information or receive analysis results.

[1563] "Business challenges" refer to the economic, operational, and strategic problems and difficulties that companies and organizations face.

[1564] "Competitive information" refers to data about companies and products that compete with each other in the market.

[1565] "Investor relations information" refers to information used to maintain and strengthen relationships with investors, such as a company's financial situation and management strategy.

[1566] "Multiple data sources" refers to diverse locations and media that provide different information, such as websites, news articles, and reports.

[1567] Natural language processing is a technology used by computers to understand, analyze, and generate human language.

[1568] "Filtering" is the process of selecting useful data and removing unnecessary data.

[1569] A "database" is a computer system used to systematically store collected and analyzed data and manage it in a way that makes it easy to search and use.

[1570] A "dashboard" is an interface designed to allow users to quickly grasp information visually.

[1571] "Emotion recognition" is a technology that analyzes a user's facial expressions and voice data to evaluate their emotional state.

[1572] "Recommended information" refers to content that provides information and products deemed appropriate and useful based on the user's needs and emotional state.

[1573] This invention relates to an information gathering system that includes an emotion recognition system that recognizes user emotions and optimizes information provision. This system is configured as follows.

[1574] First, the terminal receives keywords from the user. The user inputs keywords related to business challenges, competitor information, and investor relations information that they have specified in advance. For example, specific keywords such as "competitors," "new product announcements," and "market reactions" can be entered. These entered keywords are then sent from the terminal to the server.

[1575] Next, the server collects relevant information from multiple data sources based on the received keywords. At this stage, it uses web crawlers and APIs to retrieve data from publicly available sources such as news sites, official company pages, and industry reports. The collected data is temporarily stored in storage.

[1576] The server analyzes the collected data using natural language processing (NLP) techniques. Specifically, it extracts important information related to specified keywords from text data. The analyzed data is filtered to remove redundant information and noise, and only information that has been verified for consistency is stored in the database.

[1577] The server then evaluates the user's emotions using facial recognition and audio data. This uses data acquired through the camera and microphone. Facial recognition utilizes libraries such as OpenCV and dlib, while natural language processing techniques are used for audio data analysis. Based on this, the user's emotional state (e.g., stress, excitement, calmness) is evaluated.

[1578] Based on the assessed emotional state, the server generates a dashboard to select and deliver the most relevant information to the user. If the user is stressed, the dashboard is customized to present only the most important information concisely. This dashboard is sent to the user's device and designed to allow them to quickly grasp the information visually.

[1579] For example, if a user enters keywords such as "competitors," "new product announcement," or "market reaction," the server will collect and analyze news articles and reports related to these keywords. Furthermore, it will evaluate the user's emotional state through facial recognition and voice analysis, and provide appropriate information in a dashboard format based on the results.

[1580] A possible prompt message could include a review such as, "I'm very happy with my recent purchase. The new smartphone case is especially great!" Based on this prompt message, the system analyzes the user's emotional state and suggests the most suitable product.

[1581] This invention enables the rapid and accurate provision of information tailored to the user's emotional state, significantly improving the efficiency of decision-making.

[1582] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1583] Step 1:

[1584] The user enters keywords to be collected from their device and instructs the system to begin data collection. Input data may include terms such as "competitors," "new product announcements," and "market reactions." The server receives these keywords. The entered keyword data is transmitted, and the server prepares to collect the data.

[1585] Step 2:

[1586] The server collects data from multiple data sources based on the received keywords. It utilizes web crawlers and APIs to retrieve information from news sites, official company pages, industry reports, and other sources. The retrieved data is temporarily stored in storage. Here, the web crawler traverses web pages, searching for and retrieving new information sources.

[1587] Step 3:

[1588] The server performs natural language processing (NLP) on the collected text data. Specifically, it extracts important information related to specified keywords from the collected data. For example, it analyzes the content of an article to identify topics such as "new product announcements by competitors." The analysis results are stored as temporary data. Major text analysis processing is performed, and the data that should be highlighted is revealed.

[1589] Step 4:

[1590] The server performs filtering and data integrity checks. It removes redundant information and noise from the analyzed data. Furthermore, it cross-checks data from multiple sources to ensure consistency. Unnecessary data is removed, and data whose consistency has been verified is listed as a candidate for recommendation.

[1591] Step 5:

[1592] The server stores the filtered data in a database. This data also stores metadata such as the date and time of collection, source, and related keywords. The stored data is managed securely and efficiently so that it can be used for later analysis and provision to users.

[1593] Step 6:

[1594] The server evaluates the user's emotions based on facial recognition and voice data. It uses a camera and microphone to perform facial expression and voice analysis. Software used includes OpenCV and dlib. Emotional data is categorized into states such as "stress," "excitement," and "calmness." For example, the server evaluates the user's emotional state from facial images captured by the camera and classifies them into the appropriate category.

[1595] Step 7:

[1596] The server selects the most relevant information based on the assessed emotional state and generates a dashboard. If the user is stressed, the dashboard is customized to present highly important information concisely. The generated dashboard is sent to the device in a visually easy-to-understand format. The information displayed changes dynamically based on the user's emotional state, improving the user experience. For example, if the emotional state is assessed as "stressed," a dashboard highlighting only the most important information is provided.

