System

Abstract

To provide a system.SOLUTION: A system includes: data acquisition means for acquiring data from a specified information source; search means for performing keyword search on the basis of the acquired data; filtering means for filtering appropriate information on the basis of the search result; display means for display the filtered information in a list; and reporting means for reporting the displayed information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Clipping web news, a daily task for public relations, poses a challenge as it involves a mixture of reprinted articles and unimportant articles, and sorting through them takes time. [Means for solving the problem]

[0005] To solve this problem, we propose a system that combines a scraping method that acquires information from specified news sites, a search method that performs keyword searches based on the acquired information, a filtering method that filters appropriate articles based on the search results, a display method that displays a list of filtered articles, and a reporting method that reports the displayed articles. This will improve the efficiency of clipping work. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] In one embodiment of the present invention, a system is implemented that combines a scraping means for acquiring information from a specified news site, a search means for performing a keyword search based on the acquired information, a filtering means for filtering appropriate articles based on the search results, a display means for displaying a list of the filtered articles, and a reporting means for reporting the displayed articles. Specifically, the scraping means is implemented using Python libraries such as BeautifulSoup and Scrapy, and a keyword search is performed based on the acquired information. To filter appropriate articles based on the search results, natural language processing technology is used to analyze the content of the articles and evaluate their importance. The filtered articles are displayed in a list, and the displayed articles are reported. Reports are made via email, an internal SNS, or the like.

[0029] "Example 2"

[0030] In another embodiment of the present invention, a system is implemented in which scraping means can acquire information from multiple news sites. Specifically, the URLs of multiple news sites are specified and information is acquired from each. This makes it possible to acquire a wider range of information.

[0031] "Example 3"

[0032] In yet another embodiment of the present invention, a system is implemented in which the reporting means can summarize and report the contents of the filtered articles. Specifically, the content of the articles is summarized using natural language processing technology and the summary is reported. This allows the content of the report to be concise.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: Obtain information from the specified news site. In this step, scraping is performed using Python libraries such as BeautifulSoup and Scrapy.

[0036] Step 2: Perform a keyword search based on the acquired information. In this step, the text information of the acquired articles is analyzed and articles containing the specified keywords are extracted.

[0037] Step 3: Filter appropriate articles based on the search results. In this step, natural language processing techniques are used to analyze the content of the articles and evaluate their importance. Articles with an importance level above a certain threshold are selected.

[0038] Step 4: List the filtered articles. This step lists the titles and links of the selected articles.

[0039] Step 5: Report the displayed articles. In this step, you report the list of displayed articles via email, internal SNS, etc.

[0040] "Example 2"

[0041] Step 1: Obtain information from multiple news sites. In this step, you specify the URLs of multiple news sites and obtain information from each of them.

[0042] Step 2: Integrate the acquired information and perform a keyword search. In this step, information acquired from multiple sites is integrated and articles containing the specified keywords are extracted.

[0043] Step 3: Based on the search results, appropriate articles are filtered and displayed as a list. In this step, steps 3 and 4 of the first embodiment are referred to.

[0044] Step 4: Report the displayed article. In this step, refer to step 5 of embodiment 1.

[0045] "Example 3"

[0046] Step 1: Obtain information from a specified news site and perform a keyword search. In this step, refer to steps 1 and 2 in the first embodiment.

[0047] Step 2: Filter appropriate articles based on the search results. In this step, refer to step 3 in example 1.

[0048] Step 3: Display a list of filtered articles. In this step, refer to step 4 of the first embodiment.

[0049] Step 4: Summarize and report the content of the displayed article. In this step, natural language processing technology is used to summarize the content of the article, and the summary is reported via email or internal social networking sites.

[0050] Example 1

[0051] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0052] Conventional information gathering systems have had difficulty efficiently acquiring data from specific information sources and quickly providing information that users are interested in. Furthermore, the process of selecting appropriate information from the acquired information and reporting it to users takes time and effort, which reduces the usefulness of the information.

[0053] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a data acquisition means for acquiring data from a specified information source, a search means for performing a keyword search based on the acquired data, a filtering means for filtering appropriate information based on the search results, a display means for displaying a list of the filtered information, and a reporting means for reporting the displayed information. This makes it possible to quickly and efficiently acquire information in which the user is interested, and to select and report appropriate information.

[0054] "Data acquisition means" refers to a means for acquiring data from a specified information source.

[0055] The "search means" is a means for performing a keyword search based on the acquired data.

[0056] The "filtering means" is a means for selecting appropriate information based on the search results.

[0057] The "display means" is a means for displaying a list of filtered information.

[0058] The "reporting means" is a means for reporting the displayed information to the user.

[0059] The present invention is a system for acquiring data from a specified information source and quickly and efficiently providing information that a user is interested in. A specific embodiment of this system will be described below.

[0060] The server uses Python libraries such as BeautifulSoup and Scrapy to retrieve data from specified sources. Specifically, BeautifulSoup is used to parse data from HTML or XML, and Scrapy is used to crawl the entire website. For example, the server retrieves the latest news articles from "https: / / example-news-site.com."

[0061] Next, the server performs a keyword search based on the acquired data. If the user specifies the keyword "generative AI model," the server searches for articles containing this keyword among the acquired news articles. The search can be performed using the standard Python library or regular expressions.

[0062] The server then filters appropriate information based on the search results. It uses natural language processing (NLP) techniques to analyze the content of articles and evaluate their importance. Specifically, it uses Python libraries such as NLTK and spaCy to analyze the content of articles and calculate an importance score. For example, the server uses NLTK to tokenize and tag articles by part of speech, and spaCy to analyze the content of articles.

[0063] The filtered information is displayed as a list on the terminal. The user can check the list of filtered information on the terminal screen. The terminal receives the list of filtered information from the server and displays it in a list using HTML or GUI.

[0064] Finally, the server reports the displayed information. The report is sent via email or an internal SNS (for example, Slack or MICROSOFT® TEAMS®). Users can receive the reported information via email or SNS. The server formats the list of filtered information into an email body or SNS message, and sends the email using the SMTP protocol or posts the message to the SNS using an API.

[0065] As a specific example, if a user specifies the keyword "generative AI model," the server performs the following processing steps.

[0066] 1. The server retrieves data from a specified source using BeautifulSoup or Scrapy.

[0067] 2. The server searches for articles containing the keyword "generative AI model" based on the acquired data.

[0068] 3. The server uses natural language processing technology to analyze the content of the articles based on the search results and evaluate their importance.

[0069] 4. The terminal displays a list of filtered articles.

[0070] 5. The server reports the displayed articles via email or internal SNS.

[0071] An example of a prompt sentence might be:

[0072] Get the latest news articles containing the keyword "generative AI model," rate them by importance, and list them. Report the rated articles by email.

[0073] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0074] Step 1: Data Acquisition

[0075] The server retrieves data from the specified sources. It receives the specified URL and list of sources as input and generates the retrieved HTML or XML data as output. Specifically, the server launches Scrapy and accesses the specified URL. Next, it uses BeautifulSoup to parse the HTML document and extract information such as the article title, body, and date.

[0076] Step 2: Keyword search

[0077] The server performs a keyword search based on the acquired data. As input, it receives a keyword specified by the user (e.g., "generative AI model") and generates a list of articles containing the keyword as output. Specifically, the server stores the acquired article data in list format and checks whether each article in the list contains the specified keyword.

[0078] Step 3: Filtering

[0079] The server filters appropriate information based on the search results. As input, it receives a list of articles obtained through keyword searches, and as output, it generates a filtered list of articles with their importance assessed. Specifically, the server uses NLTK to tokenize and tag articles with parts of speech, and spaCy to analyze the content of the articles and calculate an importance score. Articles with an importance score above a certain level are filtered and added to the list.

[0080] Step 4: List View

[0081] The terminal displays the filtered information in a list. As input, it receives the list of filtered articles from the server, and as output, it displays the information in a format that can be viewed by the user. Specifically, the terminal displays the received article list using HTML or a GUI.

[0082] Step 5: Report

[0083] The server reports the displayed information. It receives the list of filtered articles as input and generates a message to be reported by email or internal SNS as output. Specifically, the server formats the list of filtered articles into an email body or SNS message, and sends the email using the SMTP protocol or posts the message to the SNS using an API.

[0084] (Application example 1)

[0085] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0086] In modern society, there is a huge amount of information available on the Internet, making it difficult for users to efficiently collect and appropriately filter information of interest from that information. Furthermore, there is a lack of a means to quickly report filtered information, which makes it difficult for users to obtain the information they need in a timely manner. Furthermore, there is a lack of a function to summarize and report filtered information, making it difficult for users to quickly understand the information.

[0087] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0088] In this invention, the server includes scraping means for acquiring information from specified information sources, search means for performing keyword searches based on the acquired information, filtering means for filtering appropriate information based on the search results, display means for displaying a list of the filtered information, reporting means for reporting the displayed information, and reporting means for reporting the filtered information using electronic communication means. This enables users to efficiently collect information of interest, appropriately filter it, and quickly report it. In addition, by summarizing and reporting the filtered information, users can quickly understand the information.

[0089] A "scraping method" is a technique for automatically obtaining information from a specified source.

[0090] "Search means" refers to a technique for searching for information using specific keywords based on the acquired information.

[0091] "Filtering means" is a technology that selects appropriate information based on search results and removes unnecessary information.

[0092] "Display means" refers to a technique for visually presenting filtered information to the user.

[0093] "Reporting means" refers to a technique for notifying the user of the displayed information.

[0094] "Electronic communication means" refers to the technology of transmitting information using electronic means such as the Internet or email.

[0095] As an embodiment of the present invention, the following system is constructed: A server includes scraping means for acquiring information from a specified information source, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate information based on the search results, display means for displaying a list of the filtered information, reporting means for reporting the displayed information, and reporting means for reporting the filtered information using electronic communication means.

[0096] Hardware and software used

[0097] Hardware: Servers, user devices (smartphones, PCs, etc.)

[0098] Software: Python, BeautifulSoup, Scrapy, NLTK, Scikit-learn, smtplib

[0099] Data processing and calculation

[0100] 1. Scraping method: The server retrieves articles from specified sources using Python's BeautifulSoup or Scrapy. This allows you to automatically collect the necessary information from news sites on the Internet.

[0101] 2. Search method: The server searches the retrieved articles using keywords set by the user, allowing it to efficiently extract information that interests the user.

[0102] 3. Filtering method: The server analyzes the content of articles using NLTK and Scikit-learn, and evaluates their importance using TF-IDF vectorization and cosine similarity, which allows it to select relevant information and remove unnecessary information.

[0103] 4. Display: The server displays a list of the filtered information on the user's terminal, allowing the user to visually check the information of interest.

[0104] 5. Reporting: The server summarizes the filtered information and reports it to the user's email or social networking site using smtplib, allowing the user to quickly obtain important information.

[0105] Specific examples

[0106] For example, if you are developing a news filter application, you can input the following prompt sentences into your generative AI model:

[0107] Example prompt sentence:

[0108] I'm developing a news filter application. I want to implement a feature that scrapes articles from specified news sites, filters the articles based on keywords set by the user, selects and displays important articles, and reports them via email. Please generate code that meets the following requirements using Python's BeautifulSoup, NLTK, Scikit-learn, and smtplib.

[0109] News site URL: https: / / example-news-site.com

[0110] Keywords: ['AI', 'technology', 'innovation']

[0111] User email: user@example.com

[0112] In this way, it becomes possible for users to efficiently collect information of interest, filter it appropriately, and quickly report it. By summarizing and reporting the filtered information, users can quickly understand the information.

[0113] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0114] Step 1:

[0115] The server retrieves information from the specified source. Specifically, it uses Python's BeautifulSoup or Scrapy to scrape articles from news sites. It receives the URL of the news site as input and generates a list of retrieved articles as output.

[0116] Step 2:

[0117] The server searches the retrieved articles using keywords set by the user. It receives the list of retrieved articles and the keywords set by the user as input, and generates a list of articles that match the keywords as output. Specifically, it checks whether each article contains the keywords and extracts the matching articles.

[0118] Step 3:

[0119] The server filters appropriate information based on the search results. It receives a list of articles matching the keywords as input and generates a list of highly relevant articles as output. Specifically, it analyzes the content of articles using NLTK and Scikit-learn, and evaluates their importance using TF-IDF vectorization and cosine similarity.

[0120] Step 4:

[0121] The server displays the filtered information in a list on the user's device. It receives a list of highly important articles as input and generates a list of articles to be displayed on the user's device as output. Specifically, it formats the articles in HTML or JSON format and displays them in the user interface.

[0122] Step 5:

[0123] The server summarizes the filtered information and reports it to the user's email or social media. It receives a list of highly important articles as input and generates email or social media messages containing the summarized article content as output. Specifically, it sends emails using smtplib and posts messages using the SNS API.

[0124] Example 2

[0125] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0126] Conventional information collection systems often only acquire information from specific sources, resulting in a lack of comprehensiveness. Furthermore, the acquired information is often not analyzed, stored, or provided to users efficiently. Furthermore, the summary report of filtered information is not automated, placing a heavy burden on users.

[0127] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0128] In this invention, the server includes a data collection means for acquiring information from specified information sources, a search means for performing a keyword search based on the acquired information, a filtering means for filtering appropriate information based on the search results, a display means for displaying a list of the filtered information, a report means for reporting the displayed information, a means for receiving a list of URLs of the specified information sources, a means for sending an HTTP request to each URL, a means for acquiring HTML content, a means for analyzing the HTML content and extracting necessary information, a means for saving the extracted information in a database, and a means for providing the saved information to a user. This makes it possible to efficiently collect, analyze, and save a wide range of information from multiple information sources and provide it to a user.

[0129] "Data Collection Method" means a method for obtaining information from a designated source.

[0130] The "search means" is a means for performing a keyword search based on the acquired information.

[0131] The "filtering means" is a means for selecting appropriate information based on the search results.

[0132] The "display means" is a means for displaying a list of filtered information.

[0133] The "reporting means" is a means for reporting the displayed information to the user.

[0134] The "means for receiving a URL list" is a means for receiving a URL list of a specified information source.

[0135] "Means for sending HTTP requests" refers to means for sending HTTP requests to each URL.

[0136] The "means for acquiring HTML content" refers to a means for acquiring HTML content returned as a response to an HTTP request.

[0137] The "means for analyzing HTML content" refers to a means for analyzing acquired HTML content and extracting necessary information.

[0138] The "means for storing in a database" refers to a means for storing the extracted information in a database.

[0139] "Means for providing to the user" refers to means for providing the stored information in a form that can be accessed by the user.

[0140] This invention relates to a system for acquiring information from a specified information source, and analyzing, storing, and providing the information. A specific embodiment of this system will be described below.

[0141] The server uses a data collector to retrieve information from the specified sources. The data collector uses Python's requests library to send an HTTP request to each URL and retrieve the HTML content. The retrieved HTML content is then parsed using libraries such as BeautifulSoup or Scrapy to extract the required information.

[0142] The extracted information is stored in a database using an RDBMS such as MySQL (registered trademark) or PostgreSQL. The stored information is provided to users through a web application built using Flask or Django. Users can access and view this information through their browser.

[0143] As a concrete example, suppose a user wants to obtain information from the following news site:

[0144] https: / / example-news1.com

[0145] https: / / example-news2.com

[0146] The server receives these URLs as a list and retrieves the article titles and body text from each site, storing this information in a database and allowing users to access it through a web application.

[0147] By inputting the following prompt sentence into the generative AI model, we can generate a description of a program that retrieves information from a news site.

[0148] Example prompt sentence:

[0149] "Describe a program that retrieves information from multiple news sites. Specifically, explain how to retrieve the HTML content of each site from a given list of URLs, extract the article titles and body text, and store them in a database. Include the names of any specific libraries or databases you use."

[0150] In this way, the server can efficiently collect, analyze, store, and provide a wide range of information from multiple sources to users.

[0151] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0152] Step 1:

[0153] The server receives a list of URLs for news sites specified by the user. The input is the list of URLs provided by the user, and the output is the list of URLs received by the server. This list is often provided in JSON format, CSV format, etc.

[0154] Step 2:

[0155] The server sends an HTTP request to each URL in the received URL list. The input is the URL list, and the output is the HTTP response obtained from each URL. This is done using Python's requests library.

[0156] Step 3:

[0157] The server retrieves the HTML content returned as a response to the HTTP request. The input is the HTTP response, and the output is the retrieved HTML content. The retrieved HTML content is analyzed in the next step.

[0158] Step 4:

[0159] The server uses libraries such as BeautifulSoup or Scrapy to parse the retrieved HTML content and extract the necessary information. The input is the HTML content, and the output is the extracted information (e.g., article title, body, publication date, etc.). Specifically, it parses HTML tags and extracts the necessary data.

[0160] Step 5:

[0161] The server saves the extracted information in a database. The input is the extracted information, and the output is the information saved in the database. The database uses an RDBMS such as MySQL or PostgreSQL. When saving, the data is inserted into a table with fields such as the title, body, and publication date of each article.

[0162] Step 6:

[0163] The server provides the stored information in a form that can be accessed by the user. The input is the information stored in the database and the output is the information that is presented to the user. For example, you can build a web application using Flask or Django to allow the user to view the information through a browser.

[0164] In this way, the server can efficiently collect, analyze, and store a wide range of information from multiple news sites and provide it to users.

[0165] (Application example 2)

[0166] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0167] Conventional news aggregator systems only retrieve information from specific news sites, which can lead to information bias. Furthermore, they lacked a way to efficiently store and later reference the retrieved information, making it difficult for users to quickly obtain the information they need.

[0168] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes scraping means for acquiring information from specified information sources, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate information based on the search results, display means for displaying a list of the filtered information, reporting means for reporting the displayed information, and storage means for saving the acquired information in a data format. This makes it possible to acquire a wide range of information from multiple information sources and efficiently store and reference it.

[0169] "Specified information source" refers to a website, database, or other information source that the user has pre-selected.

[0170] "Scraping method" refers to a technology or program used to automatically obtain data from a specified source.

[0171] "Keyword search" refers to the process of locating relevant information based on specific words or phrases within retrieved information.

[0172] "Search means" refers to the technology or program used to perform keyword searches.

[0173] "Filtering means" refers to technology or programs for selecting appropriate information from search results based on specific conditions.

[0174] "Display means" refers to the technology or program used to visually present filtered information to users.

[0175] "Reporting means" refers to the technology or program used to notify users of displayed information.

[0176] "Storage means" refers to the technology or programs used to store acquired information in data format so that it can be referenced later.

[0177] A system for implementing this invention includes scraping means for acquiring information from a specified information source, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate information based on the search results, display means for displaying a list of the filtered information, reporting means for reporting the displayed information, and storage means for saving the acquired information in a data format.

[0178] Program processing explanation

[0179] The server implements the scraping method using a Python program. Specifically, it uses the requests library to retrieve HTML content from the specified source URL, and then parses the HTML content using the BeautifulSoup library. Article titles and summaries are extracted from the parsed data and stored in a database.

[0180] The search function searches for information in the database based on keywords entered by the user. The search results are then filtered based on appropriate criteria, for example, to extract only the most recent information or information related to a specific category.

[0181] The display means visually presents the filtered information to the user's device, using a web browser or smartphone app. The reporting means notifies the user of the filtered information, and could be email or push notification.

[0182] The storage method saves the acquired information in a data format so that it can be referenced later. This can be done in JSON format or a database.

[0183] Specific examples

[0184] For example, if a user wants to get the latest news in the "Technology" category, they might use a prompt like this:

[0185] Prompt Sentence Examples

[0186] urls = [

[0187] 'https: / / example-tech-news.com'

[0188] ]

[0189] aggregator = NewsAggregator(urls)

[0190] news_data = aggregator.fetch_news()

[0191] aggregator.save_news(news_data)

[0192] By inputting this prompt into a generative AI model, the latest technology news can be retrieved from specified sources and provided to the user.

[0193] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0194] Step 1:

[0195] The server receives the URLs of the information sources specified by the user as input, specifically, it obtains a list of URLs specified by the user, which is then processed by the scraping means.

[0196] Step 2:

[0197] The server uses the requests library to get the HTML content from the specified URL. It takes the URL as input and gets the HTML content as output. This HTML content is parsed in the next step.

[0198] Step 3:

[0199] The server uses the BeautifulSoup library to parse the retrieved HTML content. It receives the HTML content as input and obtains the parsed data (article titles and summaries) as output. Specifically, it parses HTML tags and extracts the article titles and summaries.

[0200] Step 4:

[0201] The server saves the parsed data in a database. It receives the parsed data as input and gets the data saved in the database as output. It uses a storage means to store the data in JSON format or in a database.

[0202] Step 5:

[0203] A user performs a search by entering keywords. The search tool receives keywords as input and obtains search results as output. The search tool is used to search for information in a database based on keywords.

[0204] Step 6:

[0205] The server filters the search results. It receives the search results as input and obtains the filtered information as output. The filtering means is used to select information based on specific criteria (e.g., the most recent information or a specific category).

[0206] Step 7:

[0207] The server displays the filtered information on the user's device. It receives the filtered information as input and obtains visually displayed information as output. It displays the information on a web browser or smartphone app using a display means.

[0208] Step 8:

[0209] The server reports the filtered information to the user. It receives the filtered information as input and obtains the notified information as output. It notifies the user of the information by email, push notification, etc. using a reporting means.

[0210] Step 9:

[0211] The server stores the retrieved information in a data format. It receives the retrieved information as input and gets the stored data as output. It uses a storage method to store the information in a database or in JSON format for future reference.

[0212] Example 3

[0213] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0214] In today's information society, a huge amount of information is generated every day. It is extremely important to efficiently retrieve, summarize, and report the necessary information from that information. However, doing this manually takes time and effort, and is inefficient. In addition, existing systems do not integrate the processes of information collection, filtering, summarization, and reporting, which makes them difficult to use for users.

[0215] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0216] In this invention, the server includes a collection means for acquiring information from a specified information source, a search means for performing a keyword search based on the acquired information, a filtering means for filtering appropriate information based on the search results, a summarization means for summarizing the filtered information, and a reporting means for reporting the summarized information. This makes it possible to efficiently acquire necessary information from a vast amount of information and summarize and report it.

[0217] "Collection means" means the means for obtaining information from a designated source.

[0218] The "search means" is a means for performing a keyword search based on the acquired information.

[0219] The "filtering means" is a means for selecting appropriate information based on the search results.

[0220] A "summarization means" is a means for concisely summarizing filtered information.

[0221] A "reporting means" is a means for notifying a user of summarized information.

[0222] This invention is a system that acquires information from a server-specified information source, searches the acquired information by keyword, filters appropriate information, summarizes the filtered information, and reports the summarized information to a user. A specific embodiment of this system will be described below.

[0223] Hardware and software used

[0224] Hardware: Servers, terminals

[0225] Software: Natural language processing libraries (e.g., spaCy, NLTK), generative AI models (e.g., GPT-4 (registered trademark))

[0226] Program processing

[0227] Retrieving articles

[0228] The server collects articles from the Internet. Specifically, it obtains the latest articles using the RSS feeds and APIs of news sites. For example, the server accesses "https: / / example.com / rss" and downloads the latest RSS feed. It then analyzes the obtained RSS feed and extracts the article title, URL, content, etc.

[0229] Article filtering

[0230] The server filters the collected articles based on specific keywords or categories. For example, it selects only articles in the "technology" category. The server analyzes the metadata (title, category, tags, etc.) of the articles it retrieves and keeps only articles that fall into the "technology" category in the list.

[0231] Article Summary

[0232] The server uses a natural language processing library (e.g., spaCy) to analyze the content of the filtered articles and generate summaries. Specifically, it extracts the main points of the articles and summarizes them into short sentences. The server inputs the filtered article text into spaCy, which extracts important sentences and key phrases. It then generates a summary based on the extracted information.

[0233] Summary Report

[0234] The server reports the generated summary to the user. Possible reporting methods include email, notification, dashboard, etc. The server sends the generated summary to the user's email address. For example, it sends an email using the SMTP protocol.

[0235] Specific examples

[0236] Retrieving articles

[0237] The server retrieves the latest news articles from "https: / / example.com / rss".

[0238] Article filtering

[0239] The server filters only articles in the "technology" category.

[0240] Article Summary

[0241] The server summarizes the article as follows:

[0242] Original article: "A new AI technology has been announced that promises to significantly speed up data analysis."

[0243] Summary: "New AI techniques speed up data analysis."

[0244] Summary Report

[0245] The server sends the summary to the user's email address.

[0246] Example prompts for generative AI models

[0247] Prompt statement

[0248] "Please summarize the following article: 'A new AI technology has been announced that promises to significantly improve the speed of data analysis.'"

[0249] In this way, a system is implemented in which the server performs a series of processes from collecting articles to summarizing and reporting. By receiving a concise summary, users can obtain information efficiently. The flow of the specific process in the third embodiment will be described with reference to FIG. 15.

[0250] Step 1: Get the article

[0251] The server collects articles from specified sources. As input, it receives the news site's RSS feed or API URL. Specifically, the server accesses "https: / / example.com / rss" and downloads the latest RSS feed. It then analyzes the obtained RSS feed and extracts the article title, URL, content, etc. As output, it generates a list of the extracted article information.

[0252] Step 2: Filtering articles

[0253] The server filters the collected articles based on specific keywords or categories. As input, it receives the list of article information obtained in step 1 and the filtering criteria (e.g., category "Technology"). Specifically, the server analyzes the article metadata (title, category, tags, etc.) and keeps in the list only those articles that match the filtering criteria. As output, it generates a list of filtered article information.

[0254] Step 3: Summarize the article

[0255] The server summarizes the content of the filtered articles. As input, it receives the list of article information filtered in step 2. Specifically, the server analyzes the text of the articles using a natural language processing library (e.g., spaCy) and extracts important sentences and key phrases. It then generates a summary based on the extracted information. As output, it generates a list of summarized article information.

[0256] Step 4: Report the summary

[0257] The server reports the generated summary to the user. As input, it receives the list of article information summarized in step 3 and the user's contact information (e.g., email address). As a specific operation, the server sends the summary to the user's email address using the SMTP protocol. As an output, it provides a confirmation of the summary sent to the user.

[0258] (Application example 3)

[0259] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0260] In modern society, a huge amount of information is generated every day, and users are required to quickly and efficiently obtain the information they need. However, conventional information acquisition systems have had the problem of being unable to properly filter and summarize information that interests users. In particular, there is a lack of means to quickly grasp long information such as news articles, which reduces the efficiency of users' information acquisition.

[0261] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0262] In this invention, the server includes an acquisition means for acquiring information from a specified information source, a search means for performing a keyword search based on the acquired information, a filtering means for filtering appropriate information based on the search results, a summarization means for summarizing and reporting the content of the filtered information, and a provision means for providing the summarized information to the user. This enables the user to quickly and efficiently acquire the information they need from a vast amount of information and grasp important content in a short amount of time.

[0263] "Acquisition means" refers to a means for acquiring information from a specified information source.

[0264] The "search means" is a means for performing a keyword search based on the acquired information.

[0265] The "filtering means" is a means for filtering appropriate information based on the search results.

[0266] The "display means" is a means for displaying a list of filtered information.

[0267] "Reporting means" means for reporting displayed information.

[0268] The "summarization means" is a means for summarizing and reporting the contents of the filtered information.

[0269] "Means of provision" refers to the means for providing summarized information to users.

[0270] A "generative AI model" is a model for generating information using artificial intelligence technology.

[0271] A "prompt sentence" is an instruction sentence input to a generative AI model.

[0272] As an embodiment of the present invention, a system is constructed that efficiently acquires news articles, summarizes them, and provides them to users. A specific embodiment of this system is described below.

[0273] System configuration

[0274] This system is composed of a server and a user terminal. The server includes an acquisition means for acquiring information from a specified information source, a search means for performing a keyword search based on the acquired information, a filtering means for filtering appropriate information based on the search results, a summarization means for summarizing and reporting the content of the filtered information, and a provision means for providing the summarized information to the user.

[0275] Hardware and software used

[0276] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[0277] Software: Python, Requests library, Hugging Face Transformers library

[0278] Data processing and calculation

[0279] 1. Acquisition method: The server acquires the latest news articles from the specified source using a news API. For example, it acquires article data in JSON format from the news API.

[0280] 2. Search method: The retrieved article data is searched using keywords specified by the user. The search results are narrowed down to articles with the highest relevance.

[0281] 3. Filtering methods: Filtering the search results to find more relevant articles, for example, based on criteria such as the article's credibility or publication date.

[0282] 4. Summarization: To summarize the content of the filtered articles, we use a generative AI model. Specifically, we use Hugging Face's Transformers library to invoke a pre-trained summarization model and generate a summary of the article.

[0283] 5. Delivery method: The summarized article is delivered to the user's device. Users can view the summarized article through their device, such as a smartphone or tablet.

[0284] Specific examples

[0285] For example, if a user searches for a news article using the keyword "economic growth," the server retrieves relevant articles from the news API and narrows down the articles related to "economic growth" using the search means. Next, it selects highly reliable articles using the filtering means and summarizes the content of the articles using the summarization means. Finally, the summarized articles are provided to the user's device.

[0286] Prompt Sentence Examples

[0287] "Please summarize the contents of the news article. Here is the article: 'Japan's economic growth rate exceeded expectations. The government has announced new economic policies and is hopeful about future growth.'"

[0288] In this way, users can efficiently obtain important information in a short time.

[0289] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0290] Step 1:

[0291] The server uses the news API to retrieve the latest news articles from the specified source. It uses the API URL and authentication key as input and gets the news article data in JSON format as output. Specifically, it sends an HTTP request using the Requests library and receives the article data as a response.

[0292] Step 2:

[0293] The server searches the retrieved article data using keywords specified by the user. The server uses the keywords specified by the user and the retrieved article data as input, and obtains a list of articles related to the keywords as output. Specifically, it analyzes the article data and extracts articles that contain the keywords.

[0294] Step 3:

[0295] The server then filters the search results to find more relevant articles. It uses the list of search results as input and obtains a filtered list of articles as output. Specifically, it filters based on criteria such as the article's credibility and publication date.

[0296] Step 4:

[0297] The server uses a generative AI model to summarize the content of the filtered articles. It uses the filtered article text as input and obtains summarized text as output. Specifically, it invokes a pre-trained summarization model using Hugging Face's Transformers library to generate article summaries.

[0298] Step 5:

[0299] The server provides the summarized article to the user terminal. It uses the summarized text as input and obtains the summarized article displayed on the user terminal as output. Specifically, it sends the summarized text to the user terminal for viewing by the user.

[0300] Step 6:

[0301] The user browses the summarized article through their device. The summarized article provided by the server is used as input, and the display of the summarized article is obtained as output. Specifically, the summarized article is displayed on the screen of the user's device, allowing the user to grasp important information in a short time.

[0302] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0303] "Example 1"

[0304] An example of a system using an emotion engine is one that provides an interface for recognizing a user's emotion. Specifically, an interface is provided that allows the user to input their own emotion into the system. This interface can take various forms, such as a text input field or a selection of icons that represent emotions. When the user inputs their emotion, the emotion engine analyzes the information and recognizes the user's emotion.

[0305] "Example 2"

[0306] One example of a system using an emotion engine is one that adjusts the content of reports based on the user's emotions. Specifically, if the user is expressing positive emotions such as joy or excitement, the system will prioritize reporting positive news articles to match those emotions. Conversely, if the user is expressing negative emotions such as sadness or anger, the system will report less negative news articles to match those emotions.

[0307] "Example 3"

[0308] As an example of a system using an emotion engine, the display order of filtered articles is adjusted based on the user's emotions. Specifically, if the user expresses positive emotions such as joy or excitement, the system will display positive news articles at the top of the list in line with that emotion. Conversely, if the user expresses negative emotions such as sadness or anger, the system will display negative news articles at the bottom in line with that emotion.

[0309] The processing flow of each embodiment will be described below.

[0310] "Example 1"

[0311] Step 1: The user accesses the system.

[0312] Step 2: The system provides the user with an emotion input interface, which can take various forms, such as a text input field or a selection of icons that represent emotions.

[0313] Step 3: The user inputs their emotions.

[0314] Step 4: The emotion engine analyzes the input from the user and recognizes the user's emotion.

[0315] "Example 2"

[0316] Step 1: The user accesses the system and inputs their emotions.

[0317] Step 2: The emotion engine analyzes the input from the user and recognizes the user's emotion.

[0318] Step 3: The system adjusts the reporting content based on the user's emotions. Specifically, if the user expresses positive emotions such as joy or excitement, the system will prioritize reporting positive news articles to match that emotion. Conversely, if the user expresses negative emotions such as sadness or anger, the system will moderate reporting negative news articles to match that emotion.

[0319] "Example 3"

[0320] Step 1: The user accesses the system and inputs their emotions.

[0321] Step 2: The emotion engine analyzes the input from the user and recognizes the user's emotion.

[0322] Step 3: The system adjusts the display order of the filtered articles based on the user's emotions. Specifically, if the user expresses positive emotions such as joy or excitement, the system will display positive news articles at the top to match that emotion. Conversely, if the user expresses negative emotions such as sadness or anger, the system will display negative news articles at the bottom to match that emotion.

[0323] Example 1

[0324] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0325] Conventional information collection systems only retrieve data from specific sources and perform keyword searches and filtering, which limits the comprehensiveness and accuracy of the information. Furthermore, there was a lack of efficient ways to report filtered information, making it difficult for users to quickly obtain the information they needed. This resulted in the time and effort required to retrieve and report information.

[0326] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0327] In this invention, the server includes a data acquisition means for acquiring data from a specified information source, a search means for performing a keyword search based on the acquired data, and a filtering means for filtering appropriate information based on the search results. By acquiring data from multiple information sources and performing a keyword search and filtering, the comprehensiveness and accuracy of information can be improved, and users can quickly obtain the information they need.

[0328] "Data acquisition means" refers to a means for acquiring data from a specified information source.

[0329] The "search means" is a means for performing a keyword search based on the acquired data.

[0330] The "filtering means" is a means for filtering appropriate information based on the search results.

[0331] The "display means" is a means for displaying a list of filtered information.

[0332] A "reporting means" is a means for reporting displayed information.

[0333] The present invention relates to a system that acquires data from a specified information source, performs keyword search and filtering, and displays and reports the filtered information in a list. Specific embodiments of this system will be described below.

[0334] The server retrieves data from the specified source using Python libraries such as BeautifulSoup or Scrapy. Specifically, the server parses the HTML structure of the source and extracts information such as the article title, body, and date. For example, the server accesses the homepage of "https: / / example-news.com" and retrieves the list of the latest articles.

[0335] Next, the server performs a keyword search based on the acquired data. If the user specifies the keyword "AI technology," the server searches for articles related to "AI technology" from among the acquired articles. Specifically, the server checks whether the keyword "AI technology" is included in the article title or text.

[0336] The server then filters out appropriate information based on the search results. It uses natural language processing (NLP) technology to analyze the content of the article and rate its importance. For example, if the content of an article is about the latest trends in AI technology, the server will rate the article highly important. Specifically, the server analyzes the frequency of keyword occurrences and context in the article to calculate a score.

[0337] The filtered information is displayed in a list on the device. The user can check the titles and summaries of the filtered articles on the device screen. Specifically, the device displays the titles, summaries, and links of the filtered articles in a list format.

[0338] Finally, the server reports the displayed information. This is done via email, an internal social networking site, or other means. For example, if an important article is found, the server sends an email to the user containing a link to the article and a summary. Specifically, the server notifies the user by sending an email using the SMTP protocol.

[0339] As a concrete example, if a user wants to know the latest news about "AI technology," they can input the following prompt sentence into the generative AI model:

[0340] Example prompt sentence:

[0341] "Get the latest news on AI technology, filter and report on the stories that matter."

[0342] After entering this prompt, the system will retrieve data from the specified source, perform keyword search and filtering, and then list and report the filtered information, allowing users to efficiently obtain the latest important news on "AI technology."

[0343] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0344] Step 1:

[0345] The server retrieves data from the specified information source. Specifically, the server uses Python libraries such as BeautifulSoup and Scrapy to parse the HTML structure of the information source and extract information such as the article title, body, and date. The URL of the information source is given as input, and the extracted article data is obtained as output.

[0346] Step 2:

[0347] The server performs a keyword search based on the acquired data. If the user specifies the keyword "AI technology," the server will search for articles related to "AI technology" from among the acquired articles. The input is the keyword specified by the user and the acquired article data, and the output is a list of articles that match the keywords. Specifically, the server checks whether the keyword "AI technology" is included in the article title or body of the article.

[0348] Step 3:

[0349] The server filters appropriate information based on the search results. It uses natural language processing (NLP) technology to analyze the content of articles and evaluate their importance. Keyword search results are given as input, and a list of articles with their importance evaluated is obtained as output. Specifically, the server analyzes the keyword frequency and context of the articles and calculates a score.

[0350] Step 4:

[0351] The terminal displays the filtered information in a list. The user can check the titles and summaries of the filtered articles on the terminal screen. The input is a list of filtered articles, and the output is the display of the articles in a format that the user can view. Specifically, the terminal displays the titles, summaries, and links of the filtered articles in a list format.

[0352] Step 5:

[0353] The server reports the displayed information. The report is sent via email, an internal SNS, etc. The input is a list of filtered articles, and the output is confirmation that the report has been sent. Specifically, the server notifies the user by sending an email using the SMTP protocol. For example, if an important article is found, the server sends the user an email containing a link to the article and a summary.

[0354] (Application example 1)

[0355] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0356] In today's information society, it is difficult for users to efficiently obtain information that is important to them from a vast amount of information. In addition, there is a problem that information is not provided in accordance with the user's emotions, making it difficult to increase user satisfaction.

[0357] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0358] In this invention, the server includes scraping means for acquiring information from a specified information source, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate information based on the search results, display means for displaying a list of the filtered information, report means for reporting the displayed information, emotion recognition means for recognizing the user's emotion, and priority display means for displaying information preferentially based on the emotion recognition means. This allows the user to efficiently acquire information that is important to them and further enables the provision of information according to their emotions.

[0359] A "scraping method" is a technique for automatically obtaining information from a specified source.

[0360] "Search means" refers to a technique for searching for information using specific keywords based on the acquired information.

[0361] "Filtering means" is a technique for selecting appropriate information based on search results.

[0362] The "display means" is a technique for visually presenting the filtered information to the user.

[0363] "Reporting means" refers to a technique for notifying the user of the displayed information.

[0364] "Emotion recognition means" is a technology that analyzes and recognizes the user's emotions.

[0365] The "priority display means" is a technology for displaying information preferentially based on emotion recognition means.

[0366] The system for carrying out the present invention has a function of acquiring information from a specified information source and displaying the information preferentially based on the user's emotions. A specific embodiment of this system will be described below.

[0367] Hardware and software used

[0368] Hardware: Smartphones, servers

[0369] Software: Python, BeautifulSoup, nltk, smtplib

[0370] System configuration

[0371] 1. Scraping Method

[0372] The server uses Python's BeautifulSoup library to automatically retrieve information from specified sources (e.g., news sites), which allows it to parse HTML structures and extract the required information.

[0373] 2. Search methods

[0374] Based on the information retrieved, the server searches for information using specific keywords, using the standard Python library to perform text searches.

[0375] 3. Filtering Methods

[0376] To select relevant information based on the search results, the server uses the SentimentIntensityAnalyzer from the nltk library, which analyzes the content of the information and evaluates its importance.

[0377] 4. Display means

[0378] The terminal (smartphone) visually presents the filtered information to the user using the smartphone's display.

[0379] 5. Reporting methods

[0380] The server uses the smtplib library to send emails to notify the user of the displayed information, allowing for a summary report of the filtered information.

[0381] 6. Emotion recognition means

[0382] The terminal provides a text input interface to analyze and recognize the user's emotions. When the user inputs their emotions, the server analyzes the information and recognizes the emotions.

[0383] 7. Priority display method

[0384] The server prioritizes displaying information based on the emotion recognition means, thereby enabling provision of information according to the user's emotion.

[0385] Specific examples

[0386] For example, if a user wants to get news about "technology," they might use a prompt like this:

[0387] Prompt Sentence Examples

[0388] Enter your news site URL: https: / / example.com / news

[0389] Enter your keyword: Technology

[0390] Enter the email address to report to: user@example.com

[0391] Please enter your current feelings (e.g. happy, sad, angry): Happy

[0392] Based on this prompt, the server retrieves information from the specified news site and searches for articles related to "technology." The server then filters the search results to display appropriate articles based on the user's sentiment. The filtered articles are then reported in a summary to the user's email address.

[0393] In this way, the user can efficiently obtain information that is important to him / her and can also receive information that corresponds to his / her emotions.

[0394] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0395] Step 1:

[0396] The server receives the URL of the specified news site as input, then uses Python's BeautifulSoup library to parse the HTML structure of the news site and extract the article information, which is then saved in text format.

[0397] Step 2:

[0398] The server receives the keywords entered by the user. It then performs a keyword search on the saved article information. It uses the standard Python library to search for articles containing the keywords in the article information and outputs a list of matching articles.

[0399] Step 3:

[0400] The server receives the list of articles obtained as search results as input. Then, it analyzes the content of each article and evaluates its importance using the SentimentIntensityAnalyzer in the nltk library. It filters out articles with high importance and outputs the filtered list of articles.

[0401] Step 4:

[0402] The device receives the filtered article list as input, then visually displays the filtered articles to the user using the smartphone's display, and outputs the displayed articles in a format that can be viewed by the user.

[0403] Step 5:

[0404] The server receives the filtered article list as input, then uses the smtplib library to summarize the filtered article content and send it to the user's email address, and the summarized article content is output in email format.

[0405] Step 6:

[0406] The terminal receives emotion information input by the user, then transmits the emotion information to the server, and outputs the emotion information in text format.

[0407] Step 7:

[0408] The server receives emotion information sent by the user as input, then analyzes the emotion information using emotion recognition means to recognize the user's emotion, and stores the recognized emotion information as internal data.

[0409] Step 8:

[0410] The server receives the recognized emotion information and the filtered article list as input. Then, the server re-evaluates the article list to prioritize articles according to the user's emotion based on the emotion recognition means. The re-evaluated article list is sorted based on the priority and transmitted to the terminal.

[0411] Example 2

[0412] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0413] Conventional news information acquisition systems have the problem of limiting the range of information they can obtain because they only obtain information from specific news sites. Furthermore, they do not adjust the content of reports based on the user's emotions, making it difficult to provide information that meets the user's needs. Furthermore, the summarization and filtering of the acquired information is insufficient, making it difficult for users to quickly obtain the information they need.

[0414] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0415] In this invention, the server includes scraping means for acquiring information from specified information sources, storage means for saving the acquired information, search means for performing a keyword search based on the saved information, filtering means for filtering appropriate articles based on the search results, display means for displaying a list of the filtered articles, reporting means for reporting the displayed articles, emotion analysis means for analyzing the user's emotions, and adjustment means for adjusting the report content based on the emotion analysis results. This makes it possible to acquire a wide range of information from multiple information sources and provide appropriate information according to the user's emotions.

[0416] A "scraping method" is a technique for automatically obtaining information from a specified source.

[0417] "Storage means" refers to technology for storing acquired information in a storage device such as a database.

[0418] "Search means" refers to technology for conducting keyword searches based on stored information.

[0419] "Filtering means" refers to a technique for selecting appropriate articles based on search results.

[0420] "Display means" refers to a technique for displaying a list of filtered articles.

[0421] The "reporting means" is a technique for reporting the displayed article to the user.

[0422] "Emotion analysis means" is a technique for analyzing the user's emotions.

[0423] "Adjustment means" refers to a technique for adjusting the report content based on the emotion analysis results.

[0424] MODE FOR CARRYING OUT THE INVENTION

[0425] The present invention is a system for acquiring information from a specified information source and adjusting the report content according to the user's emotions. Specific embodiments of this system will be described below.

[0426] Hardware and software used

[0427] The server uses the following hardware and software:

[0428] Hardware: A server machine with a powerful processor, sufficient memory, and ample storage.

[0429] Software: Python programming language, BeautifulSoup library, Scrapy library, MySQL database, IBM Watson® sentiment analysis API

[0430] A terminal is a device through which a user inputs information and receives results. Examples include computers, smartphones, and tablets.

[0431] System processing flow

[0432] 1. Specify the URL of the news site

[0433] The user inputs the URLs of multiple news sites into the system.

[0434] Example: Specify the URLs "https: / / example-news1.com" and "https: / / example-news2.com".