[1597] Step 8:

[1598] The device displays the generated dashboard to the user. The user can quickly grasp information and make decisions for the next steps through a visually intuitive interface. The user can retrieve relevant information from the dashboard and act accordingly. For example, they can quickly review important data regarding a competitor's new product launch and formulate a strategy based on that information.

[1599] The above describes the flow of the program's processing steps for the system that implements the application example, and the specific operation of each step.

[1600] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1601] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1602] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1603] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1604] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1605] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1606] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1607] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1608] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1609] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1610] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1611] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1612] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1613] 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.

[1614] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1615] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1616] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1617] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1618] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1619] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1620] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1621] The following is further disclosed regarding the embodiments described above.

[1622] (Claim 1)

[1623] A means of collecting data from multiple data sources, including business challenges, competitor information, and IR information, based on keywords received from a terminal.

[1624] A natural language processing means for analyzing collected data and extracting important information related to specified keywords,

[1625] A means of filtering the analyzed data to verify its consistency,

[1626] A means of storing filtered data in a database,

[1627] A means of building a dashboard to provide stored data to users,

[1628] A system that includes this.

[1629] (Claim 2)

[1630] The system according to claim 1, further comprising means for cross-checking the collected data and excluding inconsistent data.

[1631] (Claim 3)

[1632] The system according to claim 1, further comprising means for performing sentiment analysis on collected data and classifying it into positive, negative, and neutral categories.

[1633] "Example 1"

[1634] (Claim 1)

[1635] A means of collecting information from multiple sources, including business challenges, competitor information, and IR information, based on keywords received from a terminal.

[1636] A natural language processing means for analyzing collected information and extracting important information related to specified keywords,

[1637] A means of filtering the analyzed information to verify its consistency,

[1638] A means of storing filtered information in a database,

[1639] A means of building a dashboard to provide stored information to users,

[1640] A system that includes this.

[1641] (Claim 2)

[1642] The system according to claim 1, further comprising means for cross-checking the collected information and excluding inconsistent information.

[1643] (Claim 3)

[1644] The system according to claim 1, further comprising means for performing sentiment analysis on collected information and classifying it into positive, negative, and neutral categories.

[1645] "Application Example 1"

[1646] (Claim 1)

[1647] A method for collecting data from multiple data sources, including advertising campaigns and marketing trend information, based on keywords received from a device,

[1648] A natural language processing means for analyzing collected data and extracting important information related to specified keywords,

[1649] A means for verifying consistency by filtering the analyzed data and excluding redundant or unreliable information,

[1650] A means of storing filtered data in a database,

[1651] A means of building a dashboard to visually present stored data to users,

[1652] A system that includes this.

[1653] (Claim 2)

[1654] The system according to claim 1, further comprising means for cross-checking the collected data and excluding inconsistent data.

[1655] (Claim 3)

[1656] The system according to claim 1, further comprising means for performing sentiment analysis on collected data and classifying it into positive, negative, and neutral categories.

[1657] "Example 2 of combining an emotion engine"

[1658] (Claim 1)

[1659] A means of collecting data from multiple data sources, including business challenges, competitor information, and disclosure documents, based on keywords received from a terminal.

[1660] A natural language processing means for analyzing collected data and extracting important information related to specified keywords,

[1661] A means of filtering the analyzed data to verify its consistency,

[1662] A means of storing filtered data in a database,

[1663] An emotion engine means for analyzing user usage history and behavioral data and evaluating emotional state,

[1664] A means of customizing the information provided to the user based on their evaluated emotional state,

[1665] Means for building a dashboard that displays customized information,

[1666] A system that includes this.

[1667] (Claim 2)

[1668] The system according to claim 1, further comprising means for cross-checking the collected data and excluding inconsistent data.

[1669] (Claim 3)

[1670] The system according to claim 1, further comprising means for performing sentiment analysis on collected data and classifying it into positive, negative, and neutral categories.

[1671] "Application example 2 when combining with an emotional engine"

[1672] (Claim 1)

[1673] A means of collecting data from multiple data sources, including business challenges, competitor information, and investor relations information, based on keywords received from a terminal.

[1674] A natural language processing means for analyzing collected data and extracting important information related to specified keywords,

[1675] A means of filtering the analyzed data to verify its consistency,

[1676] A means of storing filtered data in a database,

[1677] A means of building a dashboard to provide stored data to users,

[1678] An emotion recognition method that evaluates emotions from the user's face recognition and voice,

[1679] A means of providing recommendation information based on an evaluated emotional state,

[1680] A system that includes this.

[1681] (Claim 2)

[1682] The system according to claim 1, further comprising means for cross-checking the collected data and excluding inconsistent data.

[1683] (Claim 3)

[1684] The system according to claim 1, further comprising means for performing sentiment analysis on collected data and classifying it into positive, negative, and neutral categories. [Explanation of Symbols]

[1685] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting data from multiple data sources, including business challenges, competitor information, and IR information, based on keywords received from a terminal. A natural language processing means for analyzing collected data and extracting important information related to specified keywords, A means of filtering the analyzed data to verify its consistency, A means of storing filtered data in a database, A means of building a dashboard to provide stored data to users, A system that includes this.

2. The system according to claim 1, further comprising means for cross-checking the collected data and excluding inconsistent data.

3. The system according to claim 1, further comprising means for performing sentiment analysis on collected data and dividing it into positive, negative, and neutral categories.

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