[0435] 2. Scrape information from news sites

[0436] The server accesses the specified URL and parses the HTML content using Python's BeautifulSoup library.

[0437] The server extracts information such as the title, text, and publication date of the news article.

[0438] 3. Save the retrieved information in a database

[0439] The server stores the extracted news article information in a MySQL database.

[0440] The server connects to the database and stores the data using an INSERT statement.

[0441] 4. Input user emotions

[0442] Users input their emotions into the system using text or voice.

[0443] Example: Text "I'm so happy today."

[0444] 5. Analyze emotions

[0445] The server analyzes the input emotions using IBM Watson's emotion analysis API.

[0446] The server obtains the analysis result as either positive, negative, or neutral emotion.

[0447] 6. Select news articles based on sentiment

[0448] Based on the sentiment analysis results, the server preferentially selects positive news articles if the sentiment is positive.

[0449] The server will conservatively select negative news articles in the case of negative sentiment.

[0450] 7. Report selected news stories

[0451] The server reports the selected news articles to the user.

[0452] The user browses through the news articles provided by the system.

[0453] Specific examples

[0454] The user specifies the URLs "https: / / example-news1.com" and "https: / / example-news2.com".

[0455] The server uses BeautifulSoup to retrieve the latest news articles from these sites and store them in a MySQL database.

[0456] The user texts in "I'm so happy today."

[0457] The server uses IBM Watson to analyze the emotion "happy" as positive.

[0458] The server prioritizes positive news articles and reports them to the user.

[0459] Prompt Sentence Examples

[0460] Get the latest news articles from "https: / / example-news1.com" and "https: / / example-news2.com".

[0461] If someone types, "I'm so happy today," please prioritize reporting positive news stories.

[0462] The above is a specific embodiment for carrying out the present invention.

[0463] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0464] Step 1:

[0465] Specify the URL of the news site

[0466] The user inputs the URLs of multiple news sites into the system.

[0467] Input: URL of news site (e.g. "https: / / example-news1.com", "https: / / example-news2.com")

[0468] Output: List of specified URLs

[0469] What happens: A user enters a URL into a web form or app input field and presses the submit button.

[0470] Step 2:

[0471] Scraping information from news sites

[0472] The server accesses the specified URL and parses the HTML content using Python's BeautifulSoup library.

[0473] Input: A list of specified URLs

[0474] Output: News article title, text, publication date, and other information

[0475] Specific behavior: Use BeautifulSoup to extract specific tags (e.g. <h1>、 ) and obtain the required information.

[0476] Step 3:

[0477] Save the retrieved information in the database

[0478] The server stores the extracted news article information in a MySQL database.

[0479] Input: Information such as news article title, text, publication date, etc.

[0480] Output: News article records stored in a database

[0481] Specific operation: Connect to the database using MySQL's Python library (e.g. PyMySQL) and insert data using the INSERT statement.

[0482] Step 4:

[0483] Input user emotions

[0484] Users input their emotions into the system using text or voice.

[0485] Input: User's emotion (e.g., "I'm so happy today")

[0486] Output: Text data of the input emotion

[0487] Specific action: A user enters an emotion into an input field on a web form or in an app and presses the submit button.

[0488] Step 5:

[0489] Analyzing emotions

[0490] The server analyzes the input emotions using IBM Watson's emotion analysis API.

[0491] Input: Text data of user emotions

[0492] Output: Sentiment analysis results (positive, negative, neutral)

[0493] Specific operation: Sends text to IBM Watson's API and receives sentiment analysis results.

[0494] Step 6:

[0495] Selecting news articles based on sentiment

[0496] Based on the sentiment analysis results, the server preferentially selects positive news articles if the sentiment is positive.

[0497] Input: Sentiment analysis results, news article records stored in a database

[0498] Output: A list of selected news articles

[0499] What it does: Filter news articles from a database according to their sentiment.

[0500] Step 7:

[0501] Report selected news stories

[0502] The server reports the selected news articles to the user.

[0503] Input: A list of selected news articles

[0504] Output: News article reported to user

[0505] Specific behavior: Returns the selected news article to the user in JSON format and displays it on a web page or app.

[0506] The above are the specific processing steps of the program of this system.

[0507] (Application example 2)

[0508] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0509] Conventional news delivery systems do not customize news articles according to the user's emotions, and are unable to provide information appropriate to the user's emotional state. As a result, negative news may be displayed even when the user is expressing positive emotions, and conversely, positive news may be displayed even when the user is expressing negative emotions. This leads to a problem of reduced user satisfaction.

[0510] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0511] In this invention, the server includes scraping means for acquiring information from a specified information source, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate information based on the search results, display means for displaying a list of the filtered information, reporting means for reporting the displayed information, emotion analysis means for analyzing the user's emotions, and adjustment means for adjusting the report content based on the user's emotions, thereby enabling customization of news articles according to the user's emotional state.

[0512] A "scraping method" is a technique for automatically obtaining information from a specified source.

[0513] "Search means" refers to a technique for conducting a keyword search based on the acquired information.

[0514] "Filtering means" refers to a technique for selecting appropriate information based on search results.

[0515] The "display means" is a technique for displaying the filtered information in a list.

[0516] "Reporting means" refers to a technique for reporting displayed information to the user.

[0517] "Emotion analysis means" is a technique for analyzing the user's emotions.

[0518] The "adjustment means" is a technique for adjusting the report content based on the user's feelings.

[0519] A system for carrying out this invention comprises a server and a user terminal. The server includes scraping means for acquiring information from a specified information source, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate information based on the search results, display means for displaying a list of the filtered information, reporting means for reporting the displayed information, emotion analysis means for analyzing the user's emotion, and adjustment means for adjusting the report content based on the user's emotion.

[0520] Program processing explanation

[0521] Scraping Method

[0522] The server uses software libraries called requests and BeautifulSoup to retrieve information from multiple specified news sites, allowing it to aggregate the latest news articles from a wide range of sources.

[0523] Search methods

[0524] The retrieved news articles are then subjected to keyword search using natural language processing libraries such as TextBlob, allowing articles related to specific topics of interest to the user to be extracted.

[0525] Filtering Methods

[0526] Based on the search results, appropriate news articles are filtered based on criteria such as article content, credibility, and relevance.

[0527] Display means

[0528] The filtered news articles are displayed in a list on the user's device in a format that is intuitively easy for the user to understand.

[0529] Reporting method

[0530] The displayed news article is reported to the user, including a summary of the article and key points.

[0531] Emotion analysis means

[0532] To analyze the user's sentiment, we use sentiment analysis libraries such as TextBlob and NLTK, which determine whether the user is expressing positive or negative sentiment.

[0533] Adjustment means

[0534] The system adjusts the content of reports based on the user's emotions. For example, if the user is expressing positive emotions, it prioritizes positive news articles, and if the user is expressing negative emotions, it minimizes negative news articles.

[0535] Specific examples

[0536] For example, if a user expresses positive emotions, the server will prioritize positive news articles such as "new technology advancements" and "successful business stories." Conversely, if a user expresses negative emotions, the server will de-emphasize negative news articles such as "disasters" and "crimes."

[0537] Prompt Sentence Examples

[0538] If users are expressing positive sentiment, prioritize positive news articles, such as "new technology advancements" or "successful business stories."

[0539] In this way, news articles can be customized according to the user's emotional state, improving user satisfaction.

[0540] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0541] Step 1:

[0542] The server retrieves information from multiple specified news sites. Specifically, it uses the requests library to send HTTP requests to each news site and parses the HTML content using the BeautifulSoup library. The input is a list of news site URLs, and the output is a list of news articles retrieved from each news site.

[0543] Step 2:

[0544] The server performs a keyword search on the retrieved news articles. Specifically, it uses the TextBlob library to parse the text of each article and check whether it contains the specified keywords. The input is a list of news articles and the keywords, and the output is a list of news articles that match the keywords.

[0545] Step 3:

[0546] The server filters relevant news articles based on the search results, specifically based on criteria such as article content, credibility, and relevance. The input is a list of news articles that match the keywords, and the output is a filtered list of news articles.

[0547] Step 4:

[0548] The server displays a list of filtered news articles on the user's device. Specifically, it generates HTML and CSS to display the news articles in a format that is intuitively easy for the user to understand. The input is the list of filtered news articles, and the output is the HTML content displayed on the user's device.

[0549] Step 5:

[0550] The server reports the displayed news articles to the user, including summaries and key points of the articles. The input is a filtered list of news articles, and the output is the summaries of the news articles reported to the user.

[0551] Step 6:

[0552] The server analyzes the user's emotions. Specifically, it uses an emotion analysis library such as TextBlob or NLTK to determine the user's emotional state. The input is data indicating the user's emotions (e.g., user feedback or comments), and the output is the user's emotional state (positive or negative).

[0553] Step 7:

[0554] The server adjusts the reporting content based on the user's emotions: if the user is expressing positive emotions, it prioritizes positive news articles, and if the user is expressing negative emotions, it minimizes negative news articles. The input is the user's emotional state and a filtered list of news articles, and the output is a tailored list of news articles.

[0555] Example 3

[0556] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0557] Conventional news article display systems lack the functionality to adjust the display order of articles based on the user's emotions, which means they are unable to provide information that is appropriate to the user's emotions. Furthermore, they lack the functionality to summarize and report the contents of articles, making it difficult for users to obtain information efficiently.

[0558] The identification process by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a collection means for acquiring information from a specified information source, a search means for performing a keyword search based on the acquired information, a filtering means for filtering appropriate information based on the search results, a summarization means for summarizing the filtered information, an emotion collection means for collecting user emotion data, an order adjustment means for adjusting the display order of information based on the collected emotion data, a display means for displaying information based on the adjusted order, and a report means for reporting the displayed information. This makes it possible to provide information according to the user's emotions, allowing information to be obtained efficiently.

[0559] "Collection means" means the means for obtaining information from a designated source.

[0560] The "search means" is a means for performing a keyword search based on the acquired information.

[0561] The "filtering means" is a means for filtering appropriate information based on the search results.

[0562] The "summarization means" is a means for summarizing the filtered information.

[0563] The "emotion collection means" is a means for collecting user emotion data.

[0564] The "order adjustment means" is a means for adjusting the display order of information based on the collected emotion data.

[0565] A "display means" is a means for displaying information based on the adjusted order.

[0566] A "reporting means" is a means for reporting displayed information.

[0567] MODE FOR CARRYING OUT THE INVENTION

[0568] The present invention is a system for acquiring information from a specified information source and adjusting the display order of the information based on the user's emotions. Specific embodiments of this system will be described below.

[0569] 1. Generating the system program

[0570] The server generates a program including a collection means for acquiring information from a specified information source, a search means for performing a keyword search based on the acquired information, a filtering means for filtering appropriate information based on the search results, a summarization means for summarizing the filtered information, an emotion collection means for collecting user emotion data, an order adjustment means for adjusting the display order of the information based on the collected emotion data, a display means for displaying the information based on the adjusted order, and a report means for reporting the displayed information.

[0571] 2. Explanation of program processing

[0572] The server first collects information from specified sources, typically using web scraping libraries such as Python's BeautifulSoup or Scrapy, and stores the collected information in a database.

[0573] The server then performs a keyword search based on the collected information. As a search tool, a search engine such as "ElasticSearch (registered trademark)" can be used.

[0574] Based on the search results, the server filters out the relevant information, possibly using machine learning algorithms.

[0575] The filtered information is summarized by the server using natural language processing libraries such as NLTK and spaCy.

[0576] The device collects emotional data while the user is browsing information. Specifically, emotions are detected through user input and facial recognition cameras, and the emotional data is analyzed using emotion engines such as IBM Watson Tone Analyzer and Microsoft Azure® Text Analytics.

[0577] The server adjusts the display order of information based on the emotion data sent from the device. If a positive emotion is detected, positive information is displayed at the top, and if a negative emotion is detected, negative information is displayed at the bottom. MySQL or PostgreSQL is used as the database management system (DBMS).

[0578] The terminal displays the information to the user in the display order sent from the server, allowing the user to view the information in an order that corresponds to their emotions.

[0579] 3. Examples of concrete examples and prompts

[0580] For example, if a user is using a news app, if the user feels happy, the system will display positive news such as "new technological developments" or "sports victories" at the top. On the other hand, if the user feels sad, negative news such as "disaster news" or "economic downturn" at the bottom.

[0581] Examples of prompts to be input to a generative AI model include:

[0582] Example prompt sentence:

[0583] Please implement a system that displays positive news articles higher when the user expresses positive sentiment, and negative news articles lower when the user expresses negative sentiment. Please also clearly state the specific names of the natural language processing technology and sentiment engine you use.

[0584] In this way, a system for adjusting the display order of information based on the user's feelings is realized. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0585] Program processing flow

[0586] Step 1: Gather information

[0587] The server collects information from specified sources using web scraping libraries such as Python's BeautifulSoup or Scrapy. The input is the URL of the specified source, and the output is the collected article data.

[0588] Specific behavior:

[0589] The server checks the RSS feed of the news site at a fixed time each day to see if there are any new articles. If a new article is found, it retrieves the article's URL and scrapes the article's content.

[0590] Step 2: Keyword search

[0591] The server performs keyword searches based on the collected information. A search engine such as Elasticsearch is used as the search method. The collected article data is given as input, and a list of articles that match the keywords is obtained as output.

[0592] Specific behavior:

[0593] The server indexes the collected article data in Elasticsearch and performs searches based on keywords specified by the user. The search results generate a list of articles that match the keywords.

[0594] Step 3: Filtering the information

[0595] The server filters out relevant information based on the search results. The filtering method uses a machine learning algorithm. The input is a list of articles from the search results, and the output is a filtered list of articles.

[0596] Specific behavior:

[0597] The server uses machine learning models to select reliable and relevant articles from the search results, generating a filtered list of articles.

[0598] Step 4: Summarize the information

[0599] The server summarizes the filtered information using natural language processing libraries such as NLTK and spaCy. The input is the filtered article list, and the output is the summarized article list.

[0600] Specific behavior:

[0601] The server analyzes the content of the filtered articles, extracts important parts, and generates summaries. The summarized article list is stored in a database.

[0602] Step 5: Collecting sentiment data

[0603] The device collects emotional data while the user is browsing information. Specifically, it detects emotions through user input and facial recognition cameras, and uses emotion engines such as IBM Watson Tone Analyzer and Microsoft Azure Text Analytics. The user's emotional data is given as input, and analyzed emotional data is obtained as output.

[0604] Specific behavior:

[0605] The device captures the user's facial expressions with a webcam, sends them to the emotion engine for analysis, and generates the user's emotion data as a result of the analysis and sends it to the server.

[0606] Step 6: Adjust the display order

[0607] The server adjusts the display order of information based on the emotion data sent from the terminal. As input, it receives emotion data and a summarized article list, and as output, it obtains the article list in the adjusted display order.

[0608] Specific behavior:

[0609] The server analyzes the sentiment data and places positive articles at the top if positive sentiment is detected, and negative articles at the bottom if negative sentiment is detected. An adjusted article display order is generated.

[0610] Step 7: Viewing information

[0611] The terminal displays the information to the user according to the display order sent from the server. As input, the adjusted display order article list is given, and as output, the articles displayed to the user are obtained.

[0612] Specific behavior:

[0613] The terminal displays the articles to the user based on the display order received from the server, allowing the user to view the articles in an order that suits their emotions.

[0614] In this way, a system is realized that adjusts the display order of information based on the user's emotions.

[0615] (Application example 3)

[0616] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0617] Conventional news distribution systems do not filter articles or adjust the display order according to the user's emotions, making it difficult to provide users with the information they want quickly and accurately. Furthermore, because the content of articles is lengthy, it takes time for users to understand all the information. This can lead to lower user satisfaction and a decrease in the frequency of use of news distribution services.

[0618] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0619] In this invention, the server includes scraping means for acquiring information from a specified information source, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate articles based on the search results, display means for displaying a list of the filtered articles, reporting means for reporting the displayed articles, emotion analysis means for analyzing the user's emotions, order adjustment means for adjusting the display order of articles based on the user's emotions, summarization means for summarizing the content of the articles, and reporting means for reporting the summarized articles. This enables filtering of articles and adjustment of the display order according to the user's emotions, thereby providing the information desired by the user quickly and accurately. Furthermore, summarizing articles allows the user to grasp the information they need in a short amount of time.

[0620] A "scraping method" is a technique for automatically obtaining information from a specified source.

[0621] "Search means" refers to a technique for conducting a keyword search based on the acquired information.

[0622] "Filtering means" refers to a technique for selecting appropriate articles based on search results.

[0623] "Display means" refers to a technique for displaying a list of filtered articles.

[0624] The "reporting means" is a technique for reporting the displayed article to the user.

[0625] "Emotion analysis means" is a technique for analyzing the user's emotions.

[0626] The "order adjustment means" is a technique for adjusting the display order of articles based on the user's feelings.

[0627] "Summarization means" refers to a technique for summarizing the content of an article.

[0628] A system for carrying out the present invention has the following configuration: A server includes scraping means for acquiring information from a specified information source, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate articles based on the search results, display means for displaying a list of the filtered articles, reporting means for reporting the displayed articles, emotion analysis means for analyzing user emotions, order adjustment means for adjusting the display order of articles based on the user emotions, summarization means for summarizing the content of the articles, and reporting means for reporting the summarized articles.

[0629] Hardware and software used

[0630] Hardware: Your smartphone's camera and microphone

[0631] software:

[0632] OpenCV: Camera image capture and face recognition

[0633] Transformers (Hugging Face): Sentiment Analysis and Article Summarization

[0634] TextBlob: Article Sentiment Analysis

[0635] Processing flow

[0636] 1. Scraping method: The server automatically retrieves news articles from the specified sources using web scraping technology.

[0637] 2. Search method: Based on the retrieved news articles, a keyword search is performed to identify articles that may be of interest to the user.

[0638] 3. Filtering: Based on the search results, appropriate articles are selected, so that only useful information is extracted for the user.

[0639] 4. Display method: The filtered articles are displayed in a list, allowing the user to select articles of interest from this list.

[0640] 5. Reporting: Reports the displayed article to the user, including the article title and summary.

[0641] 6. Emotion analysis: Using the smartphone camera and microphone, we analyze the user's facial expressions and tone of voice to identify their current emotions. We use OpenCV for facial recognition and Transformers for emotion analysis.

[0642] 7. Order adjustment method: The order of displaying articles is adjusted based on the user's emotions. If the user has positive emotions, positive articles are displayed at the top, and if the user has negative emotions, negative articles are displayed at the bottom.

[0643] 8. Summarization: Summarize the content of the article. Generate a summary of the article using Transformers.

[0644] 9. Reporting method: A summary of the article is reported to the user, allowing the user to grasp the necessary information in a short time.

[0645] Specific examples

[0646] For example, if a user expresses the emotion of "joy" through the smartphone camera, the system will display positive news articles at the top of the list. A summary of the article is also displayed at the same time, allowing users to quickly understand the content of the article.

[0647] Prompt Sentence Examples

[0648] Example prompts to input to a generative AI model:

[0649] If the user's emotion is "joy," then display and summarize positive news articles at the top.

[0650] Using this prompt, the generative AI model can filter and summarize articles according to the user's sentiment.

[0651] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0652] Step 1:

[0653] The server retrieves news articles from specified sources. It uses scraping techniques to collect article data from websites. The input is the URL of the source, and the output is the retrieved article data.

[0654] Step 2:

[0655] The server performs a keyword search based on the retrieved article data. It uses a search mechanism to identify articles that may be of interest to the user. The input is the article data and keywords, and the output is a list of articles that match the keywords.

[0656] Step 3:

[0657] The server filters relevant articles based on the search results. Using a filtering method, it extracts only information that is useful to the user. The input is a list of search results, and the output is a list of filtered articles.

[0658] Step 4:

[0659] The server displays a list of filtered articles, using a display means to allow the user to select articles of interest. The input is the filtered list of articles, and the output is the list of articles displayed to the user.

[0660] Step 5:

[0661] The server reports the displayed articles to the user. The reporting means provides the user with the article titles and summaries. The input is a list of displayed articles, and the output is the article information reported to the user.

[0662] Step 6:

[0663] The device analyzes the user's emotions using the smartphone's camera and microphone. Using emotion analysis means, it analyzes the user's facial expressions and tone of voice to identify their current emotions. The input is camera footage and audio data, and the output is the user's emotional information.

[0664] Step 7:

[0665] The server adjusts the display order of articles based on the user's emotions. Using an order adjustment means, if the emotion is positive, positive articles are displayed at the top, and if the emotion is negative, negative articles are displayed at the bottom. The input is the user's emotion information and the filtered article list, and the output is the adjusted display order of the articles.

[0666] Step 8:

[0667] The server summarizes the content of the articles. A summarization means is used to generate summaries of the articles. The input is a list of filtered articles, and the output is a list of summarized articles.

[0668] Step 9:

[0669] The server reports the summarized articles to the user. A reporting means is used to provide the summarized articles to the user. The input is a list of summarized articles, and the output is the summarized article information reported to the user.

[0670] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0671] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0672] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0673] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0674] [Second embodiment]

[0675] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0676] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0677] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0678] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0679] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0680] The camera 42 includes an optical system including a lens, an aperture, and a shutter, and a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0681] It is a small digital camera equipped with an imaging element such as a range sensor, and captures an image of the surroundings of the user 20 (for example, an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0682] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0683] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0684] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0685] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0686] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0687] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0688] "Example 1"

[0689] In one embodiment of the present invention, a system is implemented that combines a scraping means for acquiring information from a specified news site, a search means for performing a keyword search based on the acquired information, a filtering means for filtering appropriate articles based on the search results, a display means for displaying a list of the filtered articles, and a reporting means for reporting the displayed articles. Specifically, the scraping means is implemented using Python libraries such as BeautifulSoup and Scrapy, and a keyword search is performed based on the acquired information. To filter appropriate articles based on the search results, natural language processing technology is used to analyze the content of the articles and evaluate their importance. The filtered articles are displayed in a list, and the displayed articles are reported. Reports are made via email, an internal SNS, or the like.

[0690] "Example 2"

[0691] In another embodiment of the present invention, a system is implemented in which scraping means can acquire information from multiple news sites. Specifically, the URLs of multiple news sites are specified and information is acquired from each. This makes it possible to acquire a wider range of information.

[0692] "Example 3"

[0693] In yet another embodiment of the present invention, a system is implemented in which the reporting means can summarize and report the contents of the filtered articles. Specifically, the content of the articles is summarized using natural language processing technology and the summary is reported. This allows the content of the report to be concise.

[0694] The processing flow of each embodiment will be described below.

[0695] "Example 1"

[0696] Step 1: Obtain information from the specified news site. In this step, scraping is performed using Python libraries such as BeautifulSoup and Scrapy.

[0697] Step 2: Perform a keyword search based on the acquired information. In this step, the text information of the acquired articles is analyzed and articles containing the specified keywords are extracted.

[0698] Step 3: Filter appropriate articles based on the search results. In this step, natural language processing techniques are used to analyze the content of the articles and evaluate their importance. Articles with an importance level above a certain threshold are selected.

[0699] Step 4: List the filtered articles. This step lists the titles and links of the selected articles.

[0700] Step 5: Report the displayed articles. In this step, you report the list of displayed articles via email, internal SNS, etc.

[0701] "Example 2"

[0702] Step 1: Obtain information from multiple news sites. In this step, you specify the URLs of multiple news sites and obtain information from each of them.

[0703] Step 2: Integrate the acquired information and perform a keyword search. In this step, information acquired from multiple sites is integrated and articles containing the specified keywords are extracted.

[0704] Step 3: Based on the search results, appropriate articles are filtered and displayed as a list. In this step, steps 3 and 4 of the first embodiment are referred to.

[0705] Step 4: Report the displayed article. In this step, refer to step 5 of embodiment 1.

[0706] "Example 3"

[0707] Step 1: Obtain information from a specified news site and perform a keyword search. In this step, refer to steps 1 and 2 in the first embodiment.

[0708] Step 2: Filter appropriate articles based on the search results. In this step, refer to step 3 in example 1.

[0709] Step 3: Display a list of filtered articles. In this step, refer to step 4 of the first embodiment.

[0710] Step 4: Summarize and report the content of the displayed article. In this step, natural language processing technology is used to summarize the content of the article, and the summary is reported via email or internal social networking sites.

[0711] Example 1

[0712] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0713] Conventional information gathering systems have had difficulty efficiently acquiring data from specific information sources and quickly providing information that users are interested in. Furthermore, the process of selecting appropriate information from the acquired information and reporting it to users takes time and effort, which reduces the usefulness of the information.

[0714] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a data acquisition means for acquiring data from a specified information source, a search means for performing a keyword search based on the acquired data, a filtering means for filtering appropriate information based on the search results, a display means for displaying a list of the filtered information, and a reporting means for reporting the displayed information. This makes it possible to quickly and efficiently acquire information in which the user is interested, and to select and report appropriate information.

[0715] "Data acquisition means" refers to a means for acquiring data from a specified information source.

[0716] The "search means" is a means for performing a keyword search based on the acquired data.

[0717] The "filtering means" is a means for selecting appropriate information based on the search results.

[0718] The "display means" is a means for displaying a list of filtered information.

[0719] The "reporting means" is a means for reporting the displayed information to the user.

[0720] The present invention is a system for acquiring data from a specified information source and quickly and efficiently providing information that a user is interested in. A specific embodiment of this system will be described below.

[0721] The server uses Python libraries such as BeautifulSoup and Scrapy to retrieve data from specified sources. Specifically, BeautifulSoup is used to parse data from HTML or XML, and Scrapy is used to crawl the entire website. For example, the server retrieves the latest news articles from "https: / / example-news-site.com."

[0722] Next, the server performs a keyword search based on the acquired data. If the user specifies the keyword "generative AI model," the server searches for articles containing this keyword among the acquired news articles. The search can be performed using the standard Python library or regular expressions.

[0723] The server then filters appropriate information based on the search results. It uses natural language processing (NLP) techniques to analyze the content of articles and evaluate their importance. Specifically, it uses Python libraries such as NLTK and spaCy to analyze the content of articles and calculate an importance score. For example, the server uses NLTK to tokenize and tag articles by part of speech, and spaCy to analyze the content of articles.

[0724] The filtered information is displayed as a list on the terminal. The user can check the list of filtered information on the terminal screen. The terminal receives the list of filtered information from the server and displays it in a list using HTML or GUI.

[0725] Finally, the server reports the displayed information. The report is sent via email or an internal social networking service (e.g., Slack or Microsoft Teams). Users can receive the reported information via email or social networking service. The server formats the list of filtered information into an email body or social networking service message, and sends the email using the SMTP protocol or posts the message to the social networking service using an API.

[0726] As a specific example, if a user specifies the keyword "generative AI model," the server performs the following processing steps.

[0727] 1. The server retrieves data from a specified source using BeautifulSoup or Scrapy.

[0728] 2. The server searches for articles containing the keyword "generative AI model" based on the acquired data.

[0729] 3. The server uses natural language processing technology to analyze the content of the articles based on the search results and evaluate their importance.

[0730] 4. The terminal displays a list of filtered articles.

[0731] 5. The server reports the displayed articles via email or internal SNS.

[0732] An example of a prompt sentence might be:

[0733] Get the latest news articles containing the keyword "generative AI model," rate them by importance, and list them. Report the rated articles by email.

[0734] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0735] Step 1: Data Acquisition

[0736] The server retrieves data from the specified sources. It receives the specified URL and list of sources as input and generates the retrieved HTML or XML data as output. Specifically, the server launches Scrapy and accesses the specified URL. Next, it uses BeautifulSoup to parse the HTML document and extract information such as the article title, body, and date.

[0737] Step 2: Keyword search

[0738] The server performs a keyword search based on the acquired data. As input, it receives a keyword specified by the user (e.g., "generative AI model") and generates a list of articles containing the keyword as output. Specifically, the server stores the acquired article data in list format and checks whether each article in the list contains the specified keyword.

[0739] Step 3: Filtering

[0740] The server filters appropriate information based on the search results. As input, it receives a list of articles obtained through keyword searches, and as output, it generates a filtered list of articles with their importance assessed. Specifically, the server uses NLTK to tokenize and tag articles with parts of speech, and spaCy to analyze the content of the articles and calculate an importance score. Articles with an importance score above a certain level are filtered and added to the list.

[0741] Step 4: List View

[0742] The terminal displays the filtered information in a list. As input, it receives the list of filtered articles from the server, and as output, it displays the information in a format that can be viewed by the user. Specifically, the terminal displays the received article list using HTML or a GUI.

[0743] Step 5: Report

[0744] The server reports the displayed information. It receives the list of filtered articles as input and generates a message to be reported by email or internal SNS as output. Specifically, the server formats the list of filtered articles into an email body or SNS message, and sends the email using the SMTP protocol or posts the message to the SNS using an API.

[0745] (Application example 1)

[0746] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0747] In modern society, there is a huge amount of information available on the Internet, making it difficult for users to efficiently collect and appropriately filter information of interest from that information. Furthermore, there is a lack of a means to quickly report filtered information, which makes it difficult for users to obtain the information they need in a timely manner. Furthermore, there is a lack of a function to summarize and report filtered information, making it difficult for users to quickly understand the information.

[0748] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0749] In this invention, the server includes scraping means for acquiring information from specified information sources, search means for performing keyword searches based on the acquired information, filtering means for filtering appropriate information based on the search results, display means for displaying a list of the filtered information, reporting means for reporting the displayed information, and reporting means for reporting the filtered information using electronic communication means. This enables users to efficiently collect information of interest, appropriately filter it, and quickly report it. In addition, by summarizing and reporting the filtered information, users can quickly understand the information.

[0750] A "scraping method" is a technique for automatically obtaining information from a specified source.

[0751] "Search means" refers to a technique for searching for information using specific keywords based on the acquired information.

[0752] "Filtering means" is a technology that selects appropriate information based on search results and removes unnecessary information.

[0753] "Display means" refers to a technique for visually presenting filtered information to the user.

[0754] "Reporting means" refers to a technique for notifying the user of the displayed information.

[0755] "Electronic communication means" refers to the technology of transmitting information using electronic means such as the Internet or email.

[0756] As an embodiment of the present invention, the following system is constructed: A server includes scraping means for acquiring information from a specified information source, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate information based on the search results, display means for displaying a list of the filtered information, reporting means for reporting the displayed information, and reporting means for reporting the filtered information using electronic communication means.

[0757] Hardware and software used

[0758] Hardware: Servers, user devices (smartphones, PCs, etc.)

[0759] Software: Python, BeautifulSoup, Scrapy, NLTK, Scikit-learn, smtplib

[0760] Data processing and calculation

[0761] 1. Scraping method: The server retrieves articles from specified sources using Python's BeautifulSoup or Scrapy. This allows you to automatically collect the necessary information from news sites on the Internet.

[0762] 2. Search method: The server searches the retrieved articles using keywords set by the user, allowing it to efficiently extract information that interests the user.

[0763] 3. Filtering method: The server analyzes the content of articles using NLTK and Scikit-learn, and evaluates their importance using TF-IDF vectorization and cosine similarity, which allows it to select relevant information and remove unnecessary information.

[0764] 4. Display: The server displays a list of the filtered information on the user's terminal, allowing the user to visually check the information of interest.

[0765] 5. Reporting: The server summarizes the filtered information and reports it to the user's email or social networking site using smtplib, allowing the user to quickly obtain important information.

[0766] Specific examples

[0767] For example, if you are developing a news filter application, you can input the following prompt sentences into your generative AI model:

[0768] Example prompt sentence:

[0769] I'm developing a news filter application. I want to implement a feature that scrapes articles from specified news sites, filters the articles based on keywords set by the user, selects and displays important articles, and reports them via email. Please generate code that meets the following requirements using Python's BeautifulSoup, NLTK, Scikit-learn, and smtplib.

[0770] News site URL: https: / / example-news-site.com

[0771] Keywords: ['AI', 'technology', 'innovation']

[0772] User email: user@example.com

[0773] In this way, it becomes possible for users to efficiently collect information of interest, filter it appropriately, and quickly report it. By summarizing and reporting the filtered information, users can quickly understand the information.

[0774] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0775] Step 1:

[0776] The server retrieves information from the specified source. Specifically, it uses Python's BeautifulSoup or Scrapy to scrape articles from news sites. It receives the URL of the news site as input and generates a list of retrieved articles as output.

[0777] Step 2:

[0778] The server searches the retrieved articles using keywords set by the user. It receives the list of retrieved articles and the keywords set by the user as input, and generates a list of articles that match the keywords as output. Specifically, it checks whether each article contains the keywords and extracts the matching articles.

[0779] Step 3:

[0780] The server filters appropriate information based on the search results. It receives a list of articles matching the keywords as input and generates a list of highly relevant articles as output. Specifically, it analyzes the content of articles using NLTK and Scikit-learn, and evaluates their importance using TF-IDF vectorization and cosine similarity.

[0781] Step 4:

[0782] The server displays the filtered information in a list on the user's device. It receives a list of highly important articles as input and generates a list of articles to be displayed on the user's device as output. Specifically, it formats the articles in HTML or JSON format and displays them in the user interface.

[0783] Step 5:

[0784] The server summarizes the filtered information and reports it to the user's email or social media. It receives a list of highly important articles as input and generates email or social media messages containing the summarized article content as output. Specifically, it sends emails using smtplib and posts messages using the SNS API.

[0785] Example 2

[0786] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0787] Conventional information collection systems often only acquire information from specific sources, resulting in a lack of comprehensiveness. Furthermore, the acquired information is often not analyzed, stored, or provided to users efficiently. Furthermore, the summary report of filtered information is not automated, placing a heavy burden on users.

[0788] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0789] In this invention, the server includes a data collection means for acquiring information from specified information sources, a search means for performing a keyword search based on the acquired information, a filtering means for filtering appropriate information based on the search results, a display means for displaying a list of the filtered information, a report means for reporting the displayed information, a means for receiving a list of URLs of the specified information sources, a means for sending an HTTP request to each URL, a means for acquiring HTML content, a means for analyzing the HTML content and extracting necessary information, a means for saving the extracted information in a database, and a means for providing the saved information to a user. This makes it possible to efficiently collect, analyze, and save a wide range of information from multiple information sources and provide it to a user.

[0790] "Data Collection Method" means a method for obtaining information from a designated source.

[0791] The "search means" is a means for performing a keyword search based on the acquired information.

[0792] The "filtering means" is a means for selecting appropriate information based on the search results.

[0793] The "display means" is a means for displaying a list of filtered information.

[0794] The "reporting means" is a means for reporting the displayed information to the user.

[0795] The "means for receiving a URL list" is a means for receiving a URL list of a specified information source.

[0796] "Means for sending HTTP requests" refers to means for sending HTTP requests to each URL.

[0797] The "means for acquiring HTML content" refers to a means for acquiring HTML content returned as a response to an HTTP request.

[0798] The "means for analyzing HTML content" refers to a means for analyzing acquired HTML content and extracting necessary information.

[0799] The "means for storing in a database" refers to a means for storing the extracted information in a database.

[0800] "Means for providing to the user" refers to means for providing the stored information in a form that can be accessed by the user.

[0801] This invention relates to a system for acquiring information from a specified information source, and analyzing, storing, and providing the information. A specific embodiment of this system will be described below.

[0802] The server uses a data collector to retrieve information from the specified sources. The data collector uses Python's requests library to send an HTTP request to each URL and retrieve the HTML content. The retrieved HTML content is then parsed using libraries such as BeautifulSoup or Scrapy to extract the required information.

[0803] The extracted information is stored in a database using an RDBMS such as MySQL or PostgreSQL. The stored information is provided to users through a web application built using Flask or Django. Users can access and view this information through their browser.

[0804] As a concrete example, suppose a user wants to obtain information from the following news site:

[0805] https: / / example-news1.com

[0806] https: / / example-news2.com

[0807] The server receives these URLs as a list and retrieves the article titles and body text from each site, storing this information in a database and allowing users to access it through a web application.

[0808] By inputting the following prompt sentence into the generative AI model, we can generate a description of a program that retrieves information from a news site.

[0809] Example prompt sentence:

[0810] "Describe a program that retrieves information from multiple news sites. Specifically, explain how to retrieve the HTML content of each site from a given list of URLs, extract the article titles and body text, and store them in a database. Include the names of any specific libraries or databases you use."

[0811] In this way, the server can efficiently collect, analyze, store, and provide a wide range of information from multiple sources to users.

[0812] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0813] Step 1:

[0814] The server receives a list of URLs for news sites specified by the user. The input is the list of URLs provided by the user, and the output is the list of URLs received by the server. This list is often provided in JSON format, CSV format, etc.

[0815] Step 2:

[0816] The server sends an HTTP request to each URL in the received URL list. The input is the URL list, and the output is the HTTP response obtained from each URL. This is done using Python's requests library.

[0817] Step 3:

[0818] The server retrieves the HTML content returned as a response to the HTTP request. The input is the HTTP response, and the output is the retrieved HTML content. The retrieved HTML content is analyzed in the next step.

[0819] Step 4:

[0820] The server uses libraries such as BeautifulSoup or Scrapy to parse the retrieved HTML content and extract the necessary information. The input is the HTML content, and the output is the extracted information (e.g., article title, body, publication date, etc.). Specifically, it parses HTML tags and extracts the necessary data.

[0821] Step 5:

[0822] The server saves the extracted information in a database. The input is the extracted information, and the output is the information saved in the database. The database uses an RDBMS such as MySQL or PostgreSQL. When saving, the data is inserted into a table with fields such as the title, body, and publication date of each article.

[0823] Step 6:

[0824] The server provides the stored information in a form that can be accessed by the user. The input is the information stored in the database and the output is the information that is presented to the user. For example, you can build a web application using Flask or Django to allow the user to view the information through a browser.

[0825] In this way, the server can efficiently collect, analyze, and store a wide range of information from multiple news sites and provide it to users.

[0826] (Application example 2)

[0827] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0828] Conventional news aggregator systems only retrieve information from specific news sites, which can lead to information bias. Furthermore, they lacked a way to efficiently store and later reference the retrieved information, making it difficult for users to quickly obtain the information they needed.

[0829] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes scraping means for acquiring information from specified information sources, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate information based on the search results, display means for displaying a list of the filtered information, reporting means for reporting the displayed information, and storage means for saving the acquired information in a data format. This makes it possible to acquire a wide range of information from multiple information sources and efficiently store and reference it.

[0830] "Specified information source" refers to a website, database, or other information source that the user has pre-selected.

[0831] "Scraping method" refers to a technology or program used to automatically obtain data from a specified source.

[0832] "Keyword search" refers to the process of locating relevant information based on specific words or phrases within retrieved information.

[0833] "Search means" refers to the technology or program used to perform keyword searches.

[0834] "Filtering means" refers to technology or programs for selecting appropriate information from search results based on specific conditions.

[0835] "Display means" refers to the technology or program used to visually present filtered information to users.

[0836] "Reporting means" refers to the technology or program used to notify users of displayed information.

[0837] "Storage means" refers to the technology or programs used to store acquired information in data format so that it can be referenced later.

[0838] A system for implementing this invention includes scraping means for acquiring information from a specified information source, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate information based on the search results, display means for displaying a list of the filtered information, reporting means for reporting the displayed information, and storage means for saving the acquired information in a data format.

[0839] Program processing explanation

[0840] The server implements the scraping method using a Python program. Specifically, it uses the requests library to retrieve HTML content from the specified source URL, and then parses the HTML content using the BeautifulSoup library. Article titles and summaries are extracted from the parsed data and stored in a database.

[0841] The search function searches for information in the database based on keywords entered by the user. The search results are then filtered based on appropriate criteria, for example, to extract only the most recent information or information related to a specific category.

[0842] The display means visually presents the filtered information to the user's device, using a web browser or smartphone app. The reporting means notifies the user of the filtered information, and could be email or push notification.

[0843] The storage method saves the acquired information in a data format so that it can be referenced later. This can be done in JSON format or a database.

[0844] Specific examples

[0845] For example, if a user wants to get the latest news in the "Technology" category, they might use a prompt like this:

[0846] Prompt Sentence Examples

[0847] urls = [

[0848] 'https: / / example-tech-news.com'

[0849] ]

[0850] aggregator = NewsAggregator(urls)

[0851] news_data = aggregator.fetch_news()

[0852] aggregator.save_news(news_data)

[0853] By inputting this prompt into a generative AI model, the latest technology news can be retrieved from specified sources and provided to the user.

[0854] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0855] Step 1:

[0856] The server receives the URLs of the information sources specified by the user as input, specifically, it obtains a list of URLs specified by the user, which is then processed by the scraping means.

[0857] Step 2:

[0858] The server uses the requests library to get the HTML content from the specified URL. It takes the URL as input and gets the HTML content as output. This HTML content is parsed in the next step.

[0859] Step 3:

[0860] The server uses the BeautifulSoup library to parse the retrieved HTML content. It receives the HTML content as input and obtains the parsed data (article title and summary) as output. Specifically, it parses HTML tags and extracts the article title and summary.

[0861] Step 4:

[0862] The server saves the parsed data in a database. It receives the parsed data as input and gets the data saved in the database as output. It uses a storage means to store the data in JSON format or in a database.

[0863] Step 5:

[0864] A user performs a search by entering keywords. The search tool receives keywords as input and obtains search results as output. The search tool is used to search for information in a database based on keywords.

[0865] Step 6:

[0866] The server filters the search results. It receives the search results as input and obtains the filtered information as output. The filtering means is used to select information based on specific criteria (e.g., the most recent information or a specific category).

[0867] Step 7:

[0868] The server displays the filtered information on the user's device. It receives the filtered information as input and obtains visually displayed information as output. It displays the information on a web browser or smartphone app using a display means.

[0869] Step 8:

[0870] The server reports the filtered information to the user. It receives the filtered information as input and obtains the notified information as output. It notifies the user of the information by email, push notification, etc. using a reporting means.

[0871] Step 9:

[0872] The server stores the retrieved information in a data format. It receives the retrieved information as input and gets the stored data as output. It uses a storage method to store the information in a database or in JSON format for future reference.

[0873] Example 3

[0874] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0875] In today's information society, a huge amount of information is generated every day. It is extremely important to efficiently retrieve, summarize, and report the necessary information from that information. However, doing this manually takes time and effort, and is inefficient. In addition, existing systems do not integrate the processes of information collection, filtering, summarization, and reporting, which makes them difficult to use for users.

[0876] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0877] In this invention, the server includes a collection means for acquiring information from a specified information source, a search means for performing a keyword search based on the acquired information, a filtering means for filtering appropriate information based on the search results, a summarization means for summarizing the filtered information, and a reporting means for reporting the summarized information. This makes it possible to efficiently acquire necessary information from a vast amount of information and summarize and report it.

[0878] "Collection means" means the means for obtaining information from a designated source.

[0879] The "search means" is a means for performing a keyword search based on the acquired information.

[0880] The "filtering means" is a means for selecting appropriate information based on the search results.

[0881] A "summarization means" is a means for concisely summarizing filtered information.

[0882] A "reporting means" is a means for notifying a user of summarized information.

[0883] This invention is a system that acquires information from a server-specified information source, searches the acquired information by keyword, filters appropriate information, summarizes the filtered information, and reports the summarized information to a user. A specific embodiment of this system will be described below.

[0884] Hardware and software used

[0885] Hardware: Servers, terminals

[0886] Software: Natural language processing libraries (e.g., spaCy, NLTK), generative AI models (e.g., GPT-4)

[0887] Program processing

[0888] Retrieving articles

[0889] The server collects articles from the Internet. Specifically, it obtains the latest articles using the RSS feeds and APIs of news sites. For example, the server accesses "https: / / example.com / rss" and downloads the latest RSS feed. It then analyzes the obtained RSS feed and extracts the article title, URL, content, etc.

[0890] Article filtering

[0891] The server filters the collected articles based on specific keywords or categories. For example, it selects only articles in the "technology" category. The server analyzes the metadata (title, category, tags, etc.) of the articles it retrieves and keeps only articles that fall into the "technology" category in the list.

[0892] Article Summary

[0893] The server uses a natural language processing library (e.g., spaCy) to analyze the content of the filtered articles and generate summaries. Specifically, it extracts the main points of the articles and summarizes them into short sentences. The server inputs the filtered article text into spaCy, which extracts important sentences and key phrases. It then generates a summary based on the extracted information.

[0894] Summary Report

[0895] The server reports the generated summary to the user. Possible reporting methods include email, notification, dashboard, etc. The server sends the generated summary to the user's email address. For example, it sends an email using the SMTP protocol.

[0896] Specific examples

[0897] Retrieving articles

[0898] The server retrieves the latest news articles from "https: / / example.com / rss".

[0899] Article filtering

[0900] The server filters only articles in the "technology" category.

[0901] Article Summary

[0902] The server summarizes the article as follows:

[0903] Original article: "A new AI technology has been announced that promises to significantly speed up data analysis."

[0904] Summary: "New AI techniques speed up data analysis."

[0905] Summary Report

[0906] The server sends the summary to the user's email address.

[0907] Example prompts for generative AI models

[0908] Prompt statement

[0909] "Please summarize the following article: 'A new AI technology has been announced that promises to significantly improve the speed of data analysis.'"

[0910] In this way, a system is implemented in which the server performs a series of processes from collecting articles to summarizing and reporting. By receiving a concise summary, users can obtain information efficiently. The flow of the specific process in the third embodiment will be described with reference to FIG. 15.

[0911] Step 1: Get the article

[0912] The server collects articles from specified sources. As input, it receives the news site's RSS feed or API URL. Specifically, the server accesses "https: / / example.com / rss" and downloads the latest RSS feed. It then analyzes the obtained RSS feed and extracts the article title, URL, content, etc. As output, it generates a list of the extracted article information.

[0913] Step 2: Filtering articles

[0914] The server filters the collected articles based on specific keywords or categories. As input, it receives the list of article information obtained in step 1 and the filtering criteria (e.g., category "Technology"). Specifically, the server analyzes the article metadata (title, category, tags, etc.) and keeps in the list only those articles that match the filtering criteria. As output, it generates a list of filtered article information.

[0915] Step 3: Summarize the article

[0916] The server summarizes the content of the filtered articles. As input, it receives the list of article information filtered in step 2. Specifically, the server analyzes the text of the articles using a natural language processing library (e.g., spaCy) and extracts important sentences and key phrases. It then generates a summary based on the extracted information. As output, it generates a list of summarized article information.

[0917] Step 4: Report the summary

[0918] The server reports the generated summary to the user. As input, it receives the list of article information summarized in step 3 and the user's contact information (e.g., email address). As a specific operation, the server sends the summary to the user's email address using the SMTP protocol. As an output, it provides a confirmation of the summary sent to the user.

[0919] (Application example 3)

[0920] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0921] In modern society, a huge amount of information is generated every day, and users are required to quickly and efficiently obtain the information they need. However, conventional information acquisition systems have had the problem of being unable to properly filter and summarize information that interests users. In particular, there is a lack of means to quickly grasp long information such as news articles, which reduces the efficiency of users' information acquisition.

[0922] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0923] In this invention, the server includes an acquisition means for acquiring information from a specified information source, a search means for performing a keyword search based on the acquired information, a filtering means for filtering appropriate information based on the search results, a summarization means for summarizing and reporting the content of the filtered information, and a provision means for providing the summarized information to the user. This enables the user to quickly and efficiently acquire the information they need from a vast amount of information and grasp important content in a short amount of time.

[0924] "Acquisition means" refers to a means for acquiring information from a specified information source.

[0925] The "search means" is a means for performing a keyword search based on the acquired information.

[0926] The "filtering means" is a means for filtering appropriate information based on the search results.

[0927] The "display means" is a means for displaying a list of filtered information.

[0928] A "reporting means" is a means for reporting displayed information.

[0929] The "summarization means" is a means for summarizing and reporting the contents of the filtered information.

[0930] "Means of provision" refers to the means for providing summarized information to users.

[0931] A "generative AI model" is a model for generating information using artificial intelligence technology.

[0932] A "prompt sentence" is an instruction sentence input to a generative AI model.

[0933] As an embodiment of the present invention, a system is constructed that efficiently acquires news articles, summarizes them, and provides them to users. A specific embodiment of this system is described below.

[0934] System configuration

[0935] This system is composed of a server and a user terminal. The server includes an acquisition means for acquiring information from a specified information source, a search means for performing a keyword search based on the acquired information, a filtering means for filtering appropriate information based on the search results, a summarization means for summarizing and reporting the content of the filtered information, and a provision means for providing the summarized information to the user.

[0936] Hardware and software used

[0937] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[0938] Software: Python, Requests library, Hugging Face Transformers library

[0939] Data processing and calculation

[0940] 1. Acquisition method: The server acquires the latest news articles from the specified source using a news API. For example, it acquires article data in JSON format from the news API.

[0941] 2. Search method: The retrieved article data is searched using keywords specified by the user. The search results are narrowed down to articles with the highest relevance.

[0942] 3. Filtering methods: Filtering the search results to find more relevant articles, for example, based on criteria such as the article's credibility or publication date.

[0943] 4. Summarization: To summarize the content of the filtered articles, we use a generative AI model. Specifically, we use Hugging Face's Transformers library to invoke a pre-trained summarization model and generate a summary of the article.

[0944] 5. Delivery method: The summarized article is delivered to the user's device. Users can view the summarized article through their device, such as a smartphone or tablet.

[0945] Specific examples

[0946] For example, if a user searches for a news article using the keyword "economic growth," the server retrieves relevant articles from the news API and narrows down the articles related to "economic growth" using the search means. Next, it selects highly reliable articles using the filtering means and summarizes the content of the articles using the summarization means. Finally, the summarized articles are provided to the user's device.

[0947] Prompt Sentence Examples

[0948] "Please summarize the contents of the news article. Here is the article: 'Japan's economic growth rate exceeded expectations. The government has announced new economic policies and is hopeful about future growth.'"

[0949] In this way, users can efficiently obtain important information in a short time.

[0950] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0951] Step 1:

[0952] The server uses the news API to retrieve the latest news articles from the specified source. It uses the API URL and authentication key as input and gets the news article data in JSON format as output. Specifically, it sends an HTTP request using the Requests library and receives the article data as a response.

[0953] Step 2:

[0954] The server searches the retrieved article data using keywords specified by the user. The server uses the keywords specified by the user and the retrieved article data as input, and obtains a list of articles related to the keywords as output. Specifically, it analyzes the article data and extracts articles that contain the keywords.

[0955] Step 3:

[0956] The server then filters the search results to find more relevant articles. It uses the list of search results as input and obtains a filtered list of articles as output. Specifically, it filters based on criteria such as the article's credibility and publication date.

[0957] Step 4:

[0958] The server uses a generative AI model to summarize the content of the filtered articles. It uses the filtered article text as input and obtains summarized text as output. Specifically, it invokes a pre-trained summarization model using Hugging Face's Transformers library to generate article summaries.

[0959] Step 5:

[0960] The server provides the summarized article to the user terminal. It uses the summarized text as input and obtains the summarized article displayed on the user terminal as output. Specifically, it sends the summarized text to the user terminal for viewing by the user.

[0961] Step 6:

[0962] The user browses the summarized article through their device. The summarized article provided by the server is used as input, and the display of the summarized article is obtained as output. Specifically, the summarized article is displayed on the screen of the user's device, allowing the user to grasp important information in a short time.

[0963] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0964] "Example 1"

[0965] An example of a system using an emotion engine is one that provides an interface for recognizing a user's emotion. Specifically, an interface is provided that allows the user to input their own emotion into the system. This interface can take various forms, such as a text input field or a selection of icons that represent emotions. When the user inputs their emotion, the emotion engine analyzes the information and recognizes the user's emotion.

[0966] "Example 2"

[0967] One example of a system using an emotion engine is one that adjusts the content of reports based on the user's emotions. Specifically, if the user is expressing positive emotions such as joy or excitement, the system will prioritize reporting positive news articles to match those emotions. Conversely, if the user is expressing negative emotions such as sadness or anger, the system will report less negative news articles to match those emotions.

[0968] "Example 3"

[0969] As an example of a system using an emotion engine, the display order of filtered articles is adjusted based on the user's emotions. Specifically, if the user expresses positive emotions such as joy or excitement, the system will display positive news articles at the top of the list in line with that emotion. Conversely, if the user expresses negative emotions such as sadness or anger, the system will display negative news articles at the bottom in line with that emotion.

[0970] The processing flow of each embodiment will be described below.

[0971] "Example 1"

[0972] Step 1: The user accesses the system.

[0973] Step 2: The system provides the user with an emotion input interface, which can take various forms, such as a text input field or a selection of icons that represent emotions.

[0974] Step 3: The user inputs their emotions.

[0975] Step 4: The emotion engine analyzes the input from the user and recognizes the user's emotion.

[0976] "Example 2"

[0977] Step 1: The user accesses the system and inputs their emotions.

[0978] Step 2: The emotion engine analyzes the input from the user and recognizes the user's emotion.

[0979] Step 3: The system adjusts the reporting content based on the user's emotions. Specifically, if the user expresses positive emotions such as joy or excitement, the system will prioritize reporting positive news articles to match that emotion. Conversely, if the user expresses negative emotions such as sadness or anger, the system will moderate reporting negative news articles to match that emotion.

[0980] "Example 3"

[0981] Step 1: The user accesses the system and inputs their emotions.

[0982] Step 2: The emotion engine analyzes the input from the user and recognizes the user's emotion.

[0983] Step 3: The system adjusts the display order of the filtered articles based on the user's emotions. Specifically, if the user expresses positive emotions such as joy or excitement, the system will display positive news articles at the top to match that emotion. Conversely, if the user expresses negative emotions such as sadness or anger, the system will display negative news articles at the bottom to match that emotion.

[0984] Example 1

[0985] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0986] Conventional information collection systems only retrieve data from specific sources and perform keyword searches and filtering, which limits the comprehensiveness and accuracy of the information. Furthermore, there was a lack of efficient ways to report filtered information, making it difficult for users to quickly obtain the information they needed. This resulted in the time and effort required to retrieve and report information.

[0987] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0988] In this invention, the server includes a data acquisition means for acquiring data from a specified information source, a search means for performing a keyword search based on the acquired data, and a filtering means for filtering appropriate information based on the search results. By acquiring data from multiple information sources and performing a keyword search and filtering, the comprehensiveness and accuracy of information can be improved, and users can quickly obtain the information they need.

[0989] "Data acquisition means" refers to a means for acquiring data from a specified information source.

[0990] The "search means" is a means for performing a keyword search based on the acquired data.

[0991] The "filtering means" is a means for filtering appropriate information based on the search results.

[0992] The "display means" is a means for displaying a list of filtered information.

[0993] "Reporting means" means for reporting displayed information.

[0994] The present invention relates to a system that acquires data from a specified information source, performs keyword search and filtering, and displays and reports the filtered information in a list. Specific embodiments of this system will be described below.

[0995] The server retrieves data from the specified source using Python libraries such as BeautifulSoup or Scrapy. Specifically, the server parses the HTML structure of the source and extracts information such as the article title, body, and date. For example, the server accesses the homepage of "https: / / example-news.com" and retrieves the list of the latest articles.

[0996] Next, the server performs a keyword search based on the acquired data. If the user specifies the keyword "AI technology," the server searches for articles related to "AI technology" from among the acquired articles. Specifically, the server checks whether the keyword "AI technology" is included in the article title or text.

[0997] The server then filters out appropriate information based on the search results. It uses natural language processing (NLP) technology to analyze the content of the article and rate its importance. For example, if the content of an article is about the latest trends in AI technology, the server will rate the article highly important. Specifically, the server analyzes the frequency of keyword occurrences and context in the article to calculate a score.

[0998] The filtered information is displayed in a list on the device. The user can check the titles and summaries of the filtered articles on the device screen. Specifically, the device displays the titles, summaries, and links of the filtered articles in a list format.

[0999] Finally, the server reports the displayed information. This is done via email, an internal social networking site, or other means. For example, if an important article is found, the server sends an email to the user containing a link to the article and a summary. Specifically, the server notifies the user by sending an email using the SMTP protocol.

[1000] As a concrete example, if a user wants to know the latest news about "AI technology," they can input the following prompt sentence into the generative AI model:

[1001] Example prompt sentence:

[1002] "Get the latest news on AI technology, filter and report on the stories that matter."

[1003] After entering this prompt, the system will retrieve data from the specified source, perform keyword search and filtering, and then list and report the filtered information, allowing users to efficiently obtain the latest important news on "AI technology."

[1004] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1005] Step 1:

[1006] The server retrieves data from the specified information source. Specifically, the server uses Python libraries such as BeautifulSoup and Scrapy to parse the HTML structure of the information source and extract information such as the article title, body, and date. The URL of the information source is given as input, and the extracted article data is obtained as output.

[1007] Step 2:

[1008] The server performs a keyword search based on the acquired data. If the user specifies the keyword "AI technology," the server will search for articles related to "AI technology" from among the acquired articles. The input is the keyword specified by the user and the acquired article data, and the output is a list of articles that match the keywords. Specifically, the server checks whether the keyword "AI technology" is included in the article title or body of the article.

[1009] Step 3:

[1010] The server filters appropriate information based on the search results. It uses natural language processing (NLP) technology to analyze the content of articles and evaluate their importance. Keyword search results are given as input, and a list of articles with their importance evaluated is obtained as output. Specifically, the server analyzes the keyword frequency and context of the articles and calculates a score.

[1011] Step 4:

[1012] The terminal displays the filtered information in a list. The user can check the titles and summaries of the filtered articles on the terminal screen. The input is a list of filtered articles, and the output is the display of the articles in a format that the user can view. Specifically, the terminal displays the titles, summaries, and links of the filtered articles in a list format.

[1013] Step 5:

[1014] The server reports the displayed information. The report is sent via email, an internal SNS, etc. The input is a list of filtered articles, and the output is confirmation that the report has been sent. Specifically, the server notifies the user by sending an email using the SMTP protocol. For example, if an important article is found, the server sends the user an email containing a link to the article and a summary.

[1015] (Application example 1)

[1016] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1017] In today's information society, it is difficult for users to efficiently obtain information that is important to them from a vast amount of information. In addition, there is a problem that information is not provided in accordance with the user's emotions, making it difficult to increase user satisfaction.

[1018] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1019] In this invention, the server includes scraping means for acquiring information from a specified information source, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate information based on the search results, display means for displaying a list of the filtered information, report means for reporting the displayed information, emotion recognition means for recognizing the user's emotion, and priority display means for displaying information preferentially based on the emotion recognition means. This allows the user to efficiently acquire information that is important to them and further enables the provision of information according to their emotions.

[1020] A "scraping method" is a technique for automatically obtaining information from a specified source.

[1021] "Search means" refers to a technique for searching for information using specific keywords based on the acquired information.

[1022] "Filtering means" is a technique for selecting appropriate information based on search results.

[1023] The "display means" is a technique for visually presenting the filtered information to the user.

[1024] "Reporting means" refers to a technique for notifying the user of the displayed information.

[1025] "Emotion recognition means" is a technology that analyzes and recognizes the user's emotions.

[1026] The "priority display means" is a technology for displaying information preferentially based on emotion recognition means.

[1027] The system for carrying out the present invention has a function of acquiring information from a specified information source and displaying the information preferentially based on the user's emotions. A specific embodiment of this system will be described below.

[1028] Hardware and software used

[1029] Hardware: Smartphones, servers

[1030] Software: Python, BeautifulSoup, nltk, smtplib

[1031] System configuration

[1032] 1. Scraping Method

[1033] The server uses Python's BeautifulSoup library to automatically retrieve information from specified sources (e.g., news sites), which allows it to parse HTML structures and extract the required information.

[1034] 2. Search methods

[1035] Based on the information retrieved, the server searches for information using specific keywords, using the standard Python library to perform text searches.

[1036] 3. Filtering Methods

[1037] To select relevant information based on the search results, the server uses the SentimentIntensityAnalyzer from the nltk library, which analyzes the content of the information and evaluates its importance.

[1038] 4. Display means

[1039] The terminal (smartphone) visually presents the filtered information to the user using the smartphone's display.

[1040] 5. Reporting methods

[1041] The server uses the smtplib library to send emails to notify the user of the displayed information, allowing for a summary report of the filtered information.

[1042] 6. Emotion recognition means

[1043] The terminal provides a text input interface to analyze and recognize the user's emotions. When the user inputs their emotions, the server analyzes the information and recognizes the emotions.

[1044] 7. Priority display method

[1045] The server prioritizes displaying information based on the emotion recognition means, thereby enabling provision of information according to the user's emotion.

[1046] Specific examples

[1047] For example, if a user wants to get news about "technology," they might use a prompt like this:

[1048] Prompt Sentence Examples

[1049] Enter your news site URL: https: / / example.com / news

[1050] Enter your keyword: Technology

[1051] Enter the email address to report to: user@example.com

[1052] Please enter your current feelings (e.g. happy, sad, angry): Happy

[1053] Based on this prompt, the server retrieves information from the specified news site and searches for articles related to "technology." The server then filters the search results to display appropriate articles based on the user's sentiment. The filtered articles are then reported in a summary to the user's email address.

[1054] In this way, the user can efficiently obtain information that is important to him / her and can also receive information that corresponds to his / her emotions.

[1055] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1056] Step 1:

[1057] The server receives the URL of the specified news site as input, then uses Python's BeautifulSoup library to parse the HTML structure of the news site and extract the article information, which is then saved in text format.

[1058] Step 2:

[1059] The server receives the keywords entered by the user. It then performs a keyword search on the saved article information. It uses the standard Python library to search for articles containing the keywords in the article information and outputs a list of matching articles.

[1060] Step 3:

[1061] The server receives the list of articles obtained as search results as input. Then, it analyzes the content of each article and evaluates its importance using the SentimentIntensityAnalyzer in the nltk library. It filters out articles with high importance and outputs the filtered list of articles.

[1062] Step 4:

[1063] The device receives the filtered article list as input, then visually displays the filtered articles to the user using the smartphone's display, and outputs the displayed articles in a format that can be viewed by the user.

[1064] Step 5:

[1065] The server receives the filtered article list as input, then uses the smtplib library to summarize the filtered article content and send it to the user's email address, and the summarized article content is output in email format.

[1066] Step 6:

[1067] The terminal receives emotion information input by the user, then transmits the emotion information to the server, and outputs the emotion information in text format.

[1068] Step 7:

[1069] The server receives emotion information sent by the user as input, then analyzes the emotion information using emotion recognition means to recognize the user's emotion, and stores the recognized emotion information as internal data.

[1070] Step 8:

[1071] The server receives the recognized emotion information and the filtered article list as input. Then, the server re-evaluates the article list to prioritize articles according to the user's emotion based on the emotion recognition means. The re-evaluated article list is sorted based on the priority and transmitted to the terminal.

[1072] Example 2

[1073] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1074] Conventional news information acquisition systems have the problem of limiting the range of information they can obtain because they only obtain information from specific news sites. Furthermore, they do not adjust the content of reports based on the user's emotions, making it difficult to provide information that meets the user's needs. Furthermore, the summarization and filtering of the acquired information is insufficient, making it difficult for users to quickly obtain the information they need.

[1075] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1076] In this invention, the server includes scraping means for acquiring information from specified information sources, storage means for saving the acquired information, search means for performing a keyword search based on the saved information, filtering means for filtering appropriate articles based on the search results, display means for displaying a list of the filtered articles, reporting means for reporting the displayed articles, emotion analysis means for analyzing the user's emotions, and adjustment means for adjusting the report content based on the emotion analysis results. This makes it possible to acquire a wide range of information from multiple information sources and provide appropriate information according to the user's emotions.

[1077] A "scraping method" is a technique for automatically obtaining information from a specified source.

[1078] "Storage means" refers to technology for storing acquired information in a storage device such as a database.

[1079] "Search means" refers to technology for conducting keyword searches based on stored information.

[1080] "Filtering means" refers to a technique for selecting appropriate articles based on search results.

[1081] "Display means" refers to a technique for displaying a list of filtered articles.

[1082] The "reporting means" is a technique for reporting the displayed article to the user.

[1083] "Emotion analysis means" is a technique for analyzing the user's emotions.

[1084] "Adjustment means" refers to a technique for adjusting the report content based on the emotion analysis results.

[1085] MODE FOR CARRYING OUT THE INVENTION

[1086] The present invention is a system for acquiring information from a specified information source and adjusting the report content according to the user's emotions. Specific embodiments of this system will be described below.

[1087] Hardware and software used

[1088] The server uses the following hardware and software:

[1089] Hardware: A server machine with a powerful processor, sufficient memory, and ample storage.

[1090] Software: Python programming language, BeautifulSoup library, Scrapy library, MySQL database, IBM Watson sentiment analysis API

[1091] A terminal is a device through which a user inputs information and receives results. Examples include computers, smartphones, and tablets.

[1092] System processing flow

[1093] 1. Specify the URL of the news site

[1094] The user inputs the URLs of multiple news sites into the system.

[1095] Example: Specify the URLs "https: / / example-news1.com" and "https: / / example-news2.com".

[1096] 2. Scrape information from news sites

[1097] The server accesses the specified URL and parses the HTML content using Python's BeautifulSoup library.

[1098] The server extracts information such as the title, text, and publication date of the news article.

[1099] 3. Save the retrieved information in a database

[1100] The server stores the extracted news article information in a MySQL database.

[1101] The server connects to the database and stores the data using an INSERT statement.

[1102] 4. Input user emotions

[1103] Users input their emotions into the system using text or voice.

[1104] Example: Text "I'm so happy today."

[1105] 5. Analyze emotions

[1106] The server analyzes the input emotions using IBM Watson's emotion analysis API.

[1107] The server obtains the analysis result as either positive, negative, or neutral emotion.

[1108] 6. Select news articles based on sentiment

[1109] Based on the sentiment analysis results, the server preferentially selects positive news articles if the sentiment is positive.

[1110] The server will conservatively select negative news articles in the case of negative sentiment.

[1111] 7. Report selected news stories

[1112] The server reports the selected news articles to the user.

[1113] The user browses through the news articles provided by the system.

[1114] Specific examples

[1115] The user specifies the URLs "https: / / example-news1.com" and "https: / / example-news2.com".

[1116] The server uses BeautifulSoup to retrieve the latest news articles from these sites and store them in a MySQL database.

[1117] The user texts in "I'm so happy today."

[1118] The server uses IBM Watson to analyze the emotion "happy" as positive.

[1119] The server prioritizes positive news articles and reports them to the user.

[1120] Prompt Sentence Examples

[1121] Get the latest news articles from "https: / / example-news1.com" and "https: / / example-news2.com".

[1122] If someone types, "I'm so happy today," please prioritize reporting positive news stories.

[1123] The above is a specific embodiment for carrying out the present invention.

[1124] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1125] Step 1:

[1126] Specify the URL of the news site

[1127] The user inputs the URLs of multiple news sites into the system.

[1128] Input: URL of news site (e.g. "https: / / example-news1.com", "https: / / example-news2.com")

[1129] Output: List of specified URLs

[1130] What happens: A user enters a URL into a web form or app input field and presses the submit button.

[1131] Step 2:

[1132] Scraping information from news sites

[1133] The server accesses the specified URL and parses the HTML content using Python's BeautifulSoup library.

[1134] Input: A list of specified URLs

[1135] Output: News article title, text, publication date, and other information

[1136] Specific behavior: Use BeautifulSoup to extract specific tags (e.g.< / url:> < / h1> <h1>、 ) and obtain the required information.

[1137] Step 3:

[1138] Save the retrieved information in the database

[1139] The server stores the extracted news article information in a MySQL database.

[1140] Input: Information such as news article title, text, publication date, etc.

[1141] Output: News article records stored in a database

[1142] Specific operation: Connect to the database using MySQL's Python library (e.g. PyMySQL) and insert data using the INSERT statement.

[1143] Step 4:

[1144] Input user emotions

[1145] Users input their emotions into the system using text or voice.

[1146] Input: User's emotion (e.g., "I'm so happy today")

[1147] Output: Text data of the input emotion

[1148] Specific action: A user enters an emotion into an input field on a web form or in an app and presses the submit button.

[1149] Step 5:

[1150] Analyzing emotions

[1151] The server analyzes the input emotions using IBM Watson's emotion analysis API.

[1152] Input: Text data of user emotions

[1153] Output: Sentiment analysis results (positive, negative, neutral)

[1154] Specific operation: Sends text to IBM Watson's API and receives sentiment analysis results.

[1155] Step 6:

[1156] Selecting news articles based on sentiment

[1157] Based on the sentiment analysis results, the server preferentially selects positive news articles if the sentiment is positive.

[1158] Input: Sentiment analysis results, news article records stored in a database

[1159] Output: A list of selected news articles

[1160] What it does: Filter news articles from a database according to their sentiment.

[1161] Step 7:

[1162] Report selected news stories

[1163] The server reports the selected news articles to the user.

[1164] Input: A list of selected news articles

[1165] Output: News article reported to user

[1166] Specific behavior: Returns the selected news article to the user in JSON format and displays it on a web page or app.

[1167] The above are the specific processing steps of the program of this system.

[1168] (Application example 2)

[1169] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1170] Conventional news delivery systems do not customize news articles according to the user's emotions, and are unable to provide information appropriate to the user's emotional state. As a result, negative news may be displayed even when the user is expressing positive emotions, and conversely, positive news may be displayed even when the user is expressing negative emotions. This leads to a problem of reduced user satisfaction.

[1171] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1172] In this invention, the server includes scraping means for acquiring information from a specified information source, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate information based on the search results, display means for displaying a list of the filtered information, reporting means for reporting the displayed information, emotion analysis means for analyzing the user's emotions, and adjustment means for adjusting the report content based on the user's emotions, thereby enabling customization of news articles according to the user's emotional state.

[1173] A "scraping method" is a technique for automatically obtaining information from a specified source.

[1174] "Search means" refers to a technique for conducting a keyword search based on the acquired information.

[1175] "Filtering means" refers to a technique for selecting appropriate information based on search results.

[1176] The "display means" is a technique for displaying the filtered information in a list.

[1177] "Reporting means" refers to a technique for reporting displayed information to the user.

[1178] "Emotion analysis means" is a technique for analyzing the user's emotions.

[1179] The "adjustment means" is a technique for adjusting the report content based on the user's feelings.

[1180] A system for carrying out this invention comprises a server and a user terminal. The server includes scraping means for acquiring information from a specified information source, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate information based on the search results, display means for displaying a list of the filtered information, reporting means for reporting the displayed information, emotion analysis means for analyzing the user's emotion, and adjustment means for adjusting the report content based on the user's emotion.

[1181] Program processing explanation

[1182] Scraping Method

[1183] The server uses software libraries called requests and BeautifulSoup to retrieve information from multiple specified news sites, allowing it to aggregate the latest news articles from a wide range of sources.

[1184] Search methods

[1185] The retrieved news articles are then subjected to keyword search using natural language processing libraries such as TextBlob, allowing articles related to specific topics of interest to the user to be extracted.

[1186] Filtering Methods

[1187] Based on the search results, appropriate news articles are filtered based on criteria such as article content, credibility, and relevance.

[1188] Display means

[1189] The filtered news articles are displayed in a list on the user's device in a format that is intuitively easy for the user to understand.

[1190] Reporting method

[1191] The displayed news article is reported to the user, including a summary of the article and key points.

[1192] Emotion analysis means

[1193] To analyze the user's sentiment, we use sentiment analysis libraries such as TextBlob and NLTK, which determine whether the user is expressing positive or negative sentiment.

[1194] Adjustment means

[1195] The system adjusts the content of reports based on the user's emotions. For example, if the user is expressing positive emotions, it prioritizes positive news articles, and if the user is expressing negative emotions, it minimizes negative news articles.

[1196] Specific examples

[1197] For example, if a user expresses positive emotions, the server will prioritize positive news articles such as "new technology advancements" and "successful business stories." Conversely, if a user expresses negative emotions, the server will de-emphasize negative news articles such as "disasters" and "crimes."

[1198] Prompt Sentence Examples

[1199] If users are expressing positive sentiment, prioritize positive news articles, such as "new technology advancements" or "successful business stories."

[1200] In this way, news articles can be customized according to the user's emotional state, improving user satisfaction.

[1201] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1202] Step 1:

[1203] The server retrieves information from multiple specified news sites. Specifically, it uses the requests library to send HTTP requests to each news site and parses the HTML content using the BeautifulSoup library. The input is a list of news site URLs, and the output is a list of news articles retrieved from each news site.

[1204] Step 2:

[1205] The server performs a keyword search on the retrieved news articles. Specifically, it uses the TextBlob library to parse the text of each article and check whether it contains the specified keywords. The input is a list of news articles and the keywords, and the output is a list of news articles that match the keywords.

[1206] Step 3:

[1207] The server filters relevant news articles based on the search results, specifically based on criteria such as article content, credibility, and relevance. The input is a list of news articles that match the keywords, and the output is a filtered list of news articles.

[1208] Step 4:

[1209] The server displays a list of filtered news articles on the user's device. Specifically, it generates HTML and CSS to display the news articles in a format that is intuitively easy for the user to understand. The input is the list of filtered news articles, and the output is the HTML content displayed on the user's device.

[1210] Step 5:

[1211] The server reports the displayed news articles to the user, including summaries and key points of the articles. The input is a filtered list of news articles, and the output is the summaries of the news articles reported to the user.

[1212] Step 6:

[1213] The server analyzes the user's emotions. Specifically, it uses an emotion analysis library such as TextBlob or NLTK to determine the user's emotional state. The input is data indicating the user's emotions (e.g., user feedback or comments), and the output is the user's emotional state (positive or negative).

[1214] Step 7:

[1215] The server adjusts the reporting content based on the user's emotions: if the user is expressing positive emotions, it prioritizes positive news articles, and if the user is expressing negative emotions, it minimizes negative news articles. The input is the user's emotional state and a filtered list of news articles, and the output is a tailored list of news articles.

[1216] Example 3

[1217] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1218] Conventional news article display systems lack the functionality to adjust the display order of articles based on the user's emotions, which means they are unable to provide information that is appropriate to the user's emotions. Furthermore, they lack the functionality to summarize and report the contents of articles, making it difficult for users to obtain information efficiently.

[1219] The identification process by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a collection means for acquiring information from a specified information source, a search means for performing a keyword search based on the acquired information, a filtering means for filtering appropriate information based on the search results, a summarization means for summarizing the filtered information, an emotion collection means for collecting user emotion data, an order adjustment means for adjusting the display order of information based on the collected emotion data, a display means for displaying information based on the adjusted order, and a report means for reporting the displayed information. This makes it possible to provide information according to the user's emotions, allowing information to be obtained efficiently.

[1220] "Collection means" means the means for obtaining information from a designated source.

[1221] The "search means" is a means for performing a keyword search based on the acquired information.

[1222] The "filtering means" is a means for filtering appropriate information based on the search results.

[1223] The "summarization means" is a means for summarizing the filtered information.

[1224] The "emotion collection means" is a means for collecting user emotion data.

[1225] The "order adjustment means" is a means for adjusting the display order of information based on the collected emotion data.

[1226] A "display means" is a means for displaying information based on the adjusted order.

[1227] A "reporting means" is a means for reporting displayed information.

[1228] MODE FOR CARRYING OUT THE INVENTION

[1229] The present invention is a system for acquiring information from a specified information source and adjusting the display order of the information based on the user's emotions. Specific embodiments of this system will be described below.

[1230] 1. Generating the system program

[1231] The server generates a program including a collection means for acquiring information from a specified information source, a search means for performing a keyword search based on the acquired information, a filtering means for filtering appropriate information based on the search results, a summarization means for summarizing the filtered information, an emotion collection means for collecting user emotion data, an order adjustment means for adjusting the display order of the information based on the collected emotion data, a display means for displaying the information based on the adjusted order, and a report means for reporting the displayed information.

[1232] 2. Explanation of program processing

[1233] The server first collects information from specified sources, typically using web scraping libraries such as Python's BeautifulSoup or Scrapy, and stores the collected information in a database.

[1234] The server then performs a keyword search based on the collected information. The search method can be a search engine such as "Elasticsearch."

[1235] Based on the search results, the server filters out the relevant information, possibly using machine learning algorithms.

[1236] The filtered information is summarized by the server using natural language processing libraries such as NLTK and spaCy.

[1237] The device collects emotional data while the user is browsing information, detects emotions through user input and facial recognition cameras, and analyzes the data using emotion engines such as IBM Watson Tone Analyzer and Microsoft Azure Text Analytics.

[1238] The server adjusts the display order of information based on the emotion data sent from the device. If a positive emotion is detected, positive information is displayed at the top, and if a negative emotion is detected, negative information is displayed at the bottom. MySQL or PostgreSQL is used as the database management system (DBMS).

[1239] The terminal displays the information to the user in the display order sent from the server, allowing the user to view the information in an order that corresponds to their emotions.

[1240] 3. Examples of concrete examples and prompts

[1241] For example, if a user is using a news app, if the user feels happy, the system will display positive news such as "new technological developments" or "sports victories" at the top. On the other hand, if the user feels sad, negative news such as "disaster news" or "economic downturn" at the bottom.

[1242] Examples of prompts to be input to a generative AI model include:

[1243] Example prompt sentence:

[1244] Please implement a system that displays positive news articles higher when the user expresses positive sentiment, and negative news articles lower when the user expresses negative sentiment. Please also clearly state the specific names of the natural language processing technology and sentiment engine you use.

[1245] In this way, a system for adjusting the display order of information based on the user's feelings is realized. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1246] Program processing flow

[1247] Step 1: Gather information

[1248] The server collects information from specified sources using web scraping libraries such as Python's BeautifulSoup or Scrapy. The input is the URL of the specified source, and the output is the collected article data.

[1249] Specific behavior:

[1250] The server checks the RSS feed of the news site at a fixed time each day to see if there are any new articles. If a new article is found, it retrieves the article's URL and scrapes the article's content.

[1251] Step 2: Keyword search

[1252] The server performs keyword searches based on the collected information. A search engine such as Elasticsearch is used as the search method. The collected article data is given as input, and a list of articles that match the keywords is obtained as output.

[1253] Specific behavior:

[1254] The server indexes the collected article data in Elasticsearch and performs searches based on keywords specified by the user. The search results generate a list of articles that match the keywords.

[1255] Step 3: Filtering the information

[1256] The server filters out relevant information based on the search results. The filtering method uses a machine learning algorithm. The input is a list of articles from the search results, and the output is a filtered list of articles.

[1257] Specific behavior:

[1258] The server uses machine learning models to select reliable and relevant articles from the search results, generating a filtered list of articles.

[1259] Step 4: Summarize the information

[1260] The server summarizes the filtered information using natural language processing libraries such as NLTK and spaCy. The input is the filtered article list, and the output is the summarized article list.

[1261] Specific behavior:

[1262] The server analyzes the content of the filtered articles, extracts important parts, and generates summaries. The summarized article list is stored in a database.

[1263] Step 5: Collecting sentiment data

[1264] The device collects emotional data while the user is browsing information. Specifically, it detects emotions through user input and facial recognition cameras, and uses emotion engines such as IBM Watson Tone Analyzer and Microsoft Azure Text Analytics. The user's emotional data is given as input, and analyzed emotional data is obtained as output.

[1265] Specific behavior:

[1266] The device captures the user's facial expressions with a webcam, sends them to the emotion engine for analysis, and generates the user's emotion data as a result of the analysis and sends it to the server.

[1267] Step 6: Adjust the display order

[1268] The server adjusts the display order of information based on the emotion data sent from the terminal. As input, it receives emotion data and a summarized article list, and as output, it obtains the article list in the adjusted display order.

[1269] Specific behavior:

[1270] The server analyzes the sentiment data and places positive articles at the top if positive sentiment is detected, and negative articles at the bottom if negative sentiment is detected. An adjusted article display order is generated.

[1271] Step 7: Viewing information

[1272] The terminal displays the information to the user according to the display order sent from the server. As input, the adjusted display order article list is given, and as output, the articles displayed to the user are obtained.

[1273] Specific behavior:

[1274] The terminal displays the articles to the user based on the display order received from the server, allowing the user to view the articles in an order that suits their emotions.

[1275] In this way, a system is realized that adjusts the display order of information based on the user's emotions.

[1276] (Application example 3)

[1277] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1278] Conventional news distribution systems do not filter articles or adjust the display order according to the user's emotions, making it difficult to provide users with the information they want quickly and accurately. Furthermore, because the content of articles is lengthy, it takes time for users to understand all the information. This can lead to lower user satisfaction and a decrease in the frequency of use of news distribution services.

[1279] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1280] In this invention, the server includes scraping means for acquiring information from a specified information source, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate articles based on the search results, display means for displaying a list of the filtered articles, reporting means for reporting the displayed articles, emotion analysis means for analyzing the user's emotions, order adjustment means for adjusting the display order of articles based on the user's emotions, summarization means for summarizing the content of the articles, and reporting means for reporting the summarized articles. This enables filtering of articles and adjustment of the display order according to the user's emotions, thereby providing the information desired by the user quickly and accurately. Furthermore, summarizing articles allows the user to grasp the information they need in a short amount of time.

[1281] A "scraping method" is a technique for automatically obtaining information from a specified source.

[1282] "Search means" refers to a technique for conducting a keyword search based on the acquired information.

[1283] "Filtering means" refers to a technique for selecting appropriate articles based on search results.

[1284] "Display means" refers to a technique for displaying a list of filtered articles.

[1285] The "reporting means" is a technique for reporting the displayed article to the user.

[1286] "Emotion analysis means" is a technique for analyzing the user's emotions.

[1287] The "order adjustment means" is a technique for adjusting the display order of articles based on the user's feelings.

[1288] "Summarization means" refers to a technique for summarizing the content of an article.

[1289] A system for carrying out the present invention has the following configuration: A server includes scraping means for acquiring information from a specified information source, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate articles based on the search results, display means for displaying a list of the filtered articles, reporting means for reporting the displayed articles, emotion analysis means for analyzing user emotions, order adjustment means for adjusting the display order of articles based on the user emotions, summarization means for summarizing the content of the articles, and reporting means for reporting the summarized articles.

[1290] Hardware and software used

[1291] Hardware: Your smartphone's camera and microphone

[1292] software:

[1293] OpenCV: Camera image capture and face recognition

[1294] Transformers (Hugging Face): Sentiment Analysis and Article Summarization

[1295] TextBlob: Article Sentiment Analysis

[1296] Processing flow

[1297] 1. Scraping method: The server automatically retrieves news articles from the specified sources using web scraping technology.

[1298] 2. Search method: Based on the retrieved news articles, a keyword search is performed to identify articles that may be of interest to the user.

[1299] 3. Filtering: Based on the search results, appropriate articles are selected, so that only useful information is extracted for the user.

[1300] 4. Display method: The filtered articles are displayed in a list, allowing the user to select articles of interest from this list.

[1301] 5. Reporting: Reports the displayed article to the user, including the article title and summary.

[1302] 6. Emotion analysis: Using the smartphone camera and microphone, we analyze the user's facial expressions and tone of voice to identify their current emotions. We use OpenCV for facial recognition and Transformers for emotion analysis.

[1303] 7. Order adjustment method: The order of displaying articles is adjusted based on the user's emotions. If the user has positive emotions, positive articles are displayed at the top, and if the user has negative emotions, negative articles are displayed at the bottom.

[1304] 8. Summarization: Summarize the content of the article. Generate a summary of the article using Transformers.

[1305] 9. Reporting method: A summary of the article is reported to the user, allowing the user to grasp the necessary information in a short time.

[1306] Specific examples

[1307] For example, if a user expresses the emotion of "joy" through the smartphone camera, the system will display positive news articles at the top of the list. A summary of the article is also displayed at the same time, allowing users to quickly understand the content of the article.

[1308] Prompt Sentence Examples

[1309] Example prompts to input to a generative AI model:

[1310] If the user's emotion is "joy," then display and summarize positive news articles at the top.

[1311] Using this prompt, the generative AI model can filter and summarize articles according to the user's sentiment.

[1312] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1313] Step 1:

[1314] The server retrieves news articles from specified sources. It uses scraping techniques to collect article data from websites. The input is the URL of the source, and the output is the retrieved article data.

[1315] Step 2:

[1316] The server performs a keyword search based on the retrieved article data. It uses a search mechanism to identify articles that may be of interest to the user. The input is the article data and keywords, and the output is a list of articles that match the keywords.

[1317] Step 3:

[1318] The server filters relevant articles based on the search results. Using a filtering method, it extracts only information that is useful to the user. The input is a list of search results, and the output is a list of filtered articles.

[1319] Step 4:

[1320] The server displays a list of filtered articles, using a display means to allow the user to select articles of interest. The input is the filtered list of articles, and the output is the list of articles displayed to the user.

[1321] Step 5:

[1322] The server reports the displayed articles to the user. The reporting means provides the user with the article titles and summaries. The input is a list of displayed articles, and the output is the article information reported to the user.

[1323] Step 6:

[1324] The device analyzes the user's emotions using the smartphone's camera and microphone. Using emotion analysis means, it analyzes the user's facial expressions and tone of voice to identify their current emotions. The input is camera footage and audio data, and the output is the user's emotional information.

[1325] Step 7:

[1326] The server adjusts the display order of articles based on the user's emotions. Using an order adjustment means, if the emotion is positive, positive articles are displayed at the top, and if the emotion is negative, negative articles are displayed at the bottom. The input is the user's emotion information and the filtered article list, and the output is the adjusted display order of the articles.

[1327] Step 8:

[1328] The server summarizes the content of the articles. A summarization means is used to generate summaries of the articles. The input is a list of filtered articles, and the output is a list of summarized articles.

[1329] Step 9:

[1330] The server reports the summarized articles to the user. A reporting means is used to provide the summarized articles to the user. The input is a list of summarized articles, and the output is the summarized article information reported to the user.

[1331] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1332] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1333] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1334] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1335] [Third embodiment]

[1336] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1337] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1338] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1339] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1340] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1341] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1342] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1343] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1344] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1345] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1346] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1347] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1348] "Example 1"

[1349] In one embodiment of the present invention, a system is implemented that combines a scraping means for acquiring information from a specified news site, a search means for performing a keyword search based on the acquired information, a filtering means for filtering appropriate articles based on the search results, a display means for displaying a list of the filtered articles, and a reporting means for reporting the displayed articles. Specifically, the scraping means is implemented using Python libraries such as BeautifulSoup and Scrapy, and a keyword search is performed based on the acquired information. To filter appropriate articles based on the search results, natural language processing technology is used to analyze the content of the articles and evaluate their importance. The filtered articles are displayed in a list, and the displayed articles are reported. Reports are made via email, an internal SNS, or the like.

[1350] "Example 2"

[1351] In another embodiment of the present invention, a system is implemented in which scraping means can acquire information from multiple news sites. Specifically, the URLs of multiple news sites are specified and information is acquired from each. This makes it possible to acquire a wider range of information.

[1352] "Example 3"

[1353] In yet another embodiment of the present invention, a system is implemented in which the reporting means can summarize and report the contents of the filtered articles. Specifically, the content of the articles is summarized using natural language processing technology and the summary is reported. This allows the content of the report to be concise.

[1354] The processing flow of each embodiment will be described below.

[1355] "Example 1"

[1356] Step 1: Obtain information from the specified news site. In this step, scraping is performed using Python libraries such as BeautifulSoup and Scrapy.

[1357] Step 2: Perform a keyword search based on the acquired information. In this step, the text information of the acquired articles is analyzed and articles containing the specified keywords are extracted.

[1358] Step 3: Filter appropriate articles based on the search results. In this step, natural language processing techniques are used to analyze the content of the articles and evaluate their importance. Articles with an importance level above a certain threshold are selected.

[1359] Step 4: List the filtered articles. This step lists the titles and links of the selected articles.

[1360] Step 5: Report the displayed articles. In this step, you report the list of displayed articles via email, internal SNS, etc.

[1361] "Example 2"

[1362] Step 1: Obtain information from multiple news sites. In this step, you specify the URLs of multiple news sites and obtain information from each of them.

[1363] Step 2: Integrate the acquired information and perform a keyword search. In this step, information acquired from multiple sites is integrated and articles containing the specified keywords are extracted.

[1364] Step 3: Based on the search results, appropriate articles are filtered and displayed as a list. In this step, steps 3 and 4 of the first embodiment are referred to.

[1365] Step 4: Report the displayed article. In this step, refer to step 5 of embodiment 1.

[1366] "Example 3"

[1367] Step 1: Obtain information from a specified news site and perform a keyword search. In this step, refer to steps 1 and 2 in the first embodiment.

[1368] Step 2: Filter appropriate articles based on the search results. In this step, refer to step 3 in example 1.

[1369] Step 3: Display a list of filtered articles. In this step, refer to step 4 of the first embodiment.

[1370] Step 4: Summarize and report the content of the displayed article. In this step, natural language processing technology is used to summarize the content of the article, and the summary is reported via email or internal social networking sites.

[1371] Example 1

[1372] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1373] Conventional information gathering systems have had difficulty efficiently acquiring data from specific information sources and quickly providing information that users are interested in. Furthermore, the process of selecting appropriate information from the acquired information and reporting it to users takes time and effort, which reduces the usefulness of the information.

[1374] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a data acquisition means for acquiring data from a specified information source, a search means for performing a keyword search based on the acquired data, a filtering means for filtering appropriate information based on the search results, a display means for displaying a list of the filtered information, and a reporting means for reporting the displayed information. This makes it possible to quickly and efficiently acquire information in which the user is interested, and to select and report appropriate information.

[1375] "Data acquisition means" refers to a means for acquiring data from a specified information source.

[1376] The "search means" is a means for performing a keyword search based on the acquired data.

[1377] The "filtering means" is a means for selecting appropriate information based on the search results.

[1378] The "display means" is a means for displaying a list of filtered information.

[1379] The "reporting means" is a means for reporting the displayed information to the user.

[1380] The present invention is a system for acquiring data from a specified information source and quickly and efficiently providing information that a user is interested in. A specific embodiment of this system will be described below.

[1381] The server uses Python libraries such as BeautifulSoup and Scrapy to retrieve data from specified sources. Specifically, BeautifulSoup is used to parse data from HTML or XML, and Scrapy is used to crawl the entire website. For example, the server retrieves the latest news articles from "https: / / example-news-site.com."

[1382] Next, the server performs a keyword search based on the acquired data. If the user specifies the keyword "generative AI model," the server searches for articles containing this keyword among the acquired news articles. The search can be performed using the standard Python library or regular expressions.

[1383] The server then filters appropriate information based on the search results. It uses natural language processing (NLP) techniques to analyze the content of articles and evaluate their importance. Specifically, it uses Python libraries such as NLTK and spaCy to analyze the content of articles and calculate an importance score. For example, the server uses NLTK to tokenize and tag articles by part of speech, and spaCy to analyze the content of articles.

[1384] The filtered information is displayed as a list on the terminal. The user can check the list of filtered information on the terminal screen. The terminal receives the list of filtered information from the server and displays it in a list using HTML or GUI.

[1385] Finally, the server reports the displayed information. The report is sent via email or an internal social networking service (e.g., Slack or Microsoft Teams). Users can receive the reported information via email or social networking service. The server formats the list of filtered information into an email body or social networking service message, and sends the email using the SMTP protocol or posts the message to the social networking service using an API.

[1386] As a specific example, if a user specifies the keyword "generative AI model," the server performs the following processing steps.

[1387] 1. The server retrieves data from a specified source using BeautifulSoup or Scrapy.

[1388] 2. The server searches for articles containing the keyword "generative AI model" based on the acquired data.

[1389] 3. The server uses natural language processing technology to analyze the content of the articles based on the search results and evaluate their importance.

[1390] 4. The terminal displays a list of filtered articles.

[1391] 5. The server reports the displayed articles via email or internal SNS.

[1392] An example of a prompt sentence might be:

[1393] Get the latest news articles containing the keyword "generative AI model," rate them by importance, and list them. Report the rated articles by email.

[1394] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1395] Step 1: Data Acquisition

[1396] The server retrieves data from the specified sources. It receives the specified URL and list of sources as input and generates the retrieved HTML or XML data as output. Specifically, the server launches Scrapy and accesses the specified URL. Next, it uses BeautifulSoup to parse the HTML document and extract information such as the article title, body, and date.

[1397] Step 2: Keyword search

[1398] The server performs a keyword search based on the acquired data. As input, it receives a keyword specified by the user (e.g., "generative AI model") and generates a list of articles containing the keyword as output. Specifically, the server stores the acquired article data in list format and checks whether each article in the list contains the specified keyword.

[1399] Step 3: Filtering

[1400] The server filters appropriate information based on the search results. As input, it receives a list of articles obtained through keyword searches, and as output, it generates a filtered list of articles with their importance assessed. Specifically, the server uses NLTK to tokenize and tag articles with parts of speech, and spaCy to analyze the content of the articles and calculate an importance score. Articles with an importance score above a certain level are filtered and added to the list.

[1401] Step 4: List View

[1402] The terminal displays the filtered information in a list. As input, it receives the list of filtered articles from the server, and as output, it displays the information in a format that can be viewed by the user. Specifically, the terminal displays the received article list using HTML or a GUI.

[1403] Step 5: Report

[1404] The server reports the displayed information. It receives the list of filtered articles as input and generates a message to be reported by email or internal SNS as output. Specifically, the server formats the list of filtered articles into an email body or SNS message, and sends the email using the SMTP protocol or posts the message to the SNS using an API.

[1405] (Application example 1)

[1406] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1407] In modern society, there is a huge amount of information available on the Internet, making it difficult for users to efficiently collect and appropriately filter information of interest from that information. Furthermore, there is a lack of a means to quickly report filtered information, which makes it difficult for users to obtain the information they need in a timely manner. Furthermore, there is a lack of a function to summarize and report filtered information, making it difficult for users to quickly understand the information.

[1408] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1409] In this invention, the server includes scraping means for acquiring information from specified information sources, search means for performing keyword searches based on the acquired information, filtering means for filtering appropriate information based on the search results, display means for displaying a list of the filtered information, reporting means for reporting the displayed information, and reporting means for reporting the filtered information using electronic communication means. This enables users to efficiently collect information of interest, appropriately filter it, and quickly report it. In addition, by summarizing and reporting the filtered information, users can quickly understand the information.

[1410] A "scraping method" is a technique for automatically obtaining information from a specified source.

[1411] "Search means" refers to a technique for searching for information using specific keywords based on the acquired information.

[1412] "Filtering means" is a technology that selects appropriate information based on search results and removes unnecessary information.

[1413] "Display means" refers to a technique for visually presenting filtered information to the user.

[1414] "Reporting means" refers to a technique for notifying the user of the displayed information.

[1415] "Electronic communication means" refers to the technology of transmitting information using electronic means such as the Internet or email.

[1416] As an embodiment of the present invention, the following system is constructed: A server includes scraping means for acquiring information from a specified information source, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate information based on the search results, display means for displaying a list of the filtered information, reporting means for reporting the displayed information, and reporting means for reporting the filtered information using electronic communication means.

[1417] Hardware and software used

[1418] Hardware: Servers, user devices (smartphones, PCs, etc.)

[1419] Software: Python, BeautifulSoup, Scrapy, NLTK, Scikit-learn, smtplib

[1420] Data processing and calculation

[1421] 1. Scraping method: The server retrieves articles from specified sources using Python's BeautifulSoup or Scrapy. This allows you to automatically collect the necessary information from news sites on the Internet.

[1422] 2. Search method: The server searches the retrieved articles using keywords set by the user, allowing it to efficiently extract information that interests the user.

[1423] 3. Filtering method: The server analyzes the content of articles using NLTK and Scikit-learn, and evaluates their importance using TF-IDF vectorization and cosine similarity, which allows it to select relevant information and remove unnecessary information.

[1424] 4. Display: The server displays a list of the filtered information on the user's terminal, allowing the user to visually check the information of interest.

[1425] 5. Reporting: The server summarizes the filtered information and reports it to the user's email or social networking site using smtplib, allowing the user to quickly obtain important information.

[1426] Specific examples

[1427] For example, if you are developing a news filter application, you can input the following prompt sentences into your generative AI model:

[1428] Example prompt sentence:

[1429] I'm developing a news filter application. I want to implement a feature that scrapes articles from specified news sites, filters the articles based on keywords set by the user, selects and displays important articles, and reports them via email. Please generate code that meets the following requirements using Python's BeautifulSoup, NLTK, Scikit-learn, and smtplib.

[1430] News site URL: https: / / example-news-site.com

[1431] Keywords: ['AI', 'technology', 'innovation']

[1432] User email: user@example.com

[1433] In this way, it becomes possible for users to efficiently collect information of interest, filter it appropriately, and quickly report it. By summarizing and reporting the filtered information, users can quickly understand the information.

[1434] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1435] Step 1:

[1436] The server retrieves information from the specified source. Specifically, it uses Python's BeautifulSoup or Scrapy to scrape articles from news sites. It receives the URL of the news site as input and generates a list of retrieved articles as output.

[1437] Step 2:

[1438] The server searches the retrieved articles using keywords set by the user. It receives the list of retrieved articles and the keywords set by the user as input, and generates a list of articles that match the keywords as output. Specifically, it checks whether each article contains the keywords and extracts the matching articles.

[1439] Step 3:

[1440] The server filters appropriate information based on the search results. It receives a list of articles matching the keywords as input and generates a list of highly relevant articles as output. Specifically, it analyzes the content of articles using NLTK and Scikit-learn, and evaluates their importance using TF-IDF vectorization and cosine similarity.

[1441] Step 4:

[1442] The server displays the filtered information in a list on the user's device. It receives a list of highly important articles as input and generates a list of articles to be displayed on the user's device as output. Specifically, it formats the articles in HTML or JSON format and displays them in the user interface.

[1443] Step 5:

[1444] The server summarizes the filtered information and reports it to the user's email or social media. It receives a list of highly important articles as input and generates email or social media messages containing the summarized article content as output. Specifically, it sends emails using smtplib and posts messages using the SNS API.

[1445] Example 2

[1446] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1447] Conventional information collection systems often only acquire information from specific sources, resulting in a lack of comprehensiveness. Furthermore, the acquired information is often not analyzed, stored, or provided to users efficiently. Furthermore, the summary report of filtered information is not automated, placing a heavy burden on users.

[1448] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1449] In this invention, the server includes a data collection means for acquiring information from specified information sources, a search means for performing a keyword search based on the acquired information, a filtering means for filtering appropriate information based on the search results, a display means for displaying a list of the filtered information, a report means for reporting the displayed information, a means for receiving a list of URLs of the specified information sources, a means for sending an HTTP request to each URL, a means for acquiring HTML content, a means for analyzing the HTML content and extracting necessary information, a means for saving the extracted information in a database, and a means for providing the saved information to a user. This makes it possible to efficiently collect, analyze, and save a wide range of information from multiple information sources and provide it to a user.

[1450] "Data Collection Method" means a method for obtaining information from a designated source.

[1451] The "search means" is a means for performing a keyword search based on the acquired information.

[1452] The "filtering means" is a means for selecting appropriate information based on the search results.

[1453] The "display means" is a means for displaying a list of filtered information.

[1454] The "reporting means" is a means for reporting the displayed information to the user.

[1455] The "means for receiving a URL list" is a means for receiving a URL list of a specified information source.

[1456] "Means for sending HTTP requests" refers to means for sending HTTP requests to each URL.

[1457] The "means for acquiring HTML content" refers to a means for acquiring HTML content returned as a response to an HTTP request.

[1458] The "means for analyzing HTML content" refers to a means for analyzing acquired HTML content and extracting necessary information.

[1459] The "means for storing in a database" refers to a means for storing the extracted information in a database.

[1460] "Means for providing to the user" refers to means for providing the stored information in a form that can be accessed by the user.

[1461] This invention relates to a system for acquiring information from a specified information source, and analyzing, storing, and providing the information. A specific embodiment of this system will be described below.

[1462] The server uses a data collector to retrieve information from the specified sources. The data collector uses Python's requests library to send an HTTP request to each URL and retrieve the HTML content. The retrieved HTML content is then parsed using libraries such as BeautifulSoup or Scrapy to extract the required information.

[1463] The extracted information is stored in a database using an RDBMS such as MySQL or PostgreSQL. The stored information is provided to users through a web application built using Flask or Django. Users can access and view this information through their browser.

[1464] As a concrete example, suppose a user wants to obtain information from the following news site:

[1465] https: / / example-news1.com

[1466] https: / / example-news2.com

[1467] The server receives these URLs as a list and retrieves the article titles and body text from each site, storing this information in a database and allowing users to access it through a web application.

[1468] By inputting the following prompt sentence into the generative AI model, we can generate a description of a program that retrieves information from a news site.

[1469] Example prompt sentence:

[1470] "Describe a program that retrieves information from multiple news sites. Specifically, explain how to retrieve the HTML content of each site from a given list of URLs, extract the article titles and body text, and store them in a database. Include the names of any specific libraries or databases you use."

[1471] In this way, the server can efficiently collect, analyze, store, and provide a wide range of information from multiple sources to users.

[1472] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1473] Step 1:

[1474] The server receives a list of URLs for news sites specified by the user. The input is the list of URLs provided by the user, and the output is the list of URLs received by the server. This list is often provided in JSON format, CSV format, etc.

[1475] Step 2:

[1476] The server sends an HTTP request to each URL in the received URL list. The input is the URL list, and the output is the HTTP response obtained from each URL. This is done using Python's requests library.

[1477] Step 3:

[1478] The server retrieves the HTML content returned as a response to the HTTP request. The input is the HTTP response, and the output is the retrieved HTML content. The retrieved HTML content is analyzed in the next step.

[1479] Step 4:

[1480] The server uses libraries such as BeautifulSoup or Scrapy to parse the retrieved HTML content and extract the necessary information. The input is the HTML content, and the output is the extracted information (e.g., article title, body, publication date, etc.). Specifically, it parses HTML tags and extracts the necessary data.

[1481] Step 5:

[1482] The server saves the extracted information in a database. The input is the extracted information, and the output is the information saved in the database. The database uses an RDBMS such as MySQL or PostgreSQL. When saving, the data is inserted into a table with fields such as the title, body, and publication date of each article.

[1483] Step 6:

[1484] The server provides the stored information in a form that can be accessed by the user. The input is the information stored in the database and the output is the information that is presented to the user. For example, you can build a web application using Flask or Django to allow the user to view the information through a browser.

[1485] In this way, the server can efficiently collect, analyze, and store a wide range of information from multiple news sites and provide it to users.

[1486] (Application example 2)

[1487] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1488] Conventional news aggregator systems only retrieve information from specific news sites, which can lead to information bias. Furthermore, they lacked a way to efficiently store and later reference the retrieved information, making it difficult for users to quickly obtain the information they need.

[1489] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes scraping means for acquiring information from specified information sources, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate information based on the search results, display means for displaying a list of the filtered information, reporting means for reporting the displayed information, and storage means for saving the acquired information in a data format. This makes it possible to acquire a wide range of information from multiple information sources and efficiently store and reference it.

[1490] "Specified information source" refers to a website, database, or other information source that the user has pre-selected.

[1491] "Scraping method" refers to a technology or program used to automatically obtain data from a specified source.

[1492] "Keyword search" refers to the process of locating relevant information based on specific words or phrases within retrieved information.

[1493] "Search means" refers to the technology or program used to perform keyword searches.

[1494] "Filtering means" refers to technology or programs for selecting appropriate information from search results based on specific conditions.

[1495] "Display means" refers to the technology or program used to visually present filtered information to users.

[1496] "Reporting means" refers to the technology or program used to notify users of displayed information.

[1497] "Storage means" refers to the technology or programs used to store acquired information in data format so that it can be referenced later.

[1498] A system for implementing this invention includes scraping means for acquiring information from a specified information source, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate information based on the search results, display means for displaying a list of the filtered information, reporting means for reporting the displayed information, and storage means for saving the acquired information in a data format.

[1499] Program processing explanation

[1500] The server implements the scraping method using a Python program. Specifically, it uses the requests library to retrieve HTML content from the specified source URL, and then parses the HTML content using the BeautifulSoup library. Article titles and summaries are extracted from the parsed data and stored in a database.

[1501] The search function searches for information in the database based on keywords entered by the user. The search results are then filtered based on appropriate criteria, for example, to extract only the most recent information or information related to a specific category.

[1502] The display means visually presents the filtered information to the user's device, using a web browser or smartphone app. The reporting means notifies the user of the filtered information, and could be email or push notification.

[1503] The storage method saves the acquired information in a data format so that it can be referenced later. This can be done in JSON format or a database.

[1504] Specific examples

[1505] For example, if a user wants to get the latest news in the "Technology" category, they might use a prompt like this:

[1506] Prompt Sentence Examples

[1507] urls = [

[1508] 'https: / / example-tech-news.com'

[1509] ]

[1510] aggregator = NewsAggregator(urls)

[1511] news_data = aggregator.fetch_news()

[1512] aggregator.save_news(news_data)

[1513] By inputting this prompt into a generative AI model, the latest technology news can be retrieved from specified sources and provided to the user.

[1514] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1515] Step 1:

[1516] The server receives the URLs of the information sources specified by the user as input, specifically, it obtains a list of URLs specified by the user, which is then processed by the scraping means.

[1517] Step 2:

[1518] The server uses the requests library to get the HTML content from the specified URL. It takes the URL as input and gets the HTML content as output. This HTML content is parsed in the next step.

[1519] Step 3:

[1520] The server uses the BeautifulSoup library to parse the retrieved HTML content. It receives the HTML content as input and obtains the parsed data (article title and summary) as output. Specifically, it parses HTML tags and extracts the article title and summary.

[1521] Step 4:

[1522] The server saves the parsed data in a database. It receives the parsed data as input and gets the data saved in the database as output. It uses a storage means to store the data in JSON format or in a database.

[1523] Step 5:

[1524] A user performs a search by entering keywords. The search tool receives keywords as input and obtains search results as output. The search tool is used to search for information in a database based on keywords.

[1525] Step 6:

[1526] The server filters the search results. It receives the search results as input and obtains the filtered information as output. The filtering means is used to select information based on specific criteria (e.g., the most recent information or a specific category).

[1527] Step 7:

[1528] The server displays the filtered information on the user's device. It receives the filtered information as input and obtains visually displayed information as output. It displays the information on a web browser or smartphone app using a display means.

[1529] Step 8:

[1530] The server reports the filtered information to the user. It receives the filtered information as input and obtains the notified information as output. It notifies the user of the information by email, push notification, etc. using a reporting means.

[1531] Step 9:

[1532] The server stores the retrieved information in a data format. It receives the retrieved information as input and gets the stored data as output. It uses a storage method to store the information in a database or in JSON format for future reference.

[1533] Example 3

[1534] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1535] In today's information society, a huge amount of information is generated every day. It is extremely important to efficiently retrieve, summarize, and report the necessary information from that information. However, doing this manually takes time and effort, and is inefficient. In addition, existing systems do not integrate the processes of information collection, filtering, summarization, and reporting, which makes them difficult to use for users.

[1536] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1537] In this invention, the server includes a collection means for acquiring information from a specified information source, a search means for performing a keyword search based on the acquired information, a filtering means for filtering appropriate information based on the search results, a summarization means for summarizing the filtered information, and a reporting means for reporting the summarized information. This makes it possible to efficiently acquire necessary information from a vast amount of information and summarize and report it.

[1538] "Collection means" means the means for obtaining information from a designated source.

[1539] The "search means" is a means for performing a keyword search based on the acquired information.

[1540] The "filtering means" is a means for selecting appropriate information based on the search results.

[1541] A "summarization means" is a means for concisely summarizing filtered information.

[1542] A "reporting means" is a means for notifying a user of summarized information.

[1543] This invention is a system that acquires information from a server-specified information source, searches the acquired information by keyword, filters appropriate information, summarizes the filtered information, and reports the summarized information to a user. A specific embodiment of this system will be described below.

[1544] Hardware and software used

[1545] Hardware: Servers, terminals

[1546] Software: Natural language processing libraries (e.g., spaCy, NLTK), generative AI models (e.g., GPT-4)

[1547] Program processing

[1548] Retrieving articles

[1549] The server collects articles from the Internet. Specifically, it obtains the latest articles using the RSS feeds and APIs of news sites. For example, the server accesses "https: / / example.com / rss" and downloads the latest RSS feed. It then analyzes the obtained RSS feed and extracts the article title, URL, content, etc.

[1550] Article filtering

[1551] The server filters the collected articles based on specific keywords or categories. For example, it selects only articles in the "technology" category. The server analyzes the metadata (title, category, tags, etc.) of the articles it retrieves and keeps only articles that fall into the "technology" category in the list.

[1552] Article Summary

[1553] The server uses a natural language processing library (e.g., spaCy) to analyze the content of the filtered articles and generate summaries. Specifically, it extracts the main points of the articles and summarizes them into short sentences. The server inputs the filtered article text into spaCy, which extracts important sentences and key phrases. It then generates a summary based on the extracted information.

[1554] Summary Report

[1555] The server reports the generated summary to the user. Possible reporting methods include email, notification, dashboard, etc. The server sends the generated summary to the user's email address. For example, it sends an email using the SMTP protocol.

[1556] Specific examples

[1557] Retrieving articles

[1558] The server retrieves the latest news articles from "https: / / example.com / rss".

[1559] Article filtering

[1560] The server filters only articles in the "technology" category.

[1561] Article Summary

[1562] The server summarizes the article as follows:

[1563] Original article: "A new AI technology has been announced that promises to significantly speed up data analysis."

[1564] Summary: "New AI techniques speed up data analysis."

[1565] Summary Report

[1566] The server sends the summary to the user's email address.

[1567] Example prompts for generative AI models

[1568] Prompt statement

[1569] "Please summarize the following article: 'A new AI technology has been announced that promises to significantly improve the speed of data analysis.'"

[1570] In this way, a system is implemented in which the server performs a series of processes from collecting articles to summarizing and reporting. By receiving a concise summary, users can obtain information efficiently. The flow of the specific process in the third embodiment will be described with reference to FIG. 15.

[1571] Step 1: Get the article

[1572] The server collects articles from specified sources. As input, it receives the news site's RSS feed or API URL. Specifically, the server accesses "https: / / example.com / rss" and downloads the latest RSS feed. It then analyzes the obtained RSS feed and extracts the article title, URL, content, etc. As output, it generates a list of the extracted article information.

[1573] Step 2: Filtering articles

[1574] The server filters the collected articles based on specific keywords or categories. As input, it receives the list of article information obtained in step 1 and the filtering criteria (e.g., category "Technology"). Specifically, the server analyzes the article metadata (title, category, tags, etc.) and keeps in the list only those articles that match the filtering criteria. As output, it generates a list of filtered article information.

[1575] Step 3: Summarize the article

[1576] The server summarizes the content of the filtered articles. As input, it receives the list of article information filtered in step 2. Specifically, the server analyzes the text of the articles using a natural language processing library (e.g., spaCy) and extracts important sentences and key phrases. It then generates a summary based on the extracted information. As output, it generates a list of summarized article information.

[1577] Step 4: Report the summary

[1578] The server reports the generated summary to the user. As input, it receives the list of article information summarized in step 3 and the user's contact information (e.g., email address). As a specific operation, the server sends the summary to the user's email address using the SMTP protocol. As an output, it provides a confirmation of the summary sent to the user.

[1579] (Application example 3)

[1580] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1581] In modern society, a huge amount of information is generated every day, and users are required to quickly and efficiently obtain the information they need. However, conventional information acquisition systems have had the problem of being unable to properly filter and summarize information that interests users. In particular, there is a lack of means to quickly grasp long information such as news articles, which reduces the efficiency of users' information acquisition.

[1582] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1583] In this invention, the server includes an acquisition means for acquiring information from a specified information source, a search means for performing a keyword search based on the acquired information, a filtering means for filtering appropriate information based on the search results, a summarization means for summarizing and reporting the content of the filtered information, and a provision means for providing the summarized information to the user. This enables the user to quickly and efficiently acquire the information they need from a vast amount of information and grasp important content in a short amount of time.

[1584] "Acquisition means" refers to a means for acquiring information from a specified information source.

[1585] The "search means" is a means for performing a keyword search based on the acquired information.

[1586] The "filtering means" is a means for filtering appropriate information based on the search results.

[1587] The "display means" is a means for displaying a list of filtered information.

[1588] A "reporting means" is a means for reporting displayed information.

[1589] The "summarization means" is a means for summarizing and reporting the contents of the filtered information.

[1590] "Means of provision" refers to the means for providing summarized information to users.

[1591] A "generative AI model" is a model for generating information using artificial intelligence technology.

[1592] A "prompt sentence" is an instruction sentence input to a generative AI model.

[1593] As an embodiment of the present invention, a system is constructed that efficiently acquires news articles, summarizes them, and provides them to users. A specific embodiment of this system is described below.

[1594] System configuration

[1595] This system is composed of a server and a user terminal. The server includes an acquisition means for acquiring information from a specified information source, a search means for performing a keyword search based on the acquired information, a filtering means for filtering appropriate information based on the search results, a summarization means for summarizing and reporting the content of the filtered information, and a provision means for providing the summarized information to the user.

[1596] Hardware and software used

[1597] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[1598] Software: Python, Requests library, Hugging Face Transformers library

[1599] Data processing and calculation

[1600] 1. Acquisition method: The server acquires the latest news articles from the specified source using a news API. For example, it acquires article data in JSON format from the news API.

[1601] 2. Search method: The retrieved article data is searched using keywords specified by the user. The search results are narrowed down to articles with the highest relevance.

[1602] 3. Filtering methods: Filtering the search results to find more relevant articles, for example, based on criteria such as the article's credibility or publication date.

[1603] 4. Summarization: To summarize the content of the filtered articles, we use a generative AI model. Specifically, we use Hugging Face's Transformers library to invoke a pre-trained summarization model and generate a summary of the article.

[1604] 5. Delivery method: The summarized article is delivered to the user's device. Users can view the summarized article through their device, such as a smartphone or tablet.

[1605] Specific examples

[1606] For example, if a user searches for a news article using the keyword "economic growth," the server retrieves relevant articles from the news API and narrows down the articles related to "economic growth" using the search means. Next, it selects highly reliable articles using the filtering means and summarizes the content of the articles using the summarization means. Finally, the summarized articles are provided to the user's device.

[1607] Prompt Sentence Examples

[1608] "Please summarize the contents of the news article. Here is the article: 'Japan's economic growth rate exceeded expectations. The government has announced new economic policies and is hopeful about future growth.'"

[1609] In this way, users can efficiently obtain important information in a short time.

[1610] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1611] Step 1:

[1612] The server uses the news API to retrieve the latest news articles from the specified source. It uses the API URL and authentication key as input and gets the news article data in JSON format as output. Specifically, it sends an HTTP request using the Requests library and receives the article data as a response.

[1613] Step 2:

[1614] The server searches the retrieved article data using keywords specified by the user. The server uses the keywords specified by the user and the retrieved article data as input, and obtains a list of articles related to the keywords as output. Specifically, it analyzes the article data and extracts articles that contain the keywords.

[1615] Step 3:

[1616] The server then filters the search results to find more relevant articles. It uses the list of search results as input and obtains a filtered list of articles as output. Specifically, it filters based on criteria such as the article's credibility and publication date.

[1617] Step 4:

[1618] The server uses a generative AI model to summarize the content of the filtered articles. It uses the filtered article text as input and obtains summarized text as output. Specifically, it invokes a pre-trained summarization model using Hugging Face's Transformers library to generate article summaries.

[1619] Step 5:

[1620] The server provides the summarized article to the user terminal. It uses the summarized text as input and obtains the summarized article displayed on the user terminal as output. Specifically, it sends the summarized text to the user terminal for viewing by the user.

[1621] Step 6:

[1622] The user browses the summarized article through their device. The summarized article provided by the server is used as input, and the display of the summarized article is obtained as output. Specifically, the summarized article is displayed on the screen of the user's device, allowing the user to grasp important information in a short time.

[1623] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1624] "Example 1"

[1625] An example of a system using an emotion engine is one that provides an interface for recognizing a user's emotion. Specifically, an interface is provided that allows the user to input their own emotion into the system. This interface can take various forms, such as a text input field or a selection of icons that represent emotions. When the user inputs their emotion, the emotion engine analyzes the information and recognizes the user's emotion.

[1626] "Example 2"

[1627] One example of a system using an emotion engine is one that adjusts the content of reports based on the user's emotions. Specifically, if the user is expressing positive emotions such as joy or excitement, the system will prioritize reporting positive news articles to match those emotions. Conversely, if the user is expressing negative emotions such as sadness or anger, the system will report less negative news articles to match those emotions.

[1628] "Example 3"

[1629] As an example of a system using an emotion engine, the display order of filtered articles is adjusted based on the user's emotions. Specifically, if the user expresses positive emotions such as joy or excitement, the system will display positive news articles at the top of the list in line with that emotion. Conversely, if the user expresses negative emotions such as sadness or anger, the system will display negative news articles at the bottom in line with that emotion.

[1630] The processing flow of each embodiment will be described below.

[1631] "Example 1"

[1632] Step 1: The user accesses the system.

[1633] Step 2: The system provides the user with an emotion input interface, which can take various forms, such as a text input field or a selection of icons that represent emotions.

[1634] Step 3: The user inputs their emotions.

[1635] Step 4: The emotion engine analyzes the input from the user and recognizes the user's emotion.

[1636] "Example 2"

[1637] Step 1: The user accesses the system and inputs their emotions.

[1638] Step 2: The emotion engine analyzes the input from the user and recognizes the user's emotion.

[1639] Step 3: The system adjusts the reporting content based on the user's emotions. Specifically, if the user expresses positive emotions such as joy or excitement, the system will prioritize reporting positive news articles to match that emotion. Conversely, if the user expresses negative emotions such as sadness or anger, the system will moderate reporting negative news articles to match that emotion.

[1640] "Example 3"

[1641] Step 1: The user accesses the system and inputs their emotions.

[1642] Step 2: The emotion engine analyzes the input from the user and recognizes the user's emotion.

[1643] Step 3: The system adjusts the display order of the filtered articles based on the user's emotions. Specifically, if the user expresses positive emotions such as joy or excitement, the system will display positive news articles at the top to match that emotion. Conversely, if the user expresses negative emotions such as sadness or anger, the system will display negative news articles at the bottom to match that emotion.

[1644] Example 1

[1645] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1646] Conventional information collection systems only retrieve data from specific sources and perform keyword searches and filtering, which limits the comprehensiveness and accuracy of the information. Furthermore, there was a lack of efficient ways to report filtered information, making it difficult for users to quickly obtain the information they needed. This resulted in the time and effort required to retrieve and report information.

[1647] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1648] In this invention, the server includes a data acquisition means for acquiring data from a specified information source, a search means for performing a keyword search based on the acquired data, and a filtering means for filtering appropriate information based on the search results. By acquiring data from multiple information sources and performing a keyword search and filtering, the comprehensiveness and accuracy of information can be improved, and users can quickly obtain the information they need.

[1649] "Data acquisition means" refers to a means for acquiring data from a specified information source.

[1650] The "search means" is a means for performing a keyword search based on the acquired data.

[1651] The "filtering means" is a means for filtering appropriate information based on the search results.

[1652] The "display means" is a means for displaying a list of filtered information.

[1653] "Reporting means" means for reporting displayed information.

[1654] The present invention relates to a system that acquires data from a specified information source, performs keyword search and filtering, and displays and reports the filtered information in a list. Specific embodiments of this system will be described below.

[1655] The server retrieves data from the specified source using Python libraries such as BeautifulSoup or Scrapy. Specifically, the server parses the HTML structure of the source and extracts information such as the article title, body, and date. For example, the server accesses the homepage of "https: / / example-news.com" and retrieves the list of the latest articles.

[1656] Next, the server performs a keyword search based on the acquired data. If the user specifies the keyword "AI technology," the server searches for articles related to "AI technology" from among the acquired articles. Specifically, the server checks whether the keyword "AI technology" is included in the article title or text.

[1657] The server then filters out appropriate information based on the search results. It uses natural language processing (NLP) technology to analyze the content of the article and rate its importance. For example, if the content of an article is about the latest trends in AI technology, the server will rate the article highly important. Specifically, the server analyzes the frequency of keyword occurrences and context in the article to calculate a score.

[1658] The filtered information is displayed in a list on the device. The user can check the titles and summaries of the filtered articles on the device screen. Specifically, the device displays the titles, summaries, and links of the filtered articles in a list format.

[1659] Finally, the server reports the displayed information. This is done via email, an internal social networking site, or other means. For example, if an important article is found, the server sends an email to the user containing a link to the article and a summary. Specifically, the server notifies the user by sending an email using the SMTP protocol.

[1660] As a concrete example, if a user wants to know the latest news about "AI technology," they can input the following prompt sentence into the generative AI model:

[1661] Example prompt sentence:

[1662] "Get the latest news on AI technology, filter and report on the stories that matter."

[1663] After entering this prompt, the system will retrieve data from the specified source, perform keyword search and filtering, and then list and report the filtered information, allowing users to efficiently obtain the latest important news on "AI technology."

[1664] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1665] Step 1:

[1666] The server retrieves data from the specified information source. Specifically, the server uses Python libraries such as BeautifulSoup and Scrapy to parse the HTML structure of the information source and extract information such as the article title, body, and date. The URL of the information source is given as input, and the extracted article data is obtained as output.

[1667] Step 2:

[1668] The server performs a keyword search based on the acquired data. If the user specifies the keyword "AI technology," the server will search for articles related to "AI technology" from among the acquired articles. The input is the keyword specified by the user and the acquired article data, and the output is a list of articles that match the keywords. Specifically, the server checks whether the keyword "AI technology" is included in the article title or body of the article.

[1669] Step 3:

[1670] The server filters appropriate information based on the search results. It uses natural language processing (NLP) technology to analyze the content of articles and evaluate their importance. Keyword search results are given as input, and a list of articles with their importance evaluated is obtained as output. Specifically, the server analyzes the keyword frequency and context of the articles and calculates a score.

[1671] Step 4:

[1672] The terminal displays the filtered information in a list. The user can check the titles and summaries of the filtered articles on the terminal screen. The input is a list of filtered articles, and the output is the display of the articles in a format that the user can view. Specifically, the terminal displays the titles, summaries, and links of the filtered articles in a list format.

[1673] Step 5:

[1674] The server reports the displayed information. The report is sent via email, an internal SNS, etc. The input is a list of filtered articles, and the output is confirmation that the report has been sent. Specifically, the server notifies the user by sending an email using the SMTP protocol. For example, if an important article is found, the server sends the user an email containing a link to the article and a summary.

[1675] (Application example 1)

[1676] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1677] In today's information society, it is difficult for users to efficiently obtain information that is important to them from a vast amount of information. In addition, there is a problem that information is not provided in accordance with the user's emotions, making it difficult to increase user satisfaction.

[1678] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1679] In this invention, the server includes scraping means for acquiring information from a specified information source, search means for performing a keyword search based on the acquired information, filtering means for filtering appropriate information based on the search results, display means for displaying a list of the filtered information, report means for reporting the displayed information, emotion recognition means for recognizing the user's emotion, and priority display means for displaying information preferentially based on the emotion recognition means. This allows the user to efficiently acquire information that is important to them and further enables the provision of information according to their emotions.

[1680] A "scraping method" is a technique for automatically obtaining information from a specified source.

[1681] "Search means" refers to a technique for searching for information using specific keywords based on the acquired information.

[1682] "Filtering means" is a technique for selecting appropriate information based on search results.

[1683] The "display means" is a technique for visually presenting the filtered information to the user.

[1684] "Reporting means" refers to a technique for notifying the user of the displayed information.

[1685] "Emotion recognition means" is a technology that analyzes and recognizes the user's emotions.

[1686] The "priority display means" is a technology for displaying information preferentially based on emotion recognition means.

[1687] The system for carrying out the present invention has a function of acquiring information from a specified information source and displaying the information preferentially based on the user's emotions. A specific embodiment of this system will be described below.

[1688] Hardware and software used

[1689] Hardware: Smartphones, servers

[1690] Software: Python, BeautifulSoup, nltk, smtplib

[1691] System configuration

[1692] 1. Scraping Method

[1693] The server uses Python's BeautifulSoup library to automatically retrieve information from specified sources (e.g., news sites), which allows it to parse HTML structures and extract the required information.

[1694] 2. Search methods

[1695] Based on the information retrieved, the server searches for information using specific keywords, using the standard Python library to perform text searches.

[1696] 3. Filtering Methods

[1697] To select relevant information based on the search results, the server uses the SentimentIntensityAnalyzer from the nltk library, which analyzes the content of the information and evaluates its importance.

[1698] 4. Display means

[1699] The terminal (smartphone) visually presents the filtered information to the user using the smartphone's display.

[1700] 5. Reporting methods

[1701] The server uses the smtplib library to send emails to notify the user of the displayed information, allowing for a summary report of the filtered information.

[1702] 6. Emotion recognition means

[1703] The terminal provides a text input interface to analyze and recognize the user's emotions. When the user inputs their emotions, the server analyzes the information and recognizes the emotions.

[1704] 7. Priority display method

[1705] The server prioritizes displaying information based on the emotion recognition means, thereby enabling provision of information according to the user's emotion.

[1706] Specific examples

[1707] For example, if a user wants to get news about "technology," they might use a prompt like this:

[1708] Prompt Sentence Examples

[1709] Enter your news site URL: https: / / example.com / news

[1710] Enter your keyword: Technology

[1711] Enter the email address to report to: user@example.com

[1712] Please enter your current feelings (e.g. happy, sad, angry): Happy

[1713] Based on this prompt, the server retrieves information from the specified news site and searches for articles related to "technology." The server then filters the search results to display appropriate articles based on the user's sentiment. The filtered articles are then reported in a summary to the user's email address.

[1714] In this way, the user can efficiently obtain information that is important to him / her and can also receive information that corresponds to his / her emotions.

[1715] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1716] Step 1:

[1717] The server receives the URL of the specified news site as input, then uses Python's BeautifulSoup library to parse the HTML structure of the news site and extract the article information, which is then saved in text format.

[1718] Step 2:

[1719] The server receives the keywords entered by the user. It then performs a keyword search on the saved article information. It uses the standard Python library to search for articles containing the keywords in the article information and outputs a list of matching articles.

[1720] Step 3:

[1721] The server receives the list of articles obtained as search results as input. Then, it analyzes the content of each article and evaluates its importance using the SentimentIntensityAnalyzer in the nltk library. It filters out articles with high importance and outputs the filtered list of articles.

[1722] Step 4:

[1723] The device receives the filtered article list as input, then visually displays the filtered articles to the user using the smartphone's display, and outputs the displayed articles in a format that can be viewed by the user.

[1724] Step 5:

[1725] The server receives the filtered article list as input, then uses the smtplib library to summarize the filtered article content and send it to the user's email address, and the summarized article content is output in email format.

[1726] Step 6:

[1727] The terminal receives emotion information input by the user, then transmits the emotion information to the server, and outputs the emotion information in text format.

[1728] Step 7:

[1729] The server receives emotion information sent by the user as input, then analyzes the emotion information using emotion recognition means to recognize the user's emotion, and stores the recognized emotion information as internal data.

[1730] Step 8:

[1731] The server receives the recognized emotion information and the filtered article list as input. Then, the server re-evaluates the article list to prioritize articles according to the user's emotion based on the emotion recognition means. The re-evaluated article list is sorted based on the priority and transmitted to the terminal.

[1732] Example 2

[1733] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1734] Conventional news information acquisition systems have the problem of limiting the range of information they can obtain because they only obtain information from specific news sites. Furthermore, they do not adjust the content of reports based on the user's emotions, making it difficult to provide information that meets the user's needs. Furthermore, the summarization and filtering of the acquired information is insufficient, making it difficult for users to quickly obtain the information they need.

[1735] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1736] In this invention, the server includes scraping means for acquiring information from specified information sources, storage means for saving the acquired information, search means for performing a keyword search based on the saved information, filtering means for filtering appropriate articles based on the search results, display means for displaying a list of the filtered articles, reporting means for reporting the displayed articles, emotion analysis means for analyzing the user's emotions, and adjustment means for adjusting the report content based on the emotion analysis results. This makes it possible to acquire a wide range of information from multiple information sources and provide appropriate information according to the user's emotions.

[1737] A "scraping method" is a technique for automatically obtaining information from a specified source.

[1738] "Storage means" refers to technology for storing acquired information in a storage device such as a database.

[1739] "Search means" refers to technology for conducting keyword searches based on stored information.

[1740] "Filtering means" refers to a technique for selecting appropriate articles based on search results.

[1741] "Display means" refers to a technique for displaying a list of filtered articles.

[1742] The "reporting means" is a technique for reporting the displayed article to the user.

[1743] "Emotion analysis means" is a technique for analyzing the user's emotions.

[1744] "Adjustment means" refers to a technique for adjusting the report content based on the emotion analysis results.

[1745] MODE FOR CARRYING OUT THE INVENTION

[1746] The present invention is a system for acquiring information from a specified information source and adjusting the report content according to the user's emotions. Specific embodiments of this system will be described below.

[1747] Hardware and software used

[1748] The server uses the following hardware and software:

[1749] Hardware: A server machine with a powerful processor, sufficient memory, and ample storage.

[1750] Software: Python programming language, BeautifulSoup library, Scrapy library, MySQL database, IBM Watson sentiment analysis API

[1751] A terminal is a device through which a user inputs information and receives results. Examples include computers, smartphones, and tablets.

[1752] System processing flow

[1753] 1. Specify the URL of the news site

[1754] The user inputs the URLs of multiple news sites into the system.

[1755] Example: Specify the URLs "https: / / example-news1.com" and "https: / / example-news2.com".

[1756] 2. Scrape information from news sites

[1757] The server accesses the specified URL and parses the HTML content using Python's BeautifulSoup library.

[1758] The server extracts information such as the title, text, and publication date of the news article.

[1759] 3. Save the retrieved information in a database

[1760] The server stores the extracted news article information in a MySQL database.

[1761] The server connects to the database and stores the data using an INSERT statement.

[1762] 4. Input user emotions

[1763] Users input their emotions into the system using text or voice.

[1764] Example: Text "I'm so happy today."

[1765] 5. Analyze emotions

[1766] The server analyzes the input emotions using IBM Watson's emotion analysis API.

[1767] The server obtains the analysis result as either positive, negative, or neutral emotion.

[1768] 6. Select news articles based on sentiment

[1769] Based on the sentiment analysis results, the server preferentially selects positive news articles if the sentiment is positive.

[1770] The server will conservatively select negative news articles in the case of negative sentiment.

[1771] 7. Report selected news stories

[1772] The server reports the selected news articles to the user.

[1773] The user browses through the news articles provided by the system.

[1774] Specific examples

[1775] The user specifies the URLs "https: / / example-news1.com" and "https: / / example-news2.com".

[1776] The server uses BeautifulSoup to retrieve the latest news articles from these sites and store them in a MySQL database.

[1777] The user texts in "I'm so happy today."

[1778] The server uses IBM Watson to analyze the emotion "happy" as positive.

[1779] The server prioritizes positive news articles and reports them to the user.

[1780] Prompt Sentence Examples

[1781] Get the latest news articles from "https: / / example-news1.com" and "https: / / example-news2.com".

[1782] If someone types, "I'm so happy today," please prioritize reporting positive news stories.

[1783] The above is a specific embodiment for carrying out the present invention.

[1784] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1785] Step 1:

[1786] Specify the URL of the news site

[1787] The user inputs the URLs of multiple news sites into the system.

[1788] Input: URL of news site (e.g. "https: / / example-news1.com", "https: / / example-news2.com")

[1789] Output: List of specified URLs

[1790] What happens: A user enters a URL into a web form or app input field and presses the submit button.

[1791] Step 2:

[1792] Scraping information from news sites

[1793] The server accesses the specified URL and parses the HTML content using Python's BeautifulSoup library.

[1794] Input: A list of specified URLs

[1795] Output: News article title, text, publication date, and other information

[1796] Specific behavior: Use BeautifulSoup to extract specific tags (e.g.< / url:> < / h1> <h1>、 ) and obtain the required information.

[1797] Step 3:

[1798] Save the retrieved information in the database

[1799] The server stores the extracted news article information in a MySQL database.

[1800] Input: Information such as news article title, text, publication date, etc.

[1801] Output: News article records stored in a database

[1802] Specific operation: Connect to the database using MySQL's Python library (e.g. PyMySQL) and insert data using the INSERT statement.

[1803] Step 4:

[1804] Input user emotions

[1805] Users input their emotions into the system usi...

Claims

1. A data acquisition means for acquiring data from a specified source; A search means for performing a keyword search based on the data acquired by the data acquisition means; a filtering means for filtering appropriate information based on a search result by the searching means; a display means for displaying a list of information filtered by the filtering means; a reporting means for reporting the information displayed by the display means; An emotion recognition means for recognizing an emotion of a user; a priority display means for preferentially displaying information corresponding to the emotion of the user recognized by the emotion recognition means; Including, The reporting means is a system that reports the information displayed by the priority display means to the user.

2. 2. The system of claim 1, wherein said data acquisition means is capable of acquiring data from a plurality of sources.

3. 2. The system according to claim 1, wherein said reporting means is capable of summarizing and reporting the contents of the filtered information.

